EVALUATION OF A PEDIATRIC ANTIMICROBIAL STEWARDSHIP

PROGRAM IN A TERTIARY CARE MEDICAL CENTER

By

Chou-Cheng Lai

A dissertation submitted to Johns Hopkins University in conformity with the requirements

for the degree of Doctor of Philosophy

Baltimore, Maryland

January, 2014

ii

ABSTRACT

Background:

The problem of antibiotic resistance is increasing globally. The inappropriate use of

antibiotics has been linked to the emergence of antibiotic resistance and other adverse

effects. Antimicrobial stewardship programs (ASPs) have been developed to improve

antibiotic use, with the goals of maintaining the effectiveness of current antimicrobials and

improving patient safety and outcomes. There are several methods by which the use of

antimicrobials can be intervened upon by ASPs; most fall into two basic categories:

restriction of antimicrobial before they are dispensed initially, often called “prior approval”

and review and feedback regarding antimicrobial use sometime after prescription, often

called “post-prescription review.” Relatively few studies evaluating either approach have

been conducted in pediatric settings. This study aims to assess if a prior-approval program

combined with post-prescription review program decreases antimicrobial use, reduces the

proportion of inappropriate antimicrobial course and is associated with a higher

compliance rate with following recommendations compared to a prior-approval program

alone among pediatric inpatients. Additionally, the study aims to determine the frequency

and risk factors of inaccurate requests submitted in a pediatric web-based prior-approval

program.

Methods:

We conducted a prospective, randomized controlled study at the Johns Hopkins Children

Center a 180 bed tertiary pediatric center from September 2011 to November 2012.

Patients in 4 general pediatric floors who were assessed by ASP team to be receiving

inappropriate antibiotics after being on therapy within 25-96 hours were randomized to

iii

either receive the intervention (a phone call with the recommendations by ASP team to the

treating physician) or no additional feedback. Patients who were cystic fibrosis patients, in

oncology-hematology, ICU and patients for whom ID consult had been obtained were

excluded from the study. Data collected included days of antibiotic therapy, the proportion

of inappropriate antimicrobial course, the acceptance rate of ASP recommendations and

some patient’s outcome ( such as inconsistence between the antimicrobial susceptibilities

of any recovered organism and the recommended alternative therapy, any subsequent

infection after ASP's recommendation of stopping therapy) at follow up between the two

groups. Wilcoxon rank-sum test was taken to compare measures of antibiotic use. Chi-

square test was used to compare the proportion of inappropriate antimicrobial course and

the acceptance rate of ASP recommendations.

In addition, a retrospective review of patients whose providers ordered antimicrobial using

the web-based prior-approval program was carried out from December 2011 to March

2012 for 4 months to determine the frequency of inaccurate information contained within

the requests. Multivariate logistic regression was performed to evaluate potential risk

factors of inaccurate information in the prior-approval program.

Results:

The pediatric ASP team identified 60 pediatric patients (30 patients in the intervention

group and 30 patients in the control group) for whom use of restricted antimicrobials was

inappropriate. There were no significant differences of the amount of restricted

antimicrobial use between the intervention group and the control group (median DOTs: 750

vs. 816.7, p=0.932; median duration of antimicrobial agent per episode of infection (days):

3.5 vs. 5, p=0.094). In the comparison of total antimicrobial use, differences were also not

iv

significant. However, the prevalence of inappropriate antimicrobial use at follow up was

significantly lower in the intervention group than the control group (34.4% vs. 75.8%,

p=0.001). The acceptance rate was significantly higher in the intervention group (the

treating physician accepted the recommendation) than in the control group (the treating

physician auto-corrected antibiotic use so that it was the same as what would have

recommended by the ASP team) (67.6% vs. 22.9%, p<0.001).

In the retrospective study reviewing prior-approval requests, the result showed that

inaccuracy (discrepancies between requests and medical records) occurred in 101 out of

1159 (8.7%) requests. Patients on the surgical service, in the ICU unit, not on oncology

service and with “prophylaxis” as an indication for receiving their antimicrobials were

significantly more likely to have inaccurate antimicrobial requests in multivariate logistic

regression analysis (p=0.011, p=0.043, p=0.036, p=0.044, respectively). Inaccurate

information in the prior-approval requests could potentially affect the decisions of the

pediatric ID fellow’s approval in about 45% (45 out of 101) of inaccurate requests.

Conclusions:

Our study demonstrates that a post-prescription review program can successfully decrease

the number of inappropriate antimicrobial courses at our institution. These findings might

encourage other pediatric centers to pursue similar post-prescription review programs.

Although inaccurate information occurred not very frequently among all pediatric prior

approval requests, nearly half of them could have influenced pediatric ID fellows’ decision-

making regarding approval of the antimicrobial. Targeted review of requests for specific

antimicrobials, or for specific patient populations is warranted.

v

COMMITTEE OF FINAL THESIS READERS

Kenrad Nelson, M.D.

Professor and Chair

Department of Epidemiology

Ruth Karron, M.D.

Professor and Dissertation Advisor

Department of International Health

Sara Cosgrove, M.D., M.S.

Associate Professor

Department of Medicine, School of Medicine

Lawrence H. Moulton, Ph.D.

Professor

Department of International Health

ALTERNATE THESIS READERS

William Moss, M.D., M.P.H.

Professor

Department of Epidemiology

Andrea Ruff, M.D.

Associate Professor

Department of International Health

Aaron Milstone, M.D., M.H.S.

Assistant Professor

Department of Pediatrics, School of Medicine

vi

ACKNOWLEDGEMENTS

First and foremost, I would like to express my deepest gratitude to my advisor, Dr. Ruth

Karron. I am really fortunate to have such a great advisor. Her continuous guidance,

encouragement, patience and immense knowledge always helped me through a lot of

obstacles from the beginning of exploring the research topic to every stage of writing the

thesis.

I cannot thank Dr. Sara Cosgrove enough. Dr. Cosgrove led the intervention project and

tutored me in antibiotic stewardship, providing numerous suggestions and answers to my

questions about the antimicrobial stewardship program. I would also like to thank Dr. Larry

Moulton for his insightful comments and criticisms at different stages of my research. I also

truly appreciate the efforts of Dr. Tamma and Dr. Jehn-Hsu, who despite large workloads of

their own, completed the post-prescription reviews, helped me collect the data and

provided lots of support during the whole process.

I am very grateful to my parents, my wife and daughter for their unlimited love, support,

and encouragement to pursue my academic career and my good friends Wei-Ju, Yea-Jen,

Hsin-Jen, Yi-Fang and Tsung for their continuous support.

It is a privilege and wonderful journey to be in the Johns Hopkins University. I will never

forget this experience and I could not have finished my thesis without the contributions of

so many people in my life.

vii

Table of Contents ABSTRACT ....................................................................................................................................... ii

ACKNOWLEDGEMENTS ................................................................................................................ vi

TABLES OF ABBREVIATIONS ........................................................................................................ x

LIST OF TABLES ............................................................................................................................ xi

LIST OF FIGURES ..........................................................................................................................xiii

1. Background ............................................................................................................................. 1

1.1. The Development of Antimicrobial Stewardship Programs (ASPs) ........................... 1

1.1.1. The Emergence of Antibiotic Resistance .............................................................. 1

1.1.2. The Significance of Antibiotic Resistance and Other Adverse Outcomes .......... 3

1.1.3. Antibiotic Use and Antibiotic Resistance .............................................................. 4

1.1.4. Adverse Effects of Inappropriate Antibiotic Use ................................................. 4

1.1.5. The Development of Antimicrobial Stewardship Programs ............................... 5

1.2. Overview of Antimicrobial Stewardship Programs ..................................................... 6

1.2.1. Active Strategies ..................................................................................................... 6

1.2.2. Supplemental Strategies ........................................................................................ 7

1.2.3. Current Status of Antimicrobial Stewardship Programs ..................................... 9

1.2.4. The Influence of Effective Antimicrobial Stewardship Programs .................... 11

1.3. Prior Approval Programs ............................................................................................. 13

1.3.1. Advantage of Prior Approval Programs ............................................................. 13

1.3.2. Limitations of Prior Approval Programs ............................................................ 14

1.4. Post-prescription Review Programs ........................................................................... 16

1.4.1. Advantages of Post-prescription Review Programs .......................................... 16

1.4.2. Limitations of Post-prescription Review Programs .......................................... 18

1.5. Pediatric Antimicrobial Stewardship Programs ........................................................ 19

1.5.1. Difference Between Pediatric Patients and Adult Patients ............................... 19

1.5.2. Antimicrobial Use in Children in the United States ........................................... 20

1.5.3. Review of Pediatric ASP studies .......................................................................... 20

1.5.4. Pediatric Prior Approval Programs in the Johns Hopkins Children’s Center .. 22

1.6. Rationale for this study ................................................................................................ 23

1.7. Hypothesis and Specific Aims ...................................................................................... 25

viii

1.7.1. Specific Aims ......................................................................................................... 25

2. Methods ................................................................................................................................. 27

2.1. Methods for Aim 1 and Aim 2 ...................................................................................... 27

2.1.1. Study Design ............................................................................................................. 27

2.1.2. Outcome Measures ............................................................................................... 33

2.1.3. Statistical Analysis ................................................................................................ 39

2.2. Methods for Research Aim 3 ....................................................................................... 41

2.2.1. Study Design ......................................................................................................... 41

2.2.2. Statistical Analysis ................................................................................................ 44

3. Results ................................................................................................................................... 46

3.1. Results for Aim 1 and 2 ................................................................................................ 46

3.1.1. Demographic Data ................................................................................................ 46

3.1.2. Comparison of antibiotic use (restricted and total) in the two study arms .... 49

3.1.3. Reasons for inappropriate antimicrobial use in two groups ............................ 56

3.1.4. Proportion of inappropriate antibiotic use on Days 2 and 3 after ASP team

review ………………………………………………………………………………………………………………………….58

3.1.5. Potential factors associated with inappropriate antimicrobial courses at Day 2

after the ASP team’s review ................................................................................................. 61

3.1.6. Rate of Compliance with the Recommendation at Day 2 and Day 3 after the

ASP team’s review ................................................................................................................ 61

3.1.7. Outcomes of patients when ASP team recommended alternative empiric

therapy or stopping therapy in two arms .......................................................................... 62

3.2. Results for aim 3 ........................................................................................................... 68

3.2.1. Demographic data ................................................................................................ 68

3.2.2. Types of inaccurate requests and examples ...................................................... 68

3.2.3. Potential Factors Related to Inaccuracy of Antimicrobial Requests ................ 72

3.2.4. Types of inaccurate requests and potential influences on the approvals of ID

fellows ………………………………………………………………………………………………………………………….77

4. Discussions and Recommendations .................................................................................... 80

4.1. Discussion ..................................................................................................................... 80

4.2. Strengths and Limitations of the Study ...................................................................... 86

4.3. Recommendations for Future Study ........................................................................... 88

ix

4.4. Conclusions ................................................................................................................... 89

5. References: ............................................................................................................................ 91

5.1. Appendix: ...................................................................................................................... 91

5.2. Bibliography: ................................................................................................................ 93

Curriculum Vita:……...…………………………………………………………………………………………..……… 115

x

TABLES OF ABBREVIATIONS Abbreviations Definition aOR Adjusted odds ratio ASP(s) Antimicrobial Stewardship Program(s) CA-MRSA Community-acquired methicillin-resistant Staphylococcus

aureus CDAD Clostridium difficile–associated disease CDC Centers for Disease Control and Prevention CF Cystic fibrosis CIs Confidence intervals CRE Carbapenem-resistant Enterobacteriacea CRP C-reactive protein CT Computed tomography DDD Daily defined dose DOT Day of therapy EIN Emerging Infections Network ESBL Extended-spectrum β-lactamase ESR Erythrocyte sedimentation rate GI Gastrointestinal hrs Hours ICU Intensive care unit ID Infectious disease IDSA Infectious Disease Society of America KPC Klebsiella pneumonia carbapenemase MALDI-TOF Matrix-assisted laser desorption ionization time-of-flight

mass spectrometry MRSA Methicillin-resistant Staphylococcus aureus NDM New Delhi metallo-beta-lactamase NICU Neonatal intensive care unit OR Odds ratio PDRAB Pan-drug-resistant Acinetobacter baumannii PICU Pediatric intensive care units PIDF Pediatric infectious disease fellows STRAMA Strategic Program for the Rational Use of Antimicrobial

Agents and Surveillance of Resistance UTIs Urinary tract infections WHO World Health Organization yr Year-old

xi

LIST OF TABLES

Table 2.1 Restricted antimicrobials in Johns Hopkins Children‘s Center ................................ 29

Table 3.1 Demographic data for the intervention group and the control group with patient

and antimicrobial course as the units of measure ..................................................................... 48

Table 3.2 Reasons of inappropriate antimicrobial use (in descending order) ....................... 57

Table 3.3 Comparisons of the proportions of inappropriate antimicrobial courses at Day 2

and Day 3 after post-prescription review between two groups (unit of analysis:

antimicrobial course) ................................................................................................................... 59

Table 3.4 Potential factors associated with inappropriate antimicrobial courses at Day 2

after the ASP team’s review (Unit of analysis: patient) ............................................................. 64

Table 3.5 Recommendations recorded in the data collection forms by the ASP team for two

groups............................................................................................................................................ 65

Table 3.6 Comparisons of changes noted at Day 2 after post-prescription review between

intervention and control groups (N_change/N_total (%)) ....................................................... 66

Table 3.7 Outcome of patients when ASP recommended alternative therapy or no therapy

....................................................................................................................................................... 67

Table 3.8 Basic demographic and clinical data in the pediatric prior-approval requests (unit

of analysis: antimicrobial request) ............................................................................................. 69

Table 3.9 The frequencies of different types of inaccuracies among the inaccurate requests

....................................................................................................................................................... 70

Table 3.10 Examples of different types of inaccuracies of prior-approval requests and the

potential influence upon PIDF approval ..................................................................................... 71

Table 3.11 Bivariate analysis of potential risk factors of inaccurate requests ....................... 74

Table 3.12 Multivariate analysis of risk factors for inaccurate requests ................................ 75

Table 3.13 Odds ratio (OR), adjusted odds ratio (aOR) and adjusted p value for risk factors

of inaccuracies aged ≤1 year and ˃1 year old ........................................................................... 76

Table 3.14 The potential influence of inaccuracies on approval by PIDF ............................... 78

Table 3.15 Types of inaccuracies in patients aged ≤1 year old and > 1 year old ................... 79

xii

Table 4.1 Estimated sample size in each group in ascending order if significant reductions

of antibiotic use are to be reached by using the results of this study: power 80%, alpha 0.05

and 2-sided test of significance ................................................................................................... 90

xiii

LIST OF FIGURES

Figure 2.1 Flow chart of post-prescription review for the intervention and control groups 32

Figure 3.1 Comparisons of restricted and total antibiotic use (DOTs), with and without

outliers .......................................................................................................................................... 52

Figure 3.2 Comparisons of inappropriate restricted antibiotic use (DOTs), with and without

outliers .......................................................................................................................................... 53

Figure 3.3 Comparison of median duration (days) of restricted antibiotics and combined

restricted antibiotic use (days) per episode of infections between two groups ..................... 54

Figure 3.4 Comparisons of median duration of restricted antibiotics (days) per patient per

episode of infection in different review timing in the intervention group .............................. 55

Figure 3.5 The influence of review timing after the antibiotic was initiated on the proportion

of inappropriate antimicrobial courses ...................................................................................... 60

Figure 5.1 Sample data collection form for Aim 1 and Aim 2 ................................................. 91

Figure 5.2 Sample data collection form for Aim 3.................................................................... 92

1

1. Background

1.1. The Development of Antimicrobial Stewardship Programs (ASPs)

Since the discovery of penicillin by Alexander Fleming in 1928 1, antibiotics have been

among the most widely prescribed drugs and have improved patient care greatly. However,

their effectiveness is being curtailed by the emergence of antibiotic-resistant bacteria.2 The

inappropriate use of antibiotics has been linked to the emergence of antibiotic resistance.3

Since the development of new antibiotic classes has slowed in the recent past and is not

anticipated to change in the near future4, 5, it is imperative to maintain the effectiveness of

current antibiotics. To respond to the threat of antimicrobial resistance, antibiotic stewardship

programs (ASPs) have been developed to promote the judicious use of antibiotics and to

prolong the effectiveness of currently available antibiotics.

1.1.1. The Emergence of Antibiotic Resistance

The prevalence of antimicrobial resistance is increasing globally. In the 1940s,

Staphylococcus aureus (S. aureus) became the first organism to develop resistance to

penicillin. S. aureus subsequently developed resistance to methicillin in 1960s, and to

vancomycin in the mid-1990s.2 Furthermore, antibiotic resistant S. aureus is not only

confined to hospitals. Community-acquired methicillin-resistant Staphylococcus aureus

(CA-MRSA) has become a major problem in the United States, causing skin and soft

tissue infections in otherwise healthy children and adults. Based on information from

The Surveillance Network Database-USA, an electronic repository of antimicrobial drug

susceptibility data, the incidence of CA-MRSA in the United States rose to 66.1% among

all MRSA isolates in 2007 with the majority isolated from children.6 Additionally, the

2

incidence of CA-MRSA increased more than 7-fold in outpatient settings (from 3.6% to

28.2%), between 1999 and 2006.7

The problem of antibiotic resistance is not confined to the U.S. For example,

about 40% of community acquired S.aureus infections in Algeria were CA-MRSA8. In

Taiwan, MRSA is endemic in most hospitals, accounting for 53–83% of all S. aureus

isolates in 12 major hospitals in 20009, and CA-MRSA infections have been reported

increasingly in Taiwanese pediatric patients since 2002 with an incidence rate >50% in

pediatric cases of community acquired S. aureus infections. 10 In Hong Kong, MRSA has

been reported to account for 30% to 40% of all S. aureus isolates in hospitals during the

1995-2005 surveillance period.11

In addition to infections caused by S. aureus, those caused by other gram

positive microorganisms such as enterococci are among the most common hospital

associated infections in recent years.12 In 2009-2010, Enterococcus species were the

second most common health care associated isolates in the United States, and the

majority of Enterococcus faecium isolates from central line-associated bloodstream

infection in the U.S. were resistant to vancomycin (82.6%).12 In Taiwan, the vancomycin

resistance in E. faecium also increased significantly from 0.3% in 2004 to 24.9% in 2010

(P <0.001). 13The optimal treatment for multidrug resistant enterococcal infection has

still not been identified.14

Similarly, multidrug-resistant Gram-negative microorganisms, including

Pseudomonas aeruginosa, Acinetobacter baumannii, and extended-spectrum β-lactamase

(ESBL)–producing or carbapenemase-producing Enterobacteriaceae, are increasingly

being reported worldwide.15 Some of these organisms are extremely difficult to treat

3

due to the development of resistance to most or all antibiotics, such as the pan-drug-

resistant Acinetobacter baumannii (PDRAB), carbapenem-resistant Enterobacteriacea

(CRE), including Klebsiella pneumonia carbapenemase( KPC) Enterobacteriacea and the

New Delhi metallo-beta-lactamase(NDM) Enterobacteriaceae which emerged in the past

decade.16,17 ,18

1.1.2. The Significance of Antibiotic Resistance and Other Adverse Outcomes

As antibiotic resistance emerges and increases, many previous effective

antibiotic therapies are losing their efficacy. As discussed below, the outcomes of this

trend are higher rates of mortality and morbidity, longer hospital stays, and greater

medical care expenditures. For example, in a study with careful matching, the costs for

care of hospitalized patients with healthcare- and community-associated infections

caused by antimicrobial-resistant organisms were estimated to be $ 15,626 and $25,573

greater than for those with infection due to antimicrobial-susceptible organisms. 19 The

differences were even larger when costs for these patients were compared with costs

for patients without infection. 20

A sensitivity analysis using a regression model to adjust for potential

confounding showed that the medical costs attributable to antimicrobial-resistant

infection in the U.S. ranged from $18,588 to $29,069 per patient.21 In addition,

antimicrobial-resistant infection prolonged hospital stays by 6.4-12.7 days, and

mortality attributable to this type of infection was 6.5%. The societal costs were

estimated at $10.7-$15.0 million. In addition, the cost of the gradual loss of efficacy of

certain antimicrobial classes, or the increased need for surgical or other procedures due

to these infections, is difficult to measure.20

4

1.1.3. Antibiotic Use and Antibiotic Resistance

Multiple factors may contribute to the development of antibiotic resistance, but

prior antibiotic use plays a key role in this process. Evidence of this relationship is

apparent from several studies. For example, studies in the U.S. have shown that the

prevalence of resistance for Enterobacter and Pseudomonas species increases in

parallel with increases in antimicrobial use.22,23 Recent exposure to antibiotics was the

only predictor that was consistently associated with carbapenem-resistant

Enterobacteriaceae and vancomycin-resistant enterococci. 24,25 Areas within hospitals

with higher rates of antibiotic resistance also tend to have higher rates of antibiotic use,

and increasing the duration of antibiotic treatment also increases the risk of

colonization with resistant organisms.3

1.1.4. Adverse Effects of Inappropriate Antibiotic Use

In addition to the development of antibiotic resistance, inappropriate

antibiotic use also contributes to other adverse effects. For example, Clostridium

difficile–associated disease (CDAD) is the leading cause of nosocomial diarrhea in

industrialized countries. Clostridium difficile infections can cause pseudomembranous

colitis and may even lead to toxic megacolon, which is life-threatening. In the U.S., the

incidence of hospital admissions complicated by CDAD nearly doubled between 2000

and 2003.26 CDAD was the leading cause of nosocomial infectious diarrhea in

hospitalized patients and represents a significant economic burden.27 The most

important risk factor for CDAD is the recent receipt of antibiotics.28 CDAD was found to

be associated with use of a variety of antibiotics, especially ampicillin, clindamycin,

5

fluoroquinolones and third-generation cephalosporins.28 Some studies have shown

that programs to restrict inappropriate antibiotic use have decreased the incidence of

CDAD.29,30,31

1.1.5. The Development of Antimicrobial Stewardship Programs

Numerous studies from around the world have shown that up to 50% of

antimicrobial use in humans is inappropriate and unnecessary,32,33,34 with redundant

antibiotic use reported in up to 71% of patients who received two or more

antibiotics.35 Since inappropriate use of antimicrobials creates selective pressure for

the development of antibiotic resistance, the best strategy to curb the spread of

antibiotic resistance is to use available antimicrobials more carefully and more

appropriately.

The World Health Organization (WHO) has defined optimal prescribing as “the

cost-effective use of antimicrobials which maximizes their clinical therapeutic effect,

while minimizing both drug-related toxicity and the development of antimicrobial

resistance.”36 In hospitals, the adoption of an antimicrobial stewardship program

(ASP) as a means to achieve optimal prescribing has been widely accepted in recent

years, and has also been recommended by the Infectious Disease Society of America,

the Society for Healthcare Epidemiology of America, the Centers for Disease Control

and Prevention and World Health Organization. 37,38 The primary goal of an ASP is to

“optimize clinical outcomes while minimizing the unintended consequences of

antimicrobial utilization such as the development of resistance and toxicity”.36 The

secondary goals “include reducing health care costs without adversely impacting

6

patient care”.37 Antimicrobial stewardship programs are central to the multifaceted

efforts to control the emergence and spread of antibiotic resistance.

1.2. Overview of Antimicrobial Stewardship Programs

Effective ASPs may include a number of active and supplemental strategies as described

below. There are advantages and disadvantages to the use of each of these strategies. Many

programs adopt hybrid strategies, and strict classification is not always possible.37,39 When

choosing a strategy or set of strategies to implement, it is important to consider the local

culture, attitudes and available resources.40

1.2.1. Active Strategies

1.2.1.1. Formulary Restriction and Prior Approval Requirement for Specific

Agents (Prior Approval Programs)

This strategy could lead to immediate and significant reduction of

antimicrobial use.37 The primary care team communicates with the ASP team and

requests specific antibiotics or advice. Restricted antibiotics are not released

without ASP team approval. A detailed description of prior approval programs is

provided in section 1.3.

1.2.1.2. Prospective Audit with Intervention and Feedback (Post-prescription

Review Programs)

Post-prescription review can ensure that antimicrobial treatment is optimal

in situations where additional microbiological and clinical data become available

within 48-72 hours after initiating antimicrobial treatment. This strategy can also

7

lead to a reduction in inappropriate antimicrobial use.37 A detailed description of

post-prescription review programs is provided in section 1.4.

1.2.2. Supplemental Strategies

1.2.2.1. Antibiotic Cycling

Antibiotic cycling utilizes the scheduled rotation of antimicrobials from

different classes in order to minimize the selective pressure exerted by individual

antibiotics.37 While many institutions no longer cycle antibiotics, several centers and

certain units within centers, such as the pediatric intensive care unit (PICU) in the

Johns Hopkins Children’s Center, were using this practice at the time of our analysis.

However, the compliance with cycling might be reduced because of concerns about

adverse effects and the belief among providers that there may be better options for

antibiotic use in individual patients. 41Additionally, the available evidence to date is

too weak to support cycling of antibiotics as a means of reducing antibiotic

resistance rates.37

1.2.2.2. Education

Education is also an important component of any ASP. Education could

include teaching sessions, provisions of written guidelines, online learning, etc. to

provide a foundation of knowledge that could improve future prescribing behaviors.

However, the success of education depends on the motivation of the clinicians.

Education alone is marginally effective in changing antimicrobial prescribing

practices and is difficult to sustain if not incorporated into programs using other

active strategies.37, 42

8

1.2.2.3. Clinical Pathway and Guidelines

Clinical pathways and guidelines can lead to improved antimicrobial use if

they incorporate local microbiologic resistance patterns to recommend selection of

appropriate antimicrobial agent and dosage and if buy-in is obtained from

participating clinicians.37 For example, in one facility, the ASP team cooperated with

general surgical leadership to develop hospital guidelines for management of

complicated intra-abdominal infections, based on the 2010 IDSA guidelines. This

study showed that the use of antibiotics was improved without significant change in

readmission rates, hospital length of stay or rates of CDAD43.

1.2.2.4. Computer-Based or Assisted Antimicrobial Stewardship Programs

Certain tools (such as computer-assisted programs), when used as part of a

comprehensive ASP, may also reduce antibiotic use, decrease antibiotic dosing

errors, and more readily identify drug-associated adverse events in a timely

fashion.37

Computer-assisted programs have been developed as a means of improving

antibiotic selection, dosing and duration. In addition, these programs can also more

easily measure antibiotic utilization, monitor adverse events and identify

nosocomial infections in a timely manner.37 One computerized physician order-

entry system utilized at Brigham and Women’s Hospital showed that prescribers

wrote significantly fewer orders for vancomycin when asked to key in a rationale for

vancomycin use from one of the categories provided.44 The authors concluded that

a “relatively soft educational intervention of displaying criteria for antimicrobial use

9

and adding a justification step to ordering antimicrobials can have a substantial

effect at controlling prescribing.” 45

Another study used the computer decision-support system to adjust the

dosing guidelines for pediatric populations to ensure that treatment

recommendations were appropriate. 46 The results showed that the system was

associated with a 59% decrease in the rate of pharmacy intervention for dosing

errors and a 28% decrease in the rates of excess antimicrobial dosing days.

Despite the potential advantages of a computer-based system, there are also

limitations. Computer-based systems might decrease the opportunity for ID fellows

to acquire specific clinical information, and could also decrease the opportunity for

instant communication and education. Computer-based systems must also be

flexible, user-friendly, and allow for reprogramming with ease when there are

updated guidelines or consensus statements. 47

1.2.2.5. Other Strategies

Several additional strategies that could be incorporated in the post-

prescription review program or prior-approval program include but are not limited

to: 1) dosage optimization using pharmacokinetic and pharmacodynamic principles

and 2) when appropriate, conversion from parenteral to therapy with an oral agent

with high bioavailability.40

1.2.3. Current Status of Antimicrobial Stewardship Programs

In the US, a survey done in 2009 revealed that among 522 responding physician

members of Emerging Infections Network (EIN) who cared for adult patients, 61%

10

reported that their hospitals had ASP in place48, compared to 45% of respondents in a

similar survey done in 1999.49 The percentage of institutions implementing ASP

increased during this 10 year period, although small community hospitals were still

least likely to have ASP programs. The strategies of ASP also shifted from primarily

formulary restrictions or prior-approval programs alone to combined sets of strategies

designed to provide feedback to the prescribers. In the 2009 survey, 67% of ASP

programs reported using post-prescription review as their primary strategy. 48

One limitation of the study described above is that EIN members might be more

likely to be interested in infection control and to respond to a survey. A study that may

be more representative of all providers was performed in California. In this study, all

general acute care hospital campuses were invited to participate in a survey, and the

participating hospitals were statistically representative of all the acute care hospitals in

the state. 50% of the participating hospitals had an ASP in place and 30% reported

planning an ASP. In hospitals that had an ASP in place, 26% had implemented post-

prescription review. In addition, 22% of the responding hospitals reported that

knowledge of the legislation which mandated that all general acute care hospitals

develop processes for evaluating the judicious use of antimicrobials had influenced

initiation of their ASP. 50 This is the only law of its kind in the U.S.

With respect to pediatric hospitals, a 2008 survey of 246 pediatric infectious

disease consultants who were members of EIN showed that only about 33% of

respondents reported having an ASP51, and 18% of respondents were planning a

program. Obviously, pediatric hospitals have lagged behind general hospitals in the

implementation of ASP.48 Of the respondents with pediatric ASPs, 78% reported using

11

prior approval programs, and 33% reported using the prospective audit and feedback

strategy.51

Because of global awareness of this threatening trend of increasing

antimicrobial resistance, some countries have launched national programs for

antimicrobial stewardship.52,53 In Sweden, the Strategic Program for the Rational Use of

Antimicrobial Agents and Surveillance of Resistance (STRAMA) antimicrobial

stewardship initiative reported a reduction of antibiotic use for outpatients and low

antibiotic resistance rate for most bacterial species over 10 years without measurable

negative consequences. 52ASPs also have been successfully implemented in certain

hospitals in Taiwan, Hong Kong, India and Australia in recent years. 54,55,56,57

1.2.4. The Influence of Effective Antimicrobial Stewardship Programs

There is substantial evidence to indicate that antimicrobial stewardship can

reduce medical costs and potentially reduce antibiotic resistance. However, many

physicians don’t focus on health care costs, and the factors involved in the

development of antibiotic resistance are complex and multi-factorial. Therefore, the

most important measures of ASP success for clinicians are related to improvement of

quality of care and patient health outcomes. 58

1.2.4.1. Improve patient outcomes

ASPs have been shown to shorten the duration of antimicrobial use,

decrease the re-admission rate and increase being discharged home without

antibiotics in patients with community-acquired pneumonia.59 ASPs have also been

shown to significantly decrease nosocomial infection by C. difficile and resistant

Enterobacteriaceae,29,30 increase cure rate and reduce failure rate.39

12

A critical feature of ASPs is that they should not compromise patient safety

by reducing the use of antibiotics when use is appropriate. A prevalence survey

conducted in the Netherlands showed that inappropriate lack of treatment was

uncommon (0.6%) when antibiotics were indicated. 60 A number of other studies

have demonstrated no increase in 30-day readmissions, nosocomial infections,

length of stay or mortality,59,61,62,63,64 even when ASP was implemented in critical

care patients.61

1.2.4.2. Decrease antibiotic resistance

Although it often takes years to demonstrate the benefits of less resistance

or reduced emergence of resistance, it is still important to note that “good

antimicrobial stewardship entails more than consideration of the immediate benefit

to the individual patient being treated. It also considers the long-term effects of use

on the future preservation of susceptibility in the practice population of the

prescriber.”65 However, a literature review from Tamma et al. found that there were

only a few studies reporting short-term reductions in antimicrobial resistance, and

even fewer for long-term reductions.41 A study of a prior-approval program showed

increased susceptibility to all beta-lactam and quinolone antibiotics after a 6 month

implementation period. The effect was especially obvious when isolates from

intensive care units were examined.64 Another study demonstrated that restricted

access to third generation cephalosporins significantly decreased the prevalence of

ESBL-Escherichia coli and Klebsiella species during a 5-year study period.66 Because

there are only a few studies addressing the long-term impact of ASPs on

antimicrobial resistance, additional well-designed studies are needed in the future.67

13

1.2.4.3. Reduce medical expenditures

Effective antimicrobial stewardship programs can reduce medical expenditures

which are of great interest to administrators.67 Comprehensive ASPs have been shown

to reduce the use of antibiotics and consequently decrease medical expenditures in

both larger academic hospitals and smaller community hospitals. The savings achieved

could be used to support ASPs, making them self-sustaining.37 For example, one study

showed that ASP decreased antibiotic expenditure by 46% during its 7-year presence,

but that expenditures increased by 32% (approximately $2 million) over a 2-year

period after the program was stopped.68 Another study showed that a combined prior-

approval and post-prescription review program could have sustained economic

benefits over 11 years with average cost savings of $920,070 to $2,064,441 per year. 69

1.3. Prior Approval Programs

The strategy of prior approval programs is to limit the use of some antimicrobials to

certain approved indications. Designated persons, either infectious disease fellows or attending

physicians, or infectious disease trained pharmacists, are assigned to implement the approval

process.

1.3.1. Advantage of Prior Approval Programs

There is already good evidence that prior approval programs can result in

immediate direct and significant reduction in antimicrobial use and cost.3,64,66,70,71 The

first reported study was conducted at Boston City Hospital required prescribers to

notify a member of the infectious disease unit before their choice of restricted antibiotic

could be dispensed from the pharmacy.70 This study showed significant decreases in the

14

use of certain antibiotics from the restricted list. Other studies showed that similar

programs could reduce bacterial resistance.64,66,72,73 In a study done at the University of

Kentucky, formulary restriction combined with a prior approval program reduced the

resistance rates of several important pathogens, including multidrug-resistant

Pseudomonas aeruginosa and MRSA.72 Hospitals with policies for restriction of

carbapenem use have both lower rates of carbapenem use and lower incidence rates of

carbapenem resistance in P. aeruginosa than those without these policies.73

1.3.2. Limitations of Prior Approval Programs

Potential challenges for effective prior approval programs exist. Generally, prior

approval programs only affect the initial choice of empiric therapy, and broad-spectrum

antibiotics are often approved as initial empiric therapy for critically ill patients which

could be inappropriate as later clinical information available. However, a multi-center

study showed that post-prescription review program could reduce antimicrobial use

significantly even in hospitals with highly restricted pre-prescription approvals.74 In

addition, prior approval programs generally do not consider the appropriateness of

non-restricted antimicrobials, which are the vast majority of antimicrobials used in the

hospital. The restriction of one antibiotic might result in increased use of another

antibiotic, a phenomenon which has been described as “squeezing the balloon”. 75 Prior

approval can also be labor intensive. Sufficient staff with expertise in antibiotic use

must be available to provide immediate, real-time service to avoid delay in the initiation

of empiric therapy.71

The perceived loss of autonomy by prescribers might influence their acceptance

of a prior approval program. One study showed that about 50% of housestaff felt that

15

being forced to request approval was frustrating and limited their autonomy. This

viewpoint was more common among senior residents than interns (48.8% vs. 8.8%,

P<0.005).76 In addition, members of the antimicrobial approval team might be anxious

to maintain good relationships with their colleagues, which might influence their

approval practices. Finally, the prescribers also might overstate the severity of patients’

conditions to gain approval for use of restricted antibiotics,77,78 or might try to escape

the approval period in order to prescribe their targeted antibiotics. 79 An example of this

type of “escape” was shown in a study performed at the Hospital of the University of

Pennsylvania78, where requests for restricted antimicrobials were approved by an

infectious disease fellow or infectious disease-trained pharmacist between 8:00 am and

10:00 pm each day. However, outside this time period, prescribers could order any

restricted antibiotics but the orders needed to be approved the next morning during

ASP active hours in order to be continued. The study found that restricted antibiotics

were ordered at a greater rate (restricted antibiotics/ total antibiotics during that

period) between 10pm and 10:59pm compared to other hours (57.0% vs. 49.9%;

p=.02). In addition, restricted antibiotics prescribed in the first hour after the approval

system ended were less likely to have the antibiotics continued compared with the last

hour during the approval system active hours. The type of prescribers (surgical or non-

surgical) and the patient’s location were not confounders or effect modifiers between

the relationship of ordering time and restricted antibiotics. While the reasons for these

prescribing patterns are not completely known, the authors of the study had several

hypotheses: prescribers might have been concerned that their requests would be

denied, might have been too busy to spend time or were reluctant to communicate on

the phone, or simply wanted to avoid difficult interactions.79

16

Another study conducted at the Hospital of the University of Pennsylvania

compared the information contained in documented telephone calls from prescribers to

the ASP team to information contained in patient’s medical records.78 This study found

that inaccurate information was communicated in over one third (39%) of all ASP calls,

and that the most common types of inaccuracies included: reports of current antibiotic

therapy (12.9% of all calls), microbiological data (11% of all calls), patient body

temperature (7.8% of all calls), allergies to medications (5.1% of all calls), and

radiological data (3.5% of all calls). ASP calls from surgical services contained more

inaccurate information than those from non-surgical services (48% vs. 34%). In a follow

up study, these inaccurate communications during prior-approval calls, especially

microbiological data, were found to be associated with inappropriate antimicrobial

recommendations made from the ASP team (odds ratio 2.2, p=0.03).80

1.4. Post-prescription Review Programs

Post-prescription review usually occurs within 48-72 hours after empiric antimicrobial

therapy is initiated: a member of the ASP team contacts the prescriber to optimize antimicrobial

use. Sometimes it can also be conducted earlier (within 24 hours) to replace the often

complicated on-demand system of the prior-approval program. Earlier review can assure

appropriate prescribing of empiric therapy. 41

1.4.1. Advantages of Post-prescription Review Programs

Post-prescription review allows for reevaluation of empiric therapy with broad-

spectrum antimicrobials in unstable patients when additional microbiological,

radiological and clinical information becomes available. The ASP team can then work

with prescribers to optimize antimicrobial use including streamlining therapy or

17

modifying therapy to match the targeted pathogen, and thereby reduce the possibility

that the approved restricted antibiotic is continued indefinitely or inappropriately used

while still allowing for aggressive empirical therapy.81 This is important in all

populations, and especially in pediatrics. For example, one study showed that prolonged

initial empiric antibiotic therapy was associated with increased risks of necrotizing

enterocolitis or death in extremely low birth weight infants after adjusting gestational

age, Apgar scores, race and other confounding factors.82

Post-prescription review programs have been shown to be effective in a number

of settings. A study done in adult patients in a tertiary teaching hospital observed that

the intervention rate (defined as the number of courses of therapy in which an

intervention was recommended divided by the total number of courses of therapy

reviewed) was superior for post-prescription review as compared with a prior approval

program (28%-34% vs. 5%).81 A randomized controlled trial in an internal medicine

setting shown that ASP involvement resulted in a 37% reduction in duration of

inappropriate antimicrobial use83, and was superior to provision of indication-based

guidelines84.

Clinical pharmacists may be especially effective in the implementation of post-

prescription review programs. Studies have shown that a post-prescription review

program that utilized clinical pharmacists resulted in a significant increase in de-

escalation of therapy (from 72% to 90%, with an acceptance rate of 91%)85 , and that

clinical pharmacists intervene more often than infectious disease (ID) fellows in adult

patients (29% vs.9%). 86

18

Additional evidence suggests that post-prescription review might improve

antimicrobial use in specific settings such as intensive care units,87 long-term care

facilities,88,89 community hospitals with more limited resources,30,90 ambulatory

setting.91 . For example, an intervention in 3 ICUs in a tertiary care center with post-

prescription review at the 3rd and 10th day of therapy showed that monthly broad-

spectrum antibiotic use, incidence of CDAD and resistance to meropenem were

decreased without change in ICU length of stay and mortality.87 In a medium-sized

community hospital, a post-prescription review program showed a reduction of 22% in

the use of parental broad-spectrum antimicrobials and a significant decrease in

nosocomial C. difficile infections and multidrug-resistant Enterobacteriaceae

infections.30 In hospitals with more limited resources where daily review of

antimicrobial use is not feasible, a relatively scaled-down post-prescription review, such

as only targeting patients receiving multiple, prolonged or high-cost antimicrobials and

limiting recommendations to well-defined clinical scenarios, can still have a significant

impact with an estimated 19% reduction in antimicrobial expenditures.90

1.4.2. Limitations of Post-prescription Review Programs

One of the limitations of this strategy is the potential for unnecessary antibiotic

exposure, cost and toxicity if used in the absence of prior approval or clinical

guidelines.37 In addition, the effectiveness may be reduced if the primary team does not

consistently follow suggestions.

Post-prescription review programs are also labor intensive and time consuming.

It is important for smaller hospitals or hospitals in developing countries to modify their

strategies according to their resources.92 For example, in a hospital with only one

19

infectious disease physician available for the ASP program, focusing ASP interventions

on the intensive care unit, where the majority of restricted antimicrobials are

prescribed, might be the best strategy.92

1.5. Pediatric Antimicrobial Stewardship Programs

1.5.1. Difference Between Pediatric Patients and Adult Patients

Most of the studies published have focused on adult inpatient populations.

However, it is difficult to extrapolate from the relative efficacy observed in these studies

to pediatric populations because there are some inherent differences between these

populations. First, fewer antibiotic treatment guidelines are available for children. 93

Treatment protocols used in adults might not be able to be replicated in children. One

meta-analysis showed an increased rate of treatment failure when urinary tract

infections (UTIs) in children were treated using the standard guidelines for treatment of

UTIs in adult women.94 Second, there are many fewer pharmacokinetic studies done in

young children. Third, many infections in young children are viral and would not

require antibiotics. For example, respiratory syncytial virus (RSV) is the most commonly

identified cause of lower respiratory tract infection in young children, and is the cause of

50 to 90 percent of hospitalizations for bronchiolitis, 5 to 40 percent of those for

pneumonia, and 10 to 30 percent of those for tracheobronchitis. 95 Therefore, the recent

published pediatric community-acquired pneumonia guideline states that

‘‘antimicrobial therapy is not routinely required for preschool-aged children with CAP,

because viral pathogens are responsible for the great majority of clinical disease.’’96

Fourth, dosing errors might occur more frequently in pediatric patients than adults

20

because most antimicrobials are administered by weight in children, whereas standard

doses are used in adults. 97

1.5.2. Antimicrobial Use in Children in the United States

The prescription rate of antimicrobials is extremely high in hospitalized and

outpatient children in the United States, although data are limited. Studies show that

about 60% of pediatric inpatients receive antimicrobials; the rates reported range from

38-72%.98 Carbapenems and linezolid use increased enormously from 2002 to 2007

(100% and 279%, respectively). 99 70.8% of children in pediatric intensive care units

(PICU) and 43.2% of children in neonatal intensive care units received antimicrobials;

the majority of treatment was empiric therapy.100

On an outpatient service, antibiotics were prescribed during 21% of pediatric

visits; 50% of these included broad-spectrum antibiotics. Approximately one quarter of

the visits in which antibiotics were prescribed was for respiratory conditions for which

antibiotics are not clearly indicated.101

1.5.3. Review of Pediatric ASP studies

Although the majority of ASP studies were performed in adult settings, a few

recent studies support the use of ASP in pediatric populations. In a study done at the

Children’s Hospital of Philadelphia which relied on a prior authorization and a post-

prescription review for re-approval of targeted antimicrobials102, 45% of requests for

restricted antibiotics required an intervention by the ASP. The most common

intervention made by the ASP was consultation with the prescribing clinicians, followed

by selection of an appropriate agent, or recommendations regarding the dose or

21

duration of treatment. The compliance rate was 89% with these interventions, and the

clinical outcome of the patients for whom alternative or no therapy was recommended

by ASP was acceptable. The high rate of interventions and the reasons for those

interventions suggest that pediatric ASPs are needed. One study evaluated the use of the

CDC 12-Step Campaign to Prevent Antimicrobial Resistance performed in four tertiary

care NICUs. This study found that 28% of antibiotic courses and 24% of all antibiotic

days were considered to be non-compliant with at least one CDC 12-Step element.103

Not targeting the pathogen was the most common violation, such as continued use of

vancomycin in methicillin susceptible S. aureus. In addition, inappropriate use was more

common during continuation of antibiotics than with initiation of therapy (39% versus

4%), which implied that antimicrobial stewardship focusing on post-prescription

review might have a greater effect than prior approval in NICU populations.

Another study revealed that inappropriate use of vancomycin and cefepime was

greater on the surgical service than the medical service and in the pediatric intensive

care unit as compared to the general ward. The most common inappropriate use was

failure to stop therapy or de-escalate therapy. 104

Post-prescription review combined with guidelines could decrease targeted

antibiotic use and could have other benefits for pediatric patients. An intervention in a

district hospital showed that use of revised antibiotic guidelines aimed to avoid broad-

spectrum antibiotics combined with post-prescription review for those prescribed

broad-spectrum antibiotics led to significant decreases in the use of fluoroquinolone

and cephalosporins and also to a decrease of the incidence of CDAD.105 Another study

based on post-prescription review and the distribution of antibiotics guidelines showed

that there was a 21% decline in targeted antimicrobial doses 3 years after the

22

intervention started. Antibiotic susceptibility to broad-spectrum antibiotics remained

high for most common gram-negative bacteria isolates in a 7-year follow-up.106

Finally, ASP could also be successful in pediatric ambulatory settings. One

recent study evaluated an intervention focusing on acute sinusitis, streptococcal

pharyngitis and pneumonia. This intervention, which combined clinical education (a 1

hour on site session) with personalized quarterly audit and feedback on prescribing,

demonstrated that off-guideline antibiotic use reduced from 15.7% to 4.2% in

pneumonia cases and 38.9% to 18.8% in acute sinusitis cases for 1 year after the

intervention.98

1.5.4. Pediatric Prior Approval Programs in the Johns Hopkins Children’s Center

In June 2005, Johns Hopkins Children’s Center implemented a web-based

restricted antimicrobial approval program that was developed by a team of pediatric

infectious disease physicians, pharmacists, and information systems experts.107

Physicians use the web-based tool to submit requests for restricted antibiotics for

approval. This web-based tool not only expedites the approval (and disapproval)

process, but reduces missed and unnecessary antibiotic doses. The program was shown

to improve users’ satisfaction (from 22% to 68% among prescribers), decrease the

number of doses of restricted antimicrobials dispensed (11%), decreased patient-days

of restricted antimicrobials (14%), improve multidisciplinary communication, and

significantly decrease antimicrobial cost expenditures from restricted antibiotics (22%)

without a change of expenditures on unrestricted antibiotics.107

23

1.6. Rationale for this study

The problem of antibiotic resistance is increasing globally. In Taiwan, for example, the

excessive use of antimicrobials is very prevalent, 108 and the rate of the antibiotic resistance is

one of the highest in the world.9,10,13,109 While a large number of studies of ASP have been

conducted in adults, there have been relatively few studies conducted in pediatric settings, and

it is important to add to the evidence base for support and promotion of pediatric ASP studies.

On a local level, it is also important to determine whether any improvements could be

made to the pediatric ASP at the Johns Hopkins Children’s Center, and specifically, whether

post-prescription review could be a useful addition to the highly successful web-based prior-

approval program.107 Studies in other settings have shown that post-prescription review

programs could reduce antimicrobial use significantly even with a highly restrictive prior-

approval program in place,74 and were more likely to detect an inappropriate antibiotic course

than prior approval programs.81,83 ,103 Prior approval programs and post-prescription review

programs are not mutually exclusive, but could be bundled to better shepherd precious

antimicrobial resources and improve antibiotic use.110 The introduction of a post-prescription

review program might enhance patient care in the Johns Hopkins Children’s Center.

Currently, the pediatric ASP at the Johns Hopkins Children’s Center does not have a

mechanism for assessing the accuracy of information provided by treating physicians. In the

Johns Hopkins Children’s Center, the pediatric ID team does not routinely check the accuracy of

the submitted requests because of the urgency of the need for approval and time constraints.

Previous studies in adult settings have shown that submission of inaccurate information may

lead to decreased program effectiveness78. For this reason, it is also useful to assess this

component of the pediatric ASP.

24

Most ASP studies used quasi-experimental designs to compare outcomes before and

after the intervention. Although the intervention might be the major contributor to the

outcome, it is not the only factor and it is often difficult to control for important confounding

variables, such as “maturation effects” associated with changes in patient condition, increase in

provider experience, implementation of new guidelines or initiatives during the study period,

or seasonal changes in diseases, etc.65 Unblinded ASP study members could also be biased in

assessment of study outcomes if they had prior knowledge that an intervention could affect the

appropriateness of the antibiotic use.111 For these reasons, we introduced an intervention with

randomized controlled study design which can reduce some bias mentioned above. The

specifics of the study design are described more completely in section 2.1.

25

1.7. Hypothesis and Specific Aims

1.7.1. Specific Aims

Aim 1 To determine the effectiveness of the prior-approval program alone and prior-

approval combined with post-prescription review in reducing total and restricted

antibiotic use and in reducing the proportion of inappropriate antimicrobials used.

We hypothesize that a prior-approval program combined with post-prescription review

program would:

a) decrease total and restricted antibiotic use per patient

b) reduce the proportion of inappropriate antimicrobials used compared to a

prior-approval program alone

Aim 2 To assess both compliance with ASP team recommendations as well as patient

outcomes with a prior approval program alone and with a prior approval program

combined with post-prescription review. We hypothesize that:

a) prior approval combined with post-prescription review would yield a higher

compliance rate with the recommendation of the ASP team than prior approval

alone

b) prior approval combined with post-prescription review program would have

similar outcomes for patients as prior approval program alone

26

Aim 3 To:

a) determine how often inaccurate information was submitted on the pediatric

prior approval request forms

b) identify risk factors for these inaccurate requests and assess the appropriateness

of antimicrobials approved

We hypothesize that inaccurate information would be submitted and that risk factors might

include clinical service (surgical or medical) and the type of antimicrobial requested. We

also hypothesize that some of these inaccuracies could affect the appropriateness of

approvals.

27

2. Methods

2.1. Methods for Aim 1 and Aim 2

2.1.1. Study Design

2.1.1.1. Study Population and Data Sources

Our study was undertaken in the Johns Hopkins Children’s, a 180-bed acute

care children’s hospital which is part of the Johns Hopkins Hospital located in

Baltimore, Maryland Center from September 2011 to November 2012. The existing

stewardship program consisted of a web-based prior approval program which was

implemented on June 1st, 2005, to replace the traditional telephone-based verbal

approval program. There was no post-prescription review component before this

study started.

Restricted antibiotics in Johns Hopkins Children‘s Center are listed in Table

2.1. Some of these antimicrobials are restricted throughout the Children‘s Center,

and some are restricted except for use by certain services. Using the web-based

system, prescribers provide the rationale (from an antibiotic-specific list or through

free text message) for restricted antibiotics with supporting data, if any.

Simultaneously, pediatric infectious disease fellows (PIDF) are paged by the system

automatically. PIDF then enter the approval decisions into the system and these are

automatically transmitted to the prescribers and to the pharmacy via pager.

Pediatric ID attending physicians are available for back-up if needed. The program

operates each day from 8 am to 10 pm. Outside of this time period, prescribers can

order restricted antibiotics without approval except for a few products (for

example, fosfomycin, daptomycin, and palivizumab) and any restricted drug was

approved automatically for one or two doses during the time period when PIDF was

28

not available. The PIDF would look at the list the next morning for overnight

approvals and could choose to approve for longer or to stop further approval. The

system is also programmed with certain specific drug-indication combinations for

auto-approval to save time and facilitate intervention in more complicated cases.

Use of restricted antibiotics was approved up to a specified ‘stop date‘, but

the current system allowed the pediatric pharmacy to dispense the restricted

antibiotics after the stop date in order to prevent lapses in dosing for critically ill

patients. Once a drug was ordered, the patient could still continue to receive the

drug until the treating physician decided to write a discontinuation order, even if

the PIDF did not choose to approve it for continued administration. However, re-

submission of requests for restricted antimicrobials before the approval stop date

was encouraged.

Our study intervention included inpatients from 4 hospital locations. We

excluded several groups of patients, including those in the ICU, hematology-

oncology and cystic fibrosis patients, and patients for whom ID consults had already

been obtained. We excluded these patients because they often presented with more

complex medical conditions and/or because they were treated using existing

antibiotic algorithms. Ethics approval for the study was obtained from the Johns

Hopkins Medicine Institutional Review Board.

29

Table 2.1 Restricted antimicrobials in Johns Hopkins Children‘s Center

30

2.1.1.2. Study Design

The medical records of the patients from four general pediatric floors who

had received restricted antimicrobials within 25-96 hours after initiation of therapy

were identified. After exclusion of the special populations described in Section

2.1.1.1, medical records were reviewed prospectively by a pediatric ID pharmacist

and a pediatric ID fellow in the ASP team with a pediatric ID attending physician

providing backup.

The ASP team reviewed each case, taking into account local resistance

patterns and then decided if the antibiotic use was appropriate. Decisions regarding

inappropriateness (Y/N), and reasons why the use was inappropriate, and

recommendations for change in antibiotic use were collected on a standardized data

collection form. Reviews were not performed on weekends or holidays. Using a

random number generated by a third party, all children who were considered to be

receiving inappropriate antibiotics were randomized to either receive the

intervention (intervention group) or not (control group)(Figure 2.1).

The intervention consisted of a phone call by pediatric ASP team to the

treating physician suggesting a change in the use of antimicrobials or other related

recommendations (e.g., obtain an ID consult); however, the final decisions regarding

antimicrobial choice were left to the primary treating physicians.

In the control group, although pediatric ASP team members wrote down the

recommendations in the data collection forms, they did not communicate the

recommendations to the treating physicians. The primary providers did not know

the contents of the recommendations during the study period.

31

Before the introduction of the intervention, one of the ASP team members

explained the study to all the pediatric staff to ensure that every primary provider

understood that they might receive recommendations to improve antimicrobial use

from the ASP team during the study period.

32

Figure 2.1 Flow chart of post-prescription review for the intervention and control groups

33

2.1.1.3. Data collection

A structured data collection form (Appendix, Figure 5.1) was used to collect

basic demographic and clinical information from the electric medical records for

each patient. Basic demographic data included age, gender, primary service (defined

as the clinical service prescribing orders and writing clinical notes on a patient),

review date, and admission date. Clinical information and other data included

underlying disease, name and duration of the antibiotic used, total length of stay

(hospital days), history of drug allergy, indication for restricted antibiotic therapy,

current type of infection, reasons that antibiotic use was considered inappropriate,

details of the ASP recommendation, documentation of the treating physician’s

“compliance” with the recommendation at Day 2 ( 25-48 hours) and Day 3 ( 49-72

hours ) after the ASP team’s review, and the safety outcome in cases for which

modifying therapy or stopping therapy was recommended.

For the indications of restricted antibiotics, the definitions of empiric,

directed therapy and prophylaxis were categorized as previously described 103 :

Empiric therapy: treating for symptoms or signs of infection

Directed therapy: treating a known pathogen

Prophylaxis: Preventing infections when patients were asymptomatic or

when the antibiotics were used for perioperative phase.

2.1.2. Outcome Measures

Operational definitions of quantitative and qualitative measures of

antimicrobial use are defined in detail below:

34

2.1.2.1. Quantitative Measures of Antibiotic Use

2.1.2.1.1. Days of Therapy

Antibiotic use was measured as days of therapy (DOTs) which was

abstracted from the pharmacologic database and was normalized to 1,000

patient-days. DOTs were calculated separately for each antibiotic. For

example, if a patient received 3 days of vancomycin and 3 days of gentamicin

during a 10-day admission, then the patient was considered to have received

300 DOTs/1,000 patient-days of vancomycin and 300 DOTs/1,000 patient-

days of gentamicin. For the outcome measure of our study, total DOTs for this

patient would be 600 DOTs/1000 patient-days, and restricted DOTs would be

300 DOTs/1000 patient-days. Any dose of a drug in a calendar day was

counted as “1 day” in quantifying antibiotic use.

2.1.2.1.2. Combined antimicrobial use (days) per patient per episode of

infection

Antibiotic use was measured as days during the specific episode of

infection that the restricted antibiotic was reviewed by the ASP team. It also

included the take-home antimicrobials listed in the discharge notes for the

continuation of antibiotic therapy for the specific episode of infection. The

start of the episode was counted from the start of any antibiotic for the

indicated episode of infection. Combined antibiotic use per episode of infection

was calculated separately for each antibiotic.

35

2.1.2.1.3. Median duration (days) of antimicrobial agents per patient per

episode of infection

The median duration (days) of antimicrobial agents per patient per

episode of infection was calculated from all total or restricted antimicrobials

used in the specific episode of infection that the ASP team reviewed.

2.1.2.1.4. Measures of Proportions of Antibiotic Courses that were

Inappropriate

The proportions of antibiotic courses that were inappropriate and the

rate of compliance with ASP team recommendations were determined on Day

2 or on Day 3 by examining the patient’s pharmacy or medical records to

determine whether: 1) the antimicrobial course was still inappropriate; 2) the

treating physician accepted the ASP team recommendation or, in the case of

the control arm, auto-corrected antibiotic use so that it was the same as what

would have been recommended by the ASP team.

A course of antimicrobial therapy was considered inappropriate if any

of the following criteria was met:

Inappropriate dosage was defined as errors in dosage, frequency and/or

formulations of antimicrobials based upon dose ranges suggested by the

Johns Hopkins Pediatric Infectious Diseases Service, taking into account

specific conditions such as renal or liver dysfunction, or bacterial meningitis.

Antimicrobial-microorganism mismatching (bug/drug mismatching)

was defined as use of a requested or current antimicrobial with suboptimal

activity against the microorganism according to the culture and/or

susceptibility reports.

36

Inappropriate antibiotic selection for documented infection included

susceptibility to a narrower-spectrum agent, such as use of vancomycin for

treatment of methicillin-susceptible S. aureus in a patient without beta-

lactam allergy. The definition of inappropriate selection also included

inappropriate route of administration, such as use of intravenous therapy if

the oral form of therapy was considered acceptable.

Inappropriate spectrum of coverage included therapeutic duplication

(double coverage) which is prescription of two or more antimicrobials with

the same antimicrobial activity which is unnecessary (such as

piperacillin/tazobactam and metronidazole for treatment of anaerobic

infections).

No evidence of infection or the infection was a viral infection for which

antibiotics were unnecessary were defined as absence of clinical,

laboratory or radiographic evidence of infection or the presence of

documented or suspected viral infection.

Contraindication based on patient‘s drug allergy history was defined as

the prescription of an antimicrobial to patients with known allergies to a

particular antimicrobial or antimicrobial class.

Prolonged duration of therapy was defined as unnecessarily prolonged

therapy for the indicated infection based upon standards established by the

Pediatric Infectious Diseases Service (for example, 10 days treatment of

acute tracheitis instead of 5 days of therapy; more than 24 hours use of

antibiotics after a surgical procedure for perioperative surgical prophylaxis).

37

Not requesting an ID consult if ID consultation was considered necessary

for the appropriate selection of antibiotics

2.1.2.2. Measures of Compliance with the Recommendation

Following the assessment of use of restricted antimicrobials, the ASP team

recorded all recommendations in the structured data collection form. Potential

recommendations included:

1. Stopping antibiotics (elimination of duplicate therapy or

unnecessary therapy)

2. Modifying therapy (adding an antibiotic, prescribing an antibiotic

with a narrower or broadened spectrum, adjusting antibiotic dose

or duration, changing the route of administration, or

recommending an alternative therapy because of patient allergy)

3. ID consult (when the antibiotic choice was complex due to the

complicated nature of the patient’s condition and ID consultation

was considered necessary)

4. Other recommendations: assessing drug levels, monitoring

laboratory parameters (for example, CRP, ESR, etc.),

recommending sterile site cultures, removal of an infected source

(for example, the drainage of an abscess, the removal of a

potentially infected device, etc.).

For each patient, more than one type of recommendation could be

recorded; in this instance, all recommendations were captured on

the data collection form.

38

The compliance rate was defined as the proportion of all changes made by

the treating physicians in compliance with ASP recommendations (treating

providers made changes after the communication with the ASP team in the

intervention group or the providers made changes on their own without

communication with the ASP team) divided by all recommendations recorded in the

data collection form by ASP team.

2.1.2.3. Measures of patient outcomes

There were two safety outcomes related to ASP recommendations that we

evaluated:

2.1.2.3.1. ASP team recommendation at variance with culture results

For cases in which alternative therapy (broadened or narrowed

empirically) was recommended, we determined whether any of recommended

therapies were inconsistent with the antimicrobial susceptibilities of any

recovered organisms.

2.1.2.3.2. Developed subsequent infection

For cases recommended for stopping therapy, we determined whether

the patients developed laboratory-confirmed infections or any infections

defined by clinicians and recorded in the medical records within 48 hours

following the ASP recommendation.

39

2.1.3. Statistical Analysis

The analysis was conducted using STATA software version 11 (StataCorp, LP

Texas) as described below:

2.1.3.1. Analysis for Reducing Antibiotic Use in Intervention Group

Frequency counts and percentages were calculated to describe basic

demographic and clinical information in the intervention group and the control

group. Student’s t-test for continuous variables and Pearson’s chi-square test for

categorical variables were undertaken to determine whether there were significant

demographic or clinical differences between the two groups. If the categorical

variable had a cell with fewer than 5 cases, Fisher’s exact test was performed.

For the comparison of antimicrobial use between the intervention group and

control group, the Shapiro-Wilk test was done to determine whether the

antimicrobial use data (continuous variables) were normally distributed. If the

antimicrobial use data were normally distributed, Student’s t-test was used to

compare antimicrobial use. If the antimicrobial use data were not normally

distributed, Wilcoxon rank-sum test was undertaken with calculation of medians.

Several comparisons between the intervention and control arms were made

using a t-test or Wilcoxon rank-sum test depending on the results of Shapiro-Wilk

test for the analysis of antimicrobial use in our study, including :

Restricted and total antibiotic use( DOTs)

Inappropriate restricted and total antibiotic use (DOTs)

Combined antimicrobial use (days) per patient per episode of infection

40

Median duration (days) of antimicrobial agents per patient per episode of

infection

Cases with extended lengths of stay would potentially influence the

calculations of the denominators of DOTs. Because the ASP team did not

continuously review antibiotic use in each patient, cases with extended lengths of

stay could have extremely low DOTs compared to the cases without extended

admissions. Therefore, we determined whether there were high leverage points

(defined as the point which was higher than the 3rd quartile plus 1.5 interquartile

ranges) of length of stay and then re-evaluated the comparisons of the days of

antimicrobial use in both arms by dropping these cases.

Lastly, in order to determine whether an earlier review by the ASP team

would have a greater impact on reduction of antimicrobial use than later review,

subgroup analysis was also undertaken by t-test or Wilcoxon rank sum test to see if

there was difference of “median duration of restricted antimicrobial agents per

episode of infection” by two different review window (Day 2 and Day 3-4 after the

restricted antibiotic was initiated).

2.1.3.2. Analysis for Reducing the Proportion of Inappropriate Antibiotic use

Chi-square test was used for the comparisons of the proportion of

inappropriate antibiotic courses, compliance with the recommendations and for the

exploration of the potential factors for inappropriate antibiotics still in use at the

Day 2 follow up after the ASP team’s assessment.

The prevalence ratio (the prevalence of inappropriate antimicrobial course

still in use in the intervention group divided by the prevalence in the control group)

41

was calculated with point estimates and 95% confidence interval reported. Similar

to section 2.1.3.1, subgroup analysis for different review timing by chi-square test

was undertaken for the comparison of inappropriate antibiotic course at Day 2 after

the ASP team’s assessment.

2.1.3.3. Analysis of outcomes when ASP team recommended alternative therapy

or no therapy in two arms

A frequency count was used to describe the safety outcomes when ASP team

recommended alternative therapy or no therapy in two arms.

2.2. Methods for Research Aim 3

2.2.1. Study Design

As described above, the clinical setting of this study was the Johns Hopkins

Children’s Center. The study was a retrospective comparison between the medical

records and the prior-approval requests and focused on the accuracy of age, diagnosis,

present illness, co-morbid conditions or treatment at the time of requests, laboratory

and radiological exams, physical examination findings within the 24 hours preceding

the request and current antimicrobial treatment. The study included all documented

requests for patients in the Children’s Center to the pediatric web-based prior-approval

system for 4 months, from December 2011 to March 2012. More than one request could

be included for a single patient during a given hospitalization. Duplicate requests for a

specific antimicrobial were excluded from the study.

The patient’s medical records, including admission notes, progress notes,

consultation notes, laboratory test results, radiological reports, medication

administration and patient’s history of allergies were used as the source of the data and

42

served as the gold standard for comparison with the documented pediatric web-based

prior-approval requests.

A standardized data collection form was used to abstract data for each

antimicrobial request and the corresponding medical record for each patient (Appendix,

Figure 5.2). The form included the patient‘s age, physical examination results (body

temperature and blood pressure within the 24 hours before the request, and other

pertinent physical findings), underlying diseases and pertinent treatment (for example,

immunosuppressive treatment or presence of a central venous catheter, current

antimicrobial treatment), history of allergies, clinical laboratory test results (white

blood cell and differential counts, CRP or ESR, microbiological results), radiographic test

results, and the patient‘s location and the level and subspecialty of the physician

requesting the antimicrobials. Potential risk factors, such as the patient‘s age, gender,

history of prematurity, duration of hospitalization at the time of the request, location

(general pediatric ward, NICU, PICU, or oncology ward), level and subspecialty of the

requesting physician (attending physician, house staff fellow or resident, surgical or

non-surgical) were recorded (the potential importance of these factors is suggested by a

previous study in adult patients).

The definition of inaccurate requests was clinically significant discrepancies

between communication data elements abstracted from documented requests in the

web-based system and the data in the medical record. Clinically significant

discrepancies were those judged by the study team (Drs. Tamma, Jenh-Hsu and Lai) to

be likely to influence antimicrobial prescribing. The absence of information documented

from the prior requests was not defined as inaccuracy.

43

The definition of each type of inaccurate requests was described as follows:

Laboratory data: including significant discrepancies in hematological,

biochemical, microbiological or radiographic data.

Diagnosis: the diagnosis recorded in the prior request was directly

contradicted by the diagnosis recorded in the medical records.

Physical exam or vital signs: clinically significant inaccurate physical exam

data or vital signs data within the preceding 24 hours of the prior request.

History (present illness and past history): including incorrect information

regarding drug allergies or past antimicrobial use, or clinically significant

inaccuracies in present or past medical history or in presence or absence of

an underlying condition.

Age: clinically significant inaccurate age of patient

The potential risk factors for inaccurate requests were then explored, including

age, gender, primary service, whether the patient was in the ICU, or an oncology or

cystic fibrosis patient, types of antimicrobials, presence of underlying disease,

prematurity (only in patients aged less than 1 year), number of hospital days before the

antimicrobial request, types of infections, types of indications for restricted

antimicrobial use, whether requests were submitted in off-hours, and whether the ASP

team had previously rejected the request, Office hours were defined as the time

period from 8 am to 10 pm each day. Other times were defined as off-hours.

After the determination of inaccuracies of the prior-approval requests, the ASP

team also tried to understand how these inaccuracies might potentially influence the ID

fellow’s approval. The definition of inaccurate requests which might influence the

approvals of PIDF was that the ASP study team re-evaluated all the information from

44

patient’s medical records retrospectively and felt that PIDF might not approve the

antimicrobials if the accurate information had been submitted instead. Because of the

limitations of medical records, we categorized inaccurate requests as influential only in

well-defined, uncomplicated scenarios. In the case of complicated scenarios, the

assumptions were made that inaccuracies were not influential.

2.2.2. Statistical Analysis

The analysis was also conducted using STATA software version 11 (StataCorp,

LP Texas) as described below:

2.2.2.1. Analysis for Proportion of Inaccurate Requests

First, frequency counts and percentages were calculated to describe basic

demographic, clinical information and service characteristics for all of the accurate

requests, inaccurate requests and all requests. Second, t-tests for continuous

variables and chi-square tests for categorical variables were undertaken to

determine whether there were significant difference in those variables between

accurate requests and inaccurate requests. Similarly, frequency counts and

percentages were calculated to describe the type of inaccuracy in each inaccurate

request.

2.2.2.2. Analysis for Potential Risk Factors for Inaccurate Requests

The potential risk factors for inaccurate requests were evaluated first in a

bivariate logistic regression model. A multivariate logistic regression model was

then built with independent variables that had P values less than 0.20 in the

bivariate analysis. The independent variables were considered independent risk

factors if the P value was <0.05 in the multivariate logistic regression model. The

45

associations of independent variables with inaccurate requests in bivariate analyses

were reported as odds ratios for comparison with the odds ratios calculated from

the multivariate logistic regression model. Point estimates with 95% confidence

intervals (CIs) for crude odds ratio and adjusted odds ratio (aOR) were also

calculated.

2.2.2.3. Analysis for Proportion of Inaccurate Requests Which Could be Adversely

Affected by Inaccurate Information

The frequency count and the proportion of each type of inaccurate request

(laboratory data, diagnosis, physical examinations or vital signs, history of illness or

drug allergy and age) and the total number of inaccurate requests which could

potentially adversely affect the approval of PIDF were calculated to determine the

magnitude of the potential effect of inaccurate requests.

46

3. Results

3.1. Results for Aim 1 and 2

3.1.1. Demographic Data

The pediatric ASP team identified 60 pediatric patients for whom use of

restricted antimicrobials was inappropriate at post-prescription review, which occurred

on Day 2-4 after the restricted antimicrobial was initiated. These patients were

randomized to the intervention group (30 patients) or the control group (30 patients).

As shown in Table 3.1, most patients were older than 5 years (66.7%). Although there

were fewer infants in the intervention group than in the control group (10.0% of

patients ≤ 1 year old in the intervention group vs. 20.0% in the control group), this

difference was not significant. The proportion of male patients was lower in the

intervention groups (46.7 vs. 56.7 %), but this difference was not significant. About 30%

of cases belonged to the surgical service and two-thirds (68.3%) of cases were reviewed

within 3 days of admission. The majority of patients had underlying chronic diseases

(83.3%), and the proportion did not differ between groups. The indications for

restricted antibiotic included ‘empiric’ (n=35, 58.3%), ‘directed therapy’ (n=19, 31.7%)

and ‘prophylaxis’ (n=6, 10.0%). Most of those who received ‘prophylaxis’ (5 of 6) were

in the control group. The most common type of infection was gastrointestinal (30%).

There were no significant differences between the intervention and the control groups

with regard to age, gender, clinical service, proportion of patients with underlying

disease, season of admission, time to review, indications for antimicrobial use or types

of infections being treated.

A total of 65 restricted antimicrobial courses in 60 pediatric patients were found

to be inappropriate by the ASP team, including 32 inappropriate antimicrobial courses

47

in the intervention group and 33 inappropriate antimicrobial courses in the control

group. The most frequently restricted antibiotics in the study were beta-lactam

antibiotics (40.0%) and vancomycin (24.6%). No significant differences were found

between the two groups for these courses of antimicrobials.

48

Table 3.1 Demographic data for the intervention group and the control group with patient and antimicrobial course as the units of measure

Variable Intervention Control Total P value

Patient Course Patient Course Patient Course Patient Course

N (%) N (%) N (%) N (%) N (%) N (%)

N (number) 30(100) 32(100) 30(100) 33(100) 60(100) 65(100)

Age

Mean (yrs) 10.8 10.3 8.4 8.1 9.6 9.2 0.17 0.22

0-1 yrs 3(10.0) 4(12.5) 6(20.0) 8(24.2) 9(15.0) 12(18.5) 0.34 0.33

1.1-5yrs 4(13.3) 5(15.6) 7(23.3) 7(21.2) 11(18.3) 12(18.5)

>5 yrs 23(76.7) 23(71.9) 17(56.7) 18(54.6) 40(66.7) 41(63.1)

Male (%) 14(46.7) 16(50.0) 17(56.7) 19(57.6) 31(51.7) 35(53.9) 0.60 0.62

Surgical service 9(30.0) 10(31.3) 9(30.0) 10(30.3) 18(30.0) 20(30.8) 1.00 1.00

With underlying disease

26(86.7) 28(87.5) 24(80.0) 27(81.8) 50(83.3) 55(84.6) 0.73 0.73

Season at antimicrobial review

0.29

Spring 5(16.7) - 5(16.7) - 10(16.7) -

Summer 2(6.7) - 5(16.7) - 7(11.7) -

Autumn 16(53.3) - 18(60.0) - 34(56.7) -

Winter 7(23.3) - 2(6.7) - 9(15.0) -

Days of admission at review

0.61 0.81

1-3 days 22(73.3) 23(71.9) 19(63.3) 21(63.6) 41(68.3) 44(67.7)

4-14 days 6(20.0) 6(18.8) 6(20.0) 7(21.2) 12(20.0) 13(20.0)

>14 days 2(6.7) 3(9.4) 5(16.7) 5(15.2) 7(11.7) 8(12.3)

Type of indication 0.296 0.55

Prophylaxis 1(3.3) 2(6.3) 5(16.7) 5(15.2) 6(10.0) 7(10.8)

Empiric 19(63.3) 20(62.5) 16(53.3) 17(51.5) 35(58.3) 37(56.9)

Directed 10(33.3) 10(31.2) 9(30.0) 11(33.3) 19(31.7) 21(32.3)

Type of infections 0.346 0.30

Respiratory 6(20.0) 6(18.8) 6(20.0) 6(18.2) 12(20.0) 12(18.5)

GI tract 9(30.0) 9(28.1) 9(30.0) 10(30.3) 18(30.0) 19(29.2)

Sepsis 6(20.0) 8(25) 2(6.7) 3(9.1) 8(13.3) 11(16.9)

Prophylaxis 1(3.3) 1(3.1) 5(16.7) 5(15.2) 6(10.0) 6(9.2)

Others 8(26.7) 8(25) 8(26.7) 9(27.3) 16(26.7) 17(26.2)

Type of antimicrobials

- 0.40

Penicillin derivatives a

- 12(37.5) - 14(42.4) - 26(40.0)

Vancomycin - 8(25.0) - 8(24.2) - 16(24.6)

Cephalosporin - 3(6.3) - 2(9.1) - 5(7.7)

Fluroquinolone - 6(18.8) - 1(3.0) - 7(10.8)

Carbapenem - 1(3.1) - 1(3.0) - 2(3.1)

Others - 3(9.4) - 6(18.2) - 9(13.9)

a Penicillin derivatives include: piperacillin/tazobactam, ticarcillin/clavulanic acid

49

3.1.2. Comparison of antibiotic use (restricted and total) in the two study arms

3.1.2.1. Restricted and total antibiotic use measured as “days of therapy per

1000 hospital-days (DOTs)”

As shown in Figure 3.1, restricted antimicrobial use DOTs were lower in

the intervention group than in the control group (DOTs median: 750 vs. 816.7);

however, this did not reach statistical significance (p=0.923). Although several cases

with extended length of stay might have influenced the denominator data for DOTs,

the differences between the two groups were not significant even when those

outlier cases with high leverage (defined as the value of the point higher than the 3rd

quartile plus 1.5 interquartile ranges) were censored (p= 0.767). In addition, the

comparison of total antimicrobial use DOTs showed no significant difference

between two groups with or without “outliers” (p=0.830 and p=0.986, respectively).

3.1.2.2. Inappropriate restricted and total antibiotic use measured as “days of

therapy per 1000 hospital-days (DOTs)”

As shown in Figure 3.2, the median of inappropriate restricted

antimicrobial use was higher in the intervention group than in the control group

(DOTs median: 431.7 vs. 356.5 days) but this difference was not significant

(p=0.306). After removing the “outliers” (3 cases in each group with extended length

of stay), the median days of inappropriate restricted antimicrobial use (DOTs) was

lower in the intervention group than in the control group (DOTs median: 434.8 vs.

625). No statistically significant differences in DOTs for inappropriate restricted

antibiotic use (p= 0.379) or in total inappropriate antimicrobial use were

observed (data not shown).

50

3.1.2.3. Restricted and total antibiotics use measured as “median duration

(days) of antimicrobial agents per episode of infection” and “combined

antimicrobials use (days) per episode of infection”

The use of DOTs as an outcome measure has some limitations. In our study,

a single assessment of antibiotic use was performed for each patient regardless of

length of stay. In some patients with long hospital stays, antibiotics were prescribed

for conditions not evaluated during the initial assessment. Specifically, in our study,

there were 7 cases (23.3%) in the intervention group and 8 cases (26.7%) in the

control group with other episodes of infections in addition to the episode reviewed.

Therefore, if the outcome used is total hospitalization days (as defined in DOTs),

reduction of antibiotic use from intervention group during the intervention-specific

episode might be missed. For this reason, two other measures were introduced for

the comparisons of antibiotic use: the median duration (in days) of restricted

antimicrobial use per episode of infection and the combined restricted

antimicrobial use (in days) per episode of infection (Figure 3.3). The median

duration was shorter in the intervention group than in the control group (median

days: 3.5 vs. 5 days) although it did not reach statistical significance (p= 0.094).

Similarly, the length of combined restricted antimicrobial use was less in the

intervention group than in the control group (median days: 5.5 vs. 6 days); however,

there was still no significant differences between two groups in this outcome

category, perhaps owing to small sample size (p= 0.320). In the comparison of total

antimicrobial use in these two measures, differences were also not significant (data

not shown).

51

3.1.2.4. The influence of the timing of review on the median duration (days) of

restricted antimicrobial agents per episode of infection (Subgroup Analysis)

We hypothesized that earlier review compared to later review by the ASP

team might lead to earlier cessation of unnecessary treatment, shorter duration of

treatment and fewer days of antibiotic therapy. To examine this, we stratified our

analysis of the intervention arm into two categories with similar numbers of

patients in each category: early review (Day 2 after initiation, n=14) and late review

(Day 3 or Day 4 after initiation, n=16). We found that the median duration of

restricted antimicrobial agent use per episode of infection was shorter with earlier

review than with later review in the intervention group, though it did not reach

statistical significance ( p=0.175 Figure 3.4).

52

Figure 3.1 Comparisons of restricted and total antibiotic use (DOTs), with and without outliers

0

500

1,0

00

1,5

00

2,0

00

2,5

00

DO

Ts/

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Control Intervention

Restricted antibiotic use (DOTs) between two arms

0

1,0

00

2,0

00

3,0

00

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00

DO

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Control Intervention

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Without outliers

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Without outliers

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00

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53

Figure 3.2 Comparisons of inappropriate restricted antibiotic use (DOTs), with and without outliers

0

500

1,0

00

1,5

00

2,0

00

DO

Ts/

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Inappropriate restricted antibiotic use (DOTs) between two arms Without outliers

0

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00

DO

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Control Intervention

Inappropriate restricted antibiotic use (DOTs) between two arms

54

Figure 3.3 Comparison of median duration (days) of restricted antibiotics and combined restricted antibiotic use (days) per episode of infections between two groups

05

1015

Med

ian

dura

tion

(day

s)

Control Intervention

Median duration of restricted antibiotic use per patient per episode of infection between two arms

05

1015

2025

Day

s

Control Intervention

Restricted antibiotic use per episode of infection between two arms

55

Figure 3.4 Comparisons of median duration of restricted antibiotics (days) per patient per episode of infection in different review timing in the intervention group

N=14 N=16

05

10

15

Day 2 Day 3-4

Me

dia

n d

ura

tion

(d

ays)

Median duation of restricted antibiotic use per episode of infection by review timing

56

3.1.3. Reasons for inappropriate antimicrobial use in two groups

Table 3.2 described the reasons for inappropriate antimicrobial use in

descending order of frequency. The most common reason was “viral infection or no

evidence of infection” (40%). “Overlapping double coverage of antimicrobial agents”

(13.9%) and “susceptible to narrower agent” were also frequently found as reasons for

inappropriate antimicrobial use. There were no significant differences in reasons for

inappropriate antimicrobial use between the intervention and control groups.

57

Table 3.2 Reasons of inappropriate antimicrobial use (in descending order)

Reason of inappropriate Antimicrobial use

Intervention N (%)

Control N (%)

Total N (%)

p-value

Viral Infection/No infection 12(37.5) 14(42.4) 26(40.0) 0.665

Double coverage 3(9.4) 6(18.2) 9(13.9)

Susceptible to narrower agent 4(12.5) 4(12.1) 8(12.3)

Others 4(12.5) 2(6.1) 6(9.3)

Inappropriate duration 4(12.5) 1(3.0) 5(7.7)

Bug/drug mismatch 2(6.3) 1(3.0) 3(4.6)

Colonization 1(3.1) 2(6.1) 3(4.6)

Dose inappropriate 1(3.1) 2(6.1) 3(4.6)

Prolonged surgical prophylaxis 0 1(3.0) 1(1.5)

Require broader agent 1(3.1) 0 1(1.5)

Total 32(100) 33(100) 65(100)

58

3.1.4. Proportion of inappropriate antibiotic use on Days 2 and 3 after ASP team

review

The ASP intervention had a significant impact on reduction of courses of

inappropriate antibiotics. At Day 2 after the ASP team’s review, the prevalence of

inappropriate antibiotic use was significantly lower in the intervention group than the

control group (34.4% vs. 75.8%, p=0.001, Table 3.3). At Day 3 after review, 12.0%

(3/25) of the antimicrobial courses in the control group had been corrected by the

clinical team. However, the effect of the intervention was still significant at Day 3 after

the ASP team’s review (31.3% vs. 66.7%, p=0.006, Table 3.3).

In the intervention group, approximately one third (n=10, 31.3%) of

antimicrobial courses still met the definition for inappropriate use at Day 3 after the

ASP team’s review. Among these antimicrobial courses, the most common instances of

inappropriate use were a 10 day course of treatment for acute tracheitis rather than the

recommended 5 day course ( 3 cases) and unnecessary double coverage of anaerobic

pathogens (such as piperacillin/tazobactam and metronidazole) in another 3 cases.

3.1.4.1. The influence of timing of ASP review on the proportion of inappropriate

antimicrobial courses

Early ASP review (review at Day 2 after the restricted antibiotic was initiated)

and later ASP review (review at Day 3 or Day 4 after the restricted antibiotic was

initiated) both had a significant impact on the number of courses of inappropriate

antimicrobials when assessed 1 day after the ASP team’s review (p= 0.038 and p=0.016)

(Figure 3.5). Importantly, there was no significant difference in reduction in

inappropriate courses of antimicrobials in the intervention arm when ASP review was

conducted at Days 2 or Day 3-4 (p=1.000) (Figure 3.5).

59

Table 3.3 Comparisons of the proportions of inappropriate antimicrobial courses at Day 2

and Day 3 after post-prescription review between two groups (unit of analysis:

antimicrobial course)

Time Intervention group

Control Group

Prevalence ratio [95% CI] (Intervention/Control)

P value

Day 2 34.4% (11/32) 75.8%(25/33) 0.454 [0.271,0.760] 0.001

Day 3 31.3% (10/32) 66.7% (22/33) 0.469 [0.266,0.827] 0.006

60

Figure 3.5 The influence of review timing after the antibiotic was initiated on the proportion of inappropriate antimicrobial courses

Intervention Control

Day2 33.3 72.2

Day3-4 35.3 80

20

40

60

80

100

Pe

rce

nta

ge

Inappropriate antimicrobial course(%) by review timing

61

3.1.5. Potential factors associated with inappropriate antimicrobial courses at Day

2 after the ASP team’s review

In the entire cohort of 60 patients include in this study, 33 were receiving

inappropriate antimicrobials at Day 2 after the ASP team’s review (Table 3.4). Patients

receiving inappropriate antimicrobials were older, more likely to be on the medical

service, more likely to be receiving antimicrobial prophylaxis, and more likely to have GI

infections than those not receiving inappropriate antimicrobials courses at the time of

follow-up. However, none of these factors reached statistical significance. ASP

intervention was the only significant independent variable associated with appropriate

antimicrobial course (p=0.009, Table 3.4).

3.1.6. Rate of Compliance with the Recommendation at Day 2 and Day 3 after the

ASP team’s review

In some instances, more than one antibiotic recommendation was made or

recorded for each patient. Overall, there were 37 recommendations made by the ASP

team for 32 patients in the intervention group. In addition, 35 recommendations were

recorded in the data collection forms for 33 patients in the control group, but these

were not communicated (Table 3.5). “Stopping therapy”, (either eliminating

overlapping antibiotic therapy or stopping therapy because there was no evidence of

infection) was the most frequent recommendation (55.6%). “Modifying therapy” was

the next most frequent recommendation (26%), and included narrowing of the

antibiotic spectrum (11.2%), broadening of the antibiotic spectrum (2.8%), adjustment

of dosage (5.6%) or shortening the duration of antibiotic treatment (5 courses, 6.9%).

All 5 recommendations for shortened therapy were suggestions of 5 days of therapy for

62

acute tracheitis instead of 10 days. In 18% of recommendations, the ASP team

recommended clinical consultation with the pediatric infectious disease team because

the patient had a complicated infection or the choice of antimicrobials was complex.

Overall, the average time spent in communicating with the primary team member was

11.8 minutes in the intervention group.

Overall, the compliance rate at Day 2 after post-prescription review in the

intervention group (the physicians changed orders according to the recommendations

of ID fellows) was significantly higher (67.6% vs. 22.9%) than in the control group (the

physicians auto-corrected without prompting by the ASP team) (p<0.001, Table 3.6).

Specifically, therapies were stopped significantly more frequently in the intervention

group (58.8%) than in the control group (21.7%) (p=0.024). “Modifying therapy” and

“ID consults” both were changed more in the intervention group than in the control

group; however, they did not reach statistical significance (p=0.170 and p= 0.052)

probably due to the relatively small sample size. In the intervention group, the

compliance rate was highest for “ID consult” (88.9%). The compliance rate was a little

lower for “modifying therapy” (63.6%) and for “stopping therapy” (58.8%).

3.1.7. Outcomes of patients when ASP team recommended alternative empiric

therapy or stopping therapy in two arms

Thirty-six antimicrobial courses for 35 patients were further followed up by

medical chart review (Table 3.7). Specifically, the ASP team made 4 recommendations

for alternative empiric therapies (broadened or narrowed therapy) for the intervention

group and recorded 5 alternative therapies for the control group. All the alternative

empiric therapies covered the subsequent culture results. In cases for which the ASP

63

team recommended stopping antimicrobial therapy because there was no clear

indication of antimicrobial use, no patient developed an infection within 48 hours after

cessation of antibiotics.

64

Table 3.4 Potential factors associated with inappropriate antimicrobial courses at Day 2 after the ASP team’s review (Unit of analysis: patient)

Variable Inappropriate

antibiotic courses

No. of patient (%)

Appropriate antibiotic

courses

No. of patient (%)

P value

N (no. of patients) 33 27

Age 0.863

0-1 yrs 4(12.1) 5(18.5)

1.1-5yrs 6(18.2) 5(18.5)

>5 yrs 23(69.7) 17(63.0)

Male (%) 18(54.6) 13(48.2) 0.796

Surgical service 8(24.2) 10(37.0) 0.397

With underlying disease 29(87.9) 21(77.8) 0.322

Season at antimicrobial

review

0.624

Spring 4(12.1) 6(22.2)

Summer 3(9.1) 4(14.8)

Autumn 21(63.6) 13(48.2)

Winter 5(15.2) 4(14.8)

Days of admission at

review

0.405

1-3 days 20(60.6) 21(77.8)

4-14 days 8(24.2) 4(14.8)

>14 days 5(15.2) 2(7.4)

Type of indication 0.546

Prophylaxis 4(12.1) 2(7.4)

Empiric 17(51.5) 18(66.7)

Directed 12(36.4) 7(25.9)

Type of infections 0.243

Respiratory 8(24.2) 4(14.8)

GI tract 12(36.4) 6(22.2)

Sepsis 2(6.1) 6(22.2)

Prophylaxis 4(12.1) 2(7.4)

Others 7(21.2) 9(33.3)

Intervention 11( 33.3 ) 19( 70.4 ) 0.009*

*p<0.05

65

Table 3.5 Recommendations recorded in the data collection forms by the ASP team for two groups

Recommendations Intervention

N (%)

Control

N (%)

Total

N (%)

Stopping therapy 17( 45.9) 23(65.7) 40(55.6)

Duplicated therapy eliminated 7(18.9) 6(17.1) 13(18.1)

No evidence of infection 10(27.0) 17(48.6) 27(37.5)

Modifying therapy 11(29.7) 8(17.2) 19(26.4)

Narrowed antibiotics 3(8.1) 5(14.3) 8(11.1)

Broadened antibiotics 2(5.4) 0 2(2.8)

Shortened duration of therapy 4(11.8) 1(2.9) 5(6.9)

Adjusted dose 2(5.4) 2(5.7) 4(5.6)

ID consult 9(24.3) 4(12.4) 13(18.0)

Total 37(100) 35(100) 72(100)

66

Table 3.6 Comparisons of changes noted at Day 2 after post-prescription review between intervention and control groups (N_change/N_total (%))

Intervention arm Control arm Total P value

Stopping therapy 10/17 (58.8) 5/23(21.7) 15/40(37.5) 0.024* Modifying therapy 7/11(63.6) 2/8(25.0) 9/19(47.4) 0.170 ID consult 8/9(88.9) 1/4( 25.0) 9/13(69.2) 0.052 Total cases 25/37(67.6) 8/35(22.9) 33/72(45.8) <0.001

*p<0.05

67

Table 3.7 Outcome of patients when ASP recommended alternative therapy or no therapy

Outcome Intervention group

Control group

Number of alternative therapy

(broadened or narrowed)

Recommended

4 5

Alternative therapy used 24 hrs

after review

4 2

Positive cultures 2 3

ASP recommendation covered

positive cultures

2 3

Number of no indication of

antimicrobials recommended

10 17

Therapy stopped within 24 hrs after ASP team’s review

8 4

Developed subsequent infection 0 0 *

*In the control group, the outcome was only followed up in the 4 cases in which the

antimicrobial was stopped noted at Day 2 after ASP team’s review

68

3.2. Results for aim 3

3.2.1. Demographic data

There were a total of 1159 pediatric prior-approval requests for use of

restricted antimicrobials in the 4 month study period (Table 3.8). Inaccuracies

(discrepancies between requests and medical records) occurred for 8.7% (95% CI,

7.2%-10.5%) of all requests. Most patients were >5 years old (53.1%), had underlying

disease (76.8%), were on the medical service (87.3%), were not oncology patients

(78.0%), not cystic fibrosis patients (88.9%) and not ICU patients (64.3%). Most

requests were submitted in the first 2 days following admission (52.3%), during normal

office hours (8AM-10 PM; 85.4%) and under the indication of empiric therapy (53.0%).

Respiratory tract infections (25.7%) and sepsis (23.9%) were the most frequent

infections encountered. The antibiotic most frequently requested was vancomycin

(32.5%).

3.2.2. Types of inaccurate requests and examples

The percentages of requests by type of inaccuracy are shown in Table 3.9. The

most common types of inaccuracy were errors in laboratory data (34.6%) and in patient

history (23.8%). In Table 3.10, some examples of each type of inaccuracy are shown.

69

Table 3.8 Basic demographic and clinical data in the pediatric prior-approval requests (unit of analysis: antimicrobial request) Categories Accurate

N (%)

Inaccurate

N (%)

Total

N (%)

P value

Number of requests 1058(91.3) 101 (8.7) 1159(100)

Age

Mean (yrs) 8.0 6.8 7.8 0.120

0-1yr 300(28.4) 38(37.6) 338(29.2) 0.139

1.1yr-5 yrs 191(18.0) 14(13.9 ) 205(17.7)

5.1yrs-21yrs 567(53.6) 49(48.5) 616(53.1)

With underlying diseases No underlying disease

814(76.9) 244(23.1)

76(75.3) 25(24.7)

890(76.8) 269(23.2)

0.712

Surgical service

Non-surgical service

127(12.0)

931(88.0)

20(19.8)

81(80.2)

147(12.7)

1012(87.3)

0.029

Oncology patient

Non-oncology patient

Cystic fibrosis patient

Non-cystic fibrosis patient

244(23.1)

814(76.9)

118(11.2)

940(88.0)

11(10.9)

90(89.1)

11(10.9)

90(89.1)

255(22.0)

904(78.0)

129(11.1)

1030(88.9)

0.004

1.0

Days of admission at request

1-2 days 545(51.5) 62(61.4) 607(52.3) 0.126

3-7 days 168(15.9) 10(9.9) 178(15.4)

≥ 8 days 345(32.6) 29(28.7) 374(32.3)

Off hours( 10pm-8am)

Office hours

174(16.5)

884(83.5)

18(17.8)

83(82.2)

192(16.6)

967(85.4)

0.680

ICU patients

Non-ICU patients

367(34.7)

691(65.3)

47(46.5)

54(53.5)

414(35.7)

745(64.3)

0.022

Rejected by ID fellows 144(13.6) 18(17.8) 162(14.0) 0.230

Approved by ID fellows 914(86.4) 83(82.2) 997(86.0)

Type of indication

Prophylaxis 151(14.3) 21(20.8) 172(14.8) 0.160

Empiric 561(53.0) 53(52.5) 614(53.0)

Directed 346(32.7) 27(26.7) 373(32.2)

Types of infections 0.043

Respiratory 273(25.8) 25(24.7) 298(25.7)

GI tract 106(10.0) 9(8.9) 115(9.9)

Sepsis 263(24.9) 14(13.9) 277(23.9)

Prophylaxis 149(14.1) 22(21.8) 171(14.8)

Others 267(25.2) 31(30.7) 298(25.7)

Antimicrobial type 0.380

Penicillin derivatives a 117( 11.1) 12(11.9) 129(11.1)

Vancomycin 341(32.2) 36(35.6) 377(32.5)

Cephalosporin 147(13.9) 17(16.8) 164(14.2)

Fluoroquinolone 64(6.0) 6(5.9) 70(6.0)

Carbapenem 77(7.3) 3(3.0) 80(6.9)

Antifungals 134(12.7) 7(6.9) 141(12.2)

Others 178(16.8) 20(19.8) 198(17.1)

a Penicillin derivatives included: ampicillin/sulbactam, piperacillin/tazobactam, ticarcillin/clavulanic acid

70

Table 3.9 The frequencies of different types of inaccuracies among the inaccurate requests

Types N (%)

Laboratory data 35 (34.6)

History(Present illness and past history) 24 (23.8)

Age 21 (20.8)

Diagnosis 12 (11.9)

Physical exam/vital signs 9 (8.9)

Total 101(100)

71

Table 3.10 Examples of different types of inaccuracies of prior-approval requests and the potential influence upon PIDF approval

Types From requests From medical chart Potential influence

Laboratory data Recent positive culture with pseudomonas infection, resistance to amikacin, ciprofloxacin

No such resistance data was documented; most recent laboratory data showed pseudomonas sensitive to amikacin, ciprofloxacin

Requested ceftazidime for treatment for pseudomonas. PIDF might not approve since the data had shown that the organism was susceptible to amikacin

History(Present illness and past history)

Post-surgical infection

No infection was documented; the chart mentioned post-operative antimicrobial prophylaxis

Requested piperacillin/tazobactam for post-surgical infection. PIDF might not approve since no infection was documented

Age Neonate; persistent fever

Patient was already 16 months old

Requested vancomycin and piperacillin/tazobactam for suspected neonatal sepsis. PIDF might not approve broad-spectrum antibiotic use since the patient was 16 months old

Diagnosis Diagnosed as cellulitis

No skin rash or edema was noted; afebrile

Requested vancomycin for cellulitis. PIDF might not approve because the case was consistent with a viral syndrome based on clinical and lab findings.

Physical exam/vital signs Patient was febrile with seizure activity

Afebrile; a case of occipital skull fracture with seizure activity

Requested vancomycin for meningitis. PIDF might not approve because the patient had no fever and the seizure might be due to occipital skull fracture with subdural hematoma found in CT scan

72

3.2.3. Potential Factors Related to Inaccuracy of Antimicrobial Requests

3.2.3.1. Crude odds ratio

In bivariate analyses (Table 3.11), patients on the surgical service, not on the

oncology service, or in the ICU were each significantly more likely to have

inaccurate antimicrobial requests (p=0.029, p=0.004, p=0.022, respectively). Infants

(ages 0-1 year) and patients with “prophylaxis” as the indication of restricted

antimicrobials were also more likely to have inaccurate antimicrobial requests;

however, neither reached statistical significance (p= 0.052 and p=0.092).

3.2.3.2. Adjusted odds ratio

A multivariate logistic regression model was built with independent

variables that had a P value less than 0.20 in the bivariate analysis. The results

showed that the patients on the surgical service (adjusted OR=2.087), in the ICU

unit (adjusted OR=1.629), and non-oncology patients remained significantly more

likely to have inaccurate antimicrobial requests (p=0.011, p=0.043, p=0.036,

respectively). Additionally, “prophylaxis” as an indication for restricted

antimicrobial use was also significantly more likely to be associated with inaccurate

requests in the multivariate logistic regression model (adjusted OR=1.719, p=0.044)

(Table 3.12).

3.2.3.3. Subgroup Analysis

As shown in table 3.11, requests for use of restricted antimicrobials in

infants were more likely to be inaccurate with borderline significance (p=0.052).

For this reason, we performed a post-hoc stratified analysis by age (> 1 year old and

≤ 1 year old) to determine whether the potential risk factors held true in each age

category. As shown in Table 3.13, patients over 1 year of age who were either on

73

the surgical service, not on the oncology service ,or in the ICU were still significantly

more likely to have inaccurate antimicrobial requests (p=0.017, p=0.019, p=0.017,

respectively ) in the multivariate logistic regression model. In this age group,

requests with “prophylaxis” as the indication for restricted antimicrobials was no

longer significantly associated with inaccurate requests (p=0.097). In contrast,

none of the above potential risk factors were significantly associated with the

inaccurate requests in infants aged ≤ 1 year old (Table 3.13).

74

Table 3.11 Bivariate analysis of potential risk factors of inaccurate requests

Variable Accurate N

(%)

Inaccurate

N (%)

Odds Ratio P value

Number of requests 1058(91.3) 101(8.7)

Age

0-1yr 300(88.8) 38(11.2) 1.524 [0.997,2.329]

0.052

1.1-21yrs 758(92.3) 63(7.7)

With underlying diseases No underlying disease

814(91.5) 244(90.7)

76(8.5) 25(9.3)

0.911 [0.567,1.464]

0.712

Surgical service

Non-surgical service

127(86.4)

931(92.0)

20(13.6)

81(8.0)

1.810

[1.073,3.055]

0.029*

Oncology patient

Non-oncology patient

244(95.7)

814(90.0)

11(4.3)

90(10.0)

0.408

[0.215,0.775]

0.004*

Cystic fibrosis patient

Non-cystic fibrosis

patient

118(91.5)

940(91.3)

11(8.5)

90(8.7)

0.974

[0.506,1.874]

1.0

Days of admission at

request

0.504

1-7 days 713(90.8) 72(9.2) 1.201

≥ 8 days 345(92.2) 29(7.8) [0.766,1.884]

Off hours( 10pm-8am)

Office hours

174(90.6)

884(91.4)

18(9.4)

83(8.6)

1.102

[0.645,1.881]

0.68

ICU patients

Non-ICU patients

367(88.7)

691(92.8)

47(11.3)

54(7.2)

1.639

[1.087,2.472]

0.022*

Rejected by ID fellows

Approved by ID fellows

144(88.9)

914(91.7)

18(11.1)

83(8.3)

1.377

[0.803,2.360]

0.23

Type of indication

Prophylaxis 151(87.8) 21(12.2) 1.577 0.092

Non-prophylaxis 907(91.9) 80(8.1) [0.946,2.627]

* p<0.05

75

Table 3.12 Multivariate analysis of risk factors for inaccurate requests

Variable No. of Prior Requests

Percentage of inaccuracy (%)

Adjusted Odds ratio ( 95% CI)

Adjusted p value

Oncology patient

(Oncology vs. non-oncology)

255

904

4.3

10.0

0.484

[0.246,0.952]

0.036

Surgical service

(Surgical vs. non-surgical)

147

1112

13.6

8.0

2.087

[1.180,3.691]

0.011

ICU patient

(ICU vs. non-ICU)

414

745

11.3

7.2

1.629

[1.016,2.614]

0.043

Prophylaxis as indication for

Restricted antimicrobials

(Prophylaxis as indication vs.

Other indications)

172

987

12.2

8.1

1.719

[1.016,2.910]

0.044

76

Table 3.13 Odds ratio (OR), adjusted odds ratio (aOR) and adjusted p value for risk factors of inaccuracies aged ≤1 year and ˃1 year old

Variable Age ( yr)

No. of Prior Requests

Percentage of inaccuracy (%)

OR aOR adjusted p value

Oncology patient

vs. non-oncology

≤1 26

312

11.5

11.2

1.032

[0.295,3.616]

1.087

[0.289,4.086]

0.902

˃1 229

592

3.5

9.3

0.353

[0.166,0.754]

0.389

[0.177,0.854]

0.019*

Surgical service

vs. non-surgical

≤1 27

311

14.8

10.9

1.417

[0.462,4.342]

1.558

[0.432,5.623]

0.498

˃1 120

701

13.3

6.7

2.141

[1.170,3.916]

2.177

[1.150,4.122]

0.017*

ICU patient vs.

non-ICU

≤1 215

123

11.2

11.4

0.978

[0.486,1.910]

1.123

[0.495,2.550]

0.781

˃1 199

622

11.6

6.4

1.901

[1.108,3.262]

1.989

[1.128,3.506]

0.017*

Prophylaxis as

indication for

Targeted

antimicrobials vs.

non-prophylaxis

≤1 66

272

15.2

10.3

1.556

[0.715,3.389]

1.566

[0.717,3.420]

0.260

˃1 106

715

10.4

7.3

1.476

[0.744,2.929]

1.830

[0.896,3.736]

0.097

* p<0.05

77

3.2.4. Types of inaccurate requests and potential influences on the approvals of ID

fellows

We found that incorrect information could have potentially affected the ID

fellows’ approval in about 45% of the cases (95% CI, 34.7%-54.8%) (Table 3.14). Some

examples are shown in Table 3.10. In specific type of inaccuracies, inaccuracies in

“diagnosis” or “patient history” were more likely to influence approval decisions of

approvals than inaccuracies in laboratory data (p < 0.05). Age appeared to be a less

important factor.

3.2.4.1. Subgroup Analysis

When considering patients aged ≤1 year old (Table 3.15), most inaccuracies

(76.3%) were judged to be non-influential (95% CI, 59.8%-88.6%). Specifically,

most inaccuracies occurred in patient history or patient age. Of the age

discrepancies, only 5.9% (95%, 0.2%- 28.7%) were thought to potentially influence

the PIDFs’ approvals. Among these 17 cases of age discrepancies, 7 cases were

requests for palivizumab use and were not influential because they were either

preterm babies or suffered from congenital heart disease and were thus eligible for

palivizumab use at the time of the request.

In contrast, for patients aged >1 year old (Table 3.15), inaccuracies were

more likely to potentially influence the PIDF’s approval (57.1%) (95% CI, 44.0%-

69.5%). Most inaccuracies occurred in laboratory data and patient history. Of the

inaccuracies in patient history, about 81.3 %( 95% CI, 54.4%-96.0%) were judged to

potentially influence the PIDF’s approval.

78

Table 3.14 The potential influence of inaccuracies on approval by PIDF

Types Influence N (%)

No influence N (%)

Total N (%)

P value

Laboratory data 14(40.0) 21(60.0) 35(100) ---

Diagnosis 9(75.0) 3(25.0) 12(100) 0.045

Physical exam/vital signs 3(33.3) 6(66.7) 9 (100) 0.715

History(Present illness and past history)

17(70.8) 7 (29.2) 24 (100) 0.022

Age 2 (9.5 ) 19(90.5) 21(100) 0.024

Total 45(44.6) 56(55.4) 101(100)

79

Table 3.15 Types of inaccuracies in patients aged ≤1 year old and > 1 year old

Types Influence N (%)

No influence N (%)

Total N (%)

≤1 yr > 1 yr ≤1 yr > 1 yr ≤1 yr > 1 yr

Laboratory data 1(20.0)

13(43.3) 4(80.0)

17(56.7) 5(100)

30(100)

Diagnosis 1(100)

8(72.7) 0

3(27.3) 1(100)

11(100)

Physical exam/vital signs 2(28.6)

1(50.0) 5(71.4)

1(50.0) 7(100) 2(100)

History(Present illness and past history)

4(50.0)

13(81.3) 4(50.0)

3(18.7) 25(100)

16(100)

Age

1(5.9)

1(25.0) 16(94.1)

3(75.0) 17(100)

4(100)

Total 9(23.7) 36(57.1) 29(76.3) 27(42.9) 38(100) 63(100)

80

4. Discussions and Recommendations

4.1. Discussion

Only a few previous studies have explored the effectiveness of pediatric stewardship

programs in reducing the amount of antibiotic use and reducing the proportion of

inappropriate antibiotic courses. Our study, an intervention with a randomized, controlled

design, was focused on determining the effectiveness of post-prescription review. Our study

showed a significantly lower proportion of inappropriate antibiotic courses in the intervention

group than in the control group at Day 2 (p=0.001) and Day 3 (p=0.006). ‘Auto-correction’ of

antimicrobial therapy occurred in a small number of cases in the control group (12% or 3

courses out of 25 courses) from Day 2 to Day 3. We also did not find adverse outcomes

associated with post-prescription review. These two findings demonstrate the utility of post-

prescription review for enhancing appropriate antimicrobial use in pediatric patients.

Approximately one third of antimicrobial courses (31.3%) in patients in the

intervention group met the definition for inappropriate antimicrobial use at Day 3 after the ASP

team’s review. It is important to consider why almost a third of the ASP recommendations were

not followed in the intervention group. The most common recommendations that were not

followed were the “prolonged treatment for tracheitis” and “unnecessary double coverage of

anaerobes”. Why these specific recommendations were not followed is uncertain, but lack of

knowledge of recent literature,48 the perception that the antimicrobials are rarely harmful,74

diagnostic uncertainty, the fear of the failure to treat a treatable infection, and the absence of

clear guidelines might have been contributing factors.112

Our study demonstrated a statistically significant reduction in the proportion of

inappropriate treatment courses, but not in DOTs or median duration of therapy. The most

common choices for the measures of antibiotic use include defined daily dose (DDD) and days

81

of therapy (DOT). DDD is defined as “the assumed average maintenance dose per day of a drug

used for its main indication in a 70-kg adult”. WHO currently uses DDDs methodology to

measure antimicrobial use. 113 DDDs are normalized in most studies to 1000 patient-days to

control for difference in hospital census. However, DDD is not appropriate in analyzing drug use

in children because the maintenance dose varies significantly in children depending on age and

weight. Instead, the DOT measurement is preferred for pediatric populations because it is

independent of age and body weight difference.113 One DOT is defined as “the administration of

a single agent on a given day regardless of the number of the doses administered or dosage

strength.”114 It is also often normalized to 1000 patient-days. Because of the above reasons, we

used DOTs instead of DDDs as one of the measures of antibiotic use in our study. Total DOT is

an attractive outcome measure because it includes all antimicrobial use in every episode of

infection and can easily be used as a benchmark for comparisons in different institutions

because it is independent of differences in restricted antimicrobials, and definitions of

inappropriateness of antibiotics. However, given our study design, assessment on a “per

episode” for this study might have been more useful than DOTs because additional episodes of

infections could have contributed to DOTs but were not assessed in our study. Therefore,

“median duration (days) of antimicrobial agents per episode of infection” and “combined

antimicrobial use (days) per episode of infection” was a better measure for our study. In

addition, it was important to measure total antibiotic use in additional to restricted antibiotic

use because the reduction of study agents might result in increased used of non-restricted

antimicrobials.75

There are several possible reasons why were unable to show differences in DOTs or

median duration of therapy with the use of post-prescription review. First, it is difficult to

detect small but meaningful reductions with the relatively small sample size that we had in our

82

study (30 cases in each arm). Using the calculations derived by Noether,115 we estimated the

sample size needed for each group to measure various antimicrobial use outcomes (Table 4.1).

As shown, our sample size was sufficient to measure significant changes in the proportion of

inappropriate antibiotic courses, but not for other outcome measures. For some measures, such

as median duration (days) of restricted antibiotic agents per episode of infection, the sample

size is small enough that a study to measure this outcome might be conducted in a single large

center. However, other outcome measures, such as DOTs, probably need to be applied to

multicenter studies.

Second, all of the cases included were receiving broad-spectrum restricted antibiotics

and often had complex medical problems. Although the basic demographic and clinical

information was similar in two groups in our study, there might be some unmeasured

confounders differentially distributed in two groups, especially in pediatric patients in a

tertiary center, such as different pre-existing medical conditions with various severities, which

could influence antimicrobial use. Third, although the post-prescription review program can be

an effective practice to reduce antimicrobial use, it takes time to “buy into” the process74, and

the program which was still in its earliest stages at the time of our study. Fourth, more than

half of the recommendations in the intervention group were to modify therapy or obtain an ID

consult, which might not necessarily reduce the amount of the antibiotic use although they

might improve the appropriateness of the antibiotic use. Fifth, our study was limited to a single

post-prescription review, and some studies have shown that additional reviews provide more

opportunities to review the antibiotic use, which might lead to greater impact.87

With regard to the timing of post-prescription review, we hypothesized that earlier

review might be more likely to lead to antimicrobial cessation or modification. However, we

were unable to demonstrate this in our subgroup analysis. This may be because of small

83

sample size and might have to be evaluated in a larger study. However, it is helpful to know

that intervention at Day 3 was still associated with a significant reduction in inappropriate

courses of antibiotics.

The compliance rate (66.7%) in the intervention group was similar to some other

studies, 74,81; however, higher compliance rates have been documented in several studies.

87,102,106 For example, a study of a post-prescription review program in a pediatric hospital

demonstrated a compliance rate of 92%.106 However, most of their recommendations were for

dose adjustment which might be easier for treatment physicians to accept. Another study in the

adult ICU setting with post-prescription review twice for the targeted antimicrobials (3rd and

10th day of the therapy) with ID physicians approved every identified inappropriate case and

making suggestions also showed high compliance rate (82%).87 The recommendations after 10

days of therapy might also be more convincing because more clinical and microbiological

information was available. Besides, the expertise and trust provided by ID attending physician

might be better than by ID fellows. The authors found that active interaction with the treatment

team from the early stages of ASP program planning also played a major role for their success.

Finally, a survey found that the “prescribing etiquette” could also have major influences upon

the compliance rate. 116 If the ASP team did not communicate directly with the leader of the

treating team—attending physician, as seen in our study, the compliance might not be very high.

We found that the compliance rate with the recommendation to stop therapy was lower

compared to the other 2 categories in the intervention group. Several potential reasons for this

finding are listed below. First, in our study, no evidence of infection and double coverage of

certain pathogens were the most common reasons for inappropriate antimicrobial use. The

suggestions of “stopping therapy” therefore comprised the most frequent recommendations

with the greatest statistical power. Second, the reluctance to stop rather than modify

84

antimicrobials “reflects the discomfort that some prescribers have with stopping therapy if a

patient has improved on therapy, even when an infection etiology is not identified.”74

Furthermore, the physicians might perceive that antibiotics rarely harmful or might have

difficulty in acknowledging the undesirable consequences (such as bacterial resistance)

because the prescription and the consequence could be “so widely separately in time”. These

also contributed to the lower acceptance rate of “stopping therapy”.74,117 In order to modify this

kind of physician’s behavior, the incorporation of rapid diagnostic tests such as using low levels

of procalcitonin (low likelihood of severe bacterial infection) to guide the discontinuation of

antibiotics,118 additional studies supporting shorter duration of therapy, particularly in the

pediatric population,119 additional studies that demonstrate the improved patient outcome, and

campaigns to raise awareness of the problem of bacterial resistance might be useful. 117

Consistent with other studies,102 our study showed no antibiotic treatment failure and

no inadequate coverage when the ASP team recommended narrowed therapy or cessation of

therapy (Table 3.7). We chose these outcome measures for a number of reasons. Few studies

have tried to study the outcome of mortality rate as the effect of ASP although it is the most

objective measure, 120 because fortunately, mortality is relatively rare in children. Therefore,

microbiological treatment failure, as in our study, might be considered. In addition, re-

admission rate, need for more advanced care (such as need for ICU care, cardiovascular or

respiratory support) could be potential patient outcomes if the sample size is adequate.40

Antimicrobial resistance is the most difficult outcome measure since it often takes a long time

to develop. The available data are often not patient-specific and thus demonstrate weaker

association. 40

85

In aim 3, the results showed that inaccuracy (discrepancy between requests and

medical records) occurred in 8.7% of all requests, most of which were not discovered by the

pediatric ID fellows. Encouraging fellows to access medical records for specific requests or

indications (for example, vancomycin constituted 35.6% of all the inaccuracies in our study)

might be feasible.

The inaccuracy rate (8.7%) that we observed was less than another study evaluating

adult patients (39%).78 There may be several reasons for this. First, our web-based prior

approval system might have fewer inaccuracies than phone communications since the contents

of the requests are all documented and the prescribing physicians might be less inclined to

provide inaccurate information in the documented forms. Second, web-based systems might

decrease the opportunity for ID fellows to acquire specific clinical information through instant

communications,47 and thus might have less chance to acquire inaccurate information. In

addition, the operation definitions of inaccuracies might not be the same in different studies.

Patients on the surgical service, in the ICU unit, non-oncology patients and those with

“prophylaxis” as indication for antimicrobials were significantly more likely to have inaccurate

antimicrobial requests in a multivariate logistic regression analysis (p<0.05). Our findings are

consistent with a study in adults, which showed that calls from surgical services were also more

likely to have inaccurate communications.78 There are some possible explanations for the

findings in our study. First, PIDF and medical house staff work together more frequently than

PIDF and surgical house staff because of the rotation system, which might lead to more accurate

descriptions of the patient data by the pediatric housestaff. Secord, steeper “hierarchy” among

surgeons might cause surgical residents to feel pressured to obtain antimicrobials.78 In addition,

most antimicrobial requests in oncology patients in the Johns Hopkins Children’s Center were

based on algorithms which were less likely to be inaccurate (data not shown).

86

Based on our finding that some risk factors for inaccurate requests were distributed

differently in patients age ≤ 1 year old and > 1 year old, , we conducted a subgroup analysis and

found that age acted as an effect modifier for several risk factors (Table 3.13), although the

interactions between these risk factors and age were not statistically significant as measured by

a likelihood test using multivariate logistic regression (p=0.13, p=0.22 and p=0.89 for non-

oncology service, ICU service and surgical service interaction with age, respectively). Providing

an overall estimate would have masked this heterogeneity.

4.2. Strengths and Limitations of the Study

Our study used a prospective, patient-level randomized controlled study design to

explore the benefit of the post-prescription review intervention. A prospective study has a

number of advantages over a retrospective review.102 The randomized controlled design meant

that the ASP team was not able to choose cases with easy interventions because they did not

know the group assignment when they reviewed the cases, which should help to avoid

overestimation of the intervention effects. Weaknesses of a patient-level randomization study

design are that it is logistically difficult and time-consuming for the study team, as well as the

possibility of a “contamination” effect if the treating physicians simultaneously take care of

patients from two groups and recommendations for the intervention group influence

physicians’ antimicrobial use in treating the patients in the control group. In our intervention

study, there were total of 39 attending physicians taking care of 60 patients. The rate of auto-

correction in the control group was higher in instances in which the attending physician had

ever cared for cases from the intervention group (4 changes out of 13 recommendations,

30.8%) than when they had not (4 changes out of 22 recommendations, 18.2%), although this

87

difference was not statistically significant (p=0.391). In theory, this could potentially be

evidence of a “contamination effect”, which would bias the intervention effect toward the null.

A cluster randomized controlled study, including randomization of different units or

hospitals to receive the intervention or not, is less likely to produce a “contamination effect”.

However, it is difficult to randomize these clusters because the units or hospitals may be

fundamentally different which could result in confounding or effect modification. 40

Unlike a randomized controlled design, a quasi-experimental design comparing

outcomes before and after the intervention is simple and quick to implement, but it could be

influenced by “maturation effects”. 67 An interrupted time series approach could help alleviate

such confounding, although it still could be difficult to determine whether a change noted is due

to the intervention or to other factors, and it is more time consuming. A cross-over design could

also decrease the confounding because each patient or unit serves as its own control, but the

carryover effect from intervention could influence the effect of the intervention if the washout

period is not long enough.

For aim 3, the web-based prior approval programs were a well-documented source of

information for comparisons of discrepancies. Studies using the data from the web-based

information system are likely to have fewer abstraction errors than studies from phone-based

prior approval programs.

There are several potential limitations in our study. First, only one hospital was

involved in our study, and the Johns Hopkins Children’s Center had already had a very

successful web-based prior-request system. These factors could limit the study’s

generalizability to other institutions. In addition, we excluded ICU, CF, oncology and ID consult

patients from our intervention although these patients might receive the most restricted

88

antimicrobials. These special populations are especially vulnerable to infections and the

empiric therapy for them may tend to be broader spectrum antimicrobials; 40 also, some

institutional algorithms (such as in oncology or CF patients, antibiotic cycling in NICU) might

preclude meaningful ASP intervention.

Only restricted antimicrobials were reviewed for the purposes of our study, so we could

have missed inappropriate use of unrestricted antibiotics. Also, a single review was performed

for each patient in our study, and a convenience sample of cases was used. We therefore cannot

compare the intervention rate to other studies because we did not systematically review the

cases; however, our approach might reflect the reality of non-study situations since even the

most established ASP cannot guarantee intervening in every possible case.74

For aim 3, the medical record may have been incomplete, leading to misclassification of

the accuracy of the submitted request. However, we did not count inadequate provision of

patient’s data as an inaccuracy. Therefore, it likely biased toward recognizing fewer

inaccuracies if misclassification occurred. 78 Besides, using more objective definitions of

inaccuracies in our study might decrease the potential of misclassifications.

We also did not check the accuracy of requests throughout an entire academic year

because of time constraints. Such a study might be useful to determine whether housestaff

experience makes a difference in the proportion of inaccurate requests.

4.3. Recommendations for Future Study

Future studies might incorporate new laboratory technologies to enhance the ASP

program. For example, it might be possible to initiate appropriate antimicrobial therapy earlier

if the ASP incorporates new technology such as MALDI-TOF for rapid species-level

89

identification of pathogens.121 Use of some biomarkers such as procalcitonin could guide the

initiation and discontinuation of antibiotics.118

Multicenter collaborative studies could increase statistical power and generalizability.

However, comparisons across centers might also have some limitations such as different ASP

structure, team personnel, list of restricted drugs, etc. Risk adjustment by focusing on specific

inpatient populations and the addition of disease severity indexes could allow comparisons of

antimicrobial use across different settings.40 Similarly, more focused interventions such as

decreasing the duration of therapy in specific infections may make it more likely to observe an

effect, 40 such as studying minimal acceptable duration of therapy in community-acquired

pneumonia (CAP) or urinary tract infection (UTI). In addition, to understand the potential

influence of inaccurate communication upon the PIDF’s approval, future studies of the

associations between inaccuracies of requests and clinical outcomes might be helpful.

4.4. Conclusions

We demonstrated that a post-prescription review program could successfully decrease

the number of inappropriate antimicrobial courses at our institution. These findings might

encourage other pediatric centers to pursue similar post-prescription review programs.

Although inaccurate information occurred not very frequently among all web-based

pediatric prior approval requests, we believe that almost half of them could potentially

influence pediatric ID fellows’ decision-making. While it is not practical for a pediatric ID fellow

to check the accuracy of each request, targeted review of requests for specific antimicrobials, or

for specific patient populations is warranted.

90

Table 4.1 Estimated sample size in each group in ascending order if significant reductions of

antibiotic use are to be reached by using the results of this study: power 80%, alpha 0.05 and 2-

sided test of significance

From the results of some outcome measures

Estimated sample size in each group

Significant difference in our study ( Y/N )*

Proportion of inappropriate antimicrobial course at Day 2 after post-prescription review

26 Yes

Median duration (days) of restricted antibiotic agents per episode of infection

83 No

Median duration of total antibiotic agents per episode of infection

208 No

Combined restricted antimicrobial use (days) per episode of infection

230 No

Inappropriate restricted antimicrobial use ( DOTs) after dropping some cases with long hospitalizations

271 No

Inappropriate restricted antimicrobial use ( DOTs)

279 No

Restricted antimicrobial use (DOTs) after dropping some cases with long hospitalizations

2442 No

Restricted antimicrobial use (DOTs) 26447 No Total antimicrobial use (DOTs) after dropping some cases with long hospitalizations

325770 No

Total antimicrobial use (DOTs) Not estimated because higher rank sums (more antibiotic use ) in intervention group

No

Combined total antimicrobial use (days) per episode of infection

Not estimated because higher rank sums (more antibiotic use ) in intervention group

No

* In our study: Intervention group: 30 patients, 32 antimicrobial courses. Control group: 30

patients, 33 antimicrobial courses

91

5. References:

5.1. Appendix: Figure 5.1 Sample data collection form for Aim 1 and Aim 2

92

Figure 5.2 Sample data collection form for Aim 3

93

5.2. Bibliography: 1 Fleming, A. On antibacterial action of culture of penicillium, with special reference to their use in

isolation of B. influenzae. Br J Exp Pathol 1929; 10, 226–236

2 Lowy FD. Antimicrobial resistance: the example of Staphylococcus aureus. J Clin Invest

2003;111:1265-1273

3 Shlaes DM, Gerding DN and John JF Jr.et al. Society for Healthcare Epidemiology of America and

Infectious Diseases Society of America Joint Committee on the Prevention of Antimicrobial

Resistance: Guidelines for the Prevention of Antimicrobial Resistance in Hospitals. Infect Control

Hosp Epidemiol 1997;18(4):275-291

4 Spellberg B, Powers JH, Brass EP et al.Trends in antimicrobial drug development: implications for

the future. Clin Infect Dis 2004;38:1279-1286

5 Boucher HW, Talbot GH, Benjamin DK et al. 10 × '20 Progress—Development of New Drugs Active

Against Gram-Negative Bacilli: An Update From the Infectious Diseases Society of America. Clin

Infect Dis 2013, 56 (12): 1685-1694

6 Mera RM, Suaya JA, Amrine-Madsen H et al. Increasing role of Staphylococcus aureus and

community-acquired methicillin-resistant Staphylococcus aureus infections in the United States: a

10-year trend of replacement and expansion. Microb Drug Resist 2011;17(2):321-8

7 Klein E, Smith DL, Laxminarayan R. Community-associated methicillin-resistant Staphylococcus

aureus in outpatients, United States, 1999-2006. Emerg Infect Dis 2009 ;15(12):1925-30

8 Antri K, Rouzic N, Dauwalder O et al. High prevalence of MRSA clone ST80-IV in the hospital and

community settings in Algiers. Clin Microbiol Infect 2011;17(4): 526-32

9 Hsueh PR, Liu CY, Luh KT. Current status of antimicrobial resistance in Taiwan. Emerg Infect Dis

2002; 8:132-7.

10 Huang YC, Chen CJ. Community-associated meticillin-resistant Staphylococcus aureus in children

in Taiwan, 2000s. Int J Antimicrob Agents 2011;38(1):2-8.

11 Ho PL, Chow KH, Lo PY et al. Changes in the epidemiology of methicillin-resistant

Staphylococcus aureus associated with spread of the ST45 lineage in Hong Kong. Diagn Microbiol

Infect Dis 2009;64: 131–137

94

12 Sievert DM, Ricks P, Edwards JR et al. Antimicrobial-resistant pathogens associated with

healthcare-associated infections: summary of data reported to the National Healthcare Safety

Network at the Centers for Disease Control and Prevention, 2009-2010.Infect Control Hosp

Epidemiol. 2013 Jan;34(1):1-14

13 Wang JT, Chang SC, Wang HY et al. High rates of multidrug resistance in Enterococcus faecalis and

E. faecium isolated from inpatients and outpatients in Taiwan. Diagn Microbiol Infect Dis

2013,75(4):406-11

14 Arias CA, Contreras GA, Murray BE. Management of multidrug-resistant enterococcal infections.

Clin Microbiol Infect 2010;16:555-562

15 Peleg AY,Hooper DC. Hospital-acquired infections due to gram-negative bacteria. N Engl J Med

2010;362(19):1804-13.

16 Hsueh PR, Teng LJ, Chen CY, et al. Pandrug-resistant Acinetobacter baumannii causing

nosocomial infections in a university hospital, Taiwan. Emerg Infect Dis 2002; 8:827-832

17 CDC. Vital signs: carbapenem-resistant Enterobacteriaceae. Morb Mortal Wkly Rep

2013 ;62(9):165-70

18 Detection of Enterobacteriaceae isolates carrying metallo-betalactamase: United States, 2010.

Morb Mortal Wkly Rep 2010; 59:750

19 Neidell MJ, Cohen B, Furuya Y et al. Costs of Healthcare- and Community-Associated Infections

With Antimicrobial-Resistant Versus Antimicrobial-Susceptible Organisms. Clin Infect Dis 2012 ;

55(6): 807–815

20 Vanderkooi OG, Low DE, Green K et al. Predicting antimicrobial resistance in invasive

pneumococcal infections. Clin Infect Dis 2005;40(9):1288-97.

21 Roberts RR, Hota B, Ahmad I et al. Hospital and societal costs of antimicrobial-resistant infections

in a Chicago teaching hospital: implications for antibiotic stewardship. Clin Infect Dis

2009;49(8):1175-84.

22 Conus P, Francioli P. Relationship between ceftriaxone use and resistance of Enterobacter species.

J Clin Pharm Ther 1992;17(5):303-05

23 Polk RE, Johnson CK, McClish D et al. Predicting hospital rates of fluoroquinolone-resistant

Pseudomonas aeruginosa from fluoroquinolone use in US hospitals and their surrounding

communities. Clin Infect Dis 2004;39(4):497-503

95

24 Marchaim D, Chopra T, Bhargava A et al. Recent Exposure to Antimicrobials and Carbapenem-

Resistant Enterobacteriaceae: The Role of Antimicrobial Stewardship. Infect Control Hosp

Epidemiol 2012;33(8):817-830

25 McKinnell JA, Kunz DF, Chamot E et al. Association between Vancomycin-Resistant Enterococci

Bacteremia and Ceftriaxone Usage. Infect Control Hosp Epidemiol 2012;33(7):718-724

26 McDonald LC, Owings M, Jernigan DB. Clostridium difficile infection in patients discharged from

US short-stay hospitals, 1996-2003. Emerg Infect Dis Mar 2006;12(3):409-15.

27 Eaton SR, Mazuski JE. Overview of Severe Clostridium difficile Infection. Crit Care Clin 2013,

29(4):827-39

28 Bignardi GE. Risk factors for Clostridium difficile infection. J Hosp Infect 1998; 40:1–15.

29 Valiquette L, Cossette B, Garant M et al. Impact of a Reduction in the Use of High-Risk Antibiotics

on the Course of an Epidemic of Clostridium difficile–Associated Disease Caused by the

Hypervirulent NAP1/027 Strain. Clin Infect Dis 2007; 45:S112–21

30 Carling P, Fung T, Killion A et al. Favorable impact of a multidisciplinary antibiotic management

program conducted during 7 years. Infect Control Hosp Epidemiol 2003; 24:699–706

31 Debast SB, Vaessen N, Choudry A et al. Successful combat of an outbreak due to Clostridium

difficile PCR ribotype 027 and recognition of specific risk factors. Clin Microbiol Infect 2009; 15:

427–434

32 Erbay A, Colpan A, Bodur H et al. Evaluation of antibiotic use in a hospital with an antibiotic

restriction policy. Int J Antimicrob Agents 2003;21:308–12.

33 Gonzales R, Malone DC, Maselli JH et al. Excessive antibiotic use for acute respiratory infections in

the United States. Clin Infect Dis 2001;33:757–62

34 Kumarasamy Y, Cadwgan T, Gillanders IA et al. Optimizing antibiotic therapy—the Aberdeen

experience. Clin Microbiol Infect 2003;9:406-11.

35 Glowacki RC, Schwartz DN,Itokazu GS et al. Antibiotic Combinations with Redundant

Antimicrobial Spectra: Clinical Epidemiology and Pilot Intervention of Computer-Assisted

Surveillance. Clin Infect Dis 2003; 37:59–64

36 Smith RD, Coast J. Antimicrobial resistance: a global response. Bull World Health Organ

2002;80:126-33

96

37 Dellit TH, Owens RC, McGowan JE Jr, et al. Infectious Diseases Society of America and the Society

for Healthcare Epidemiology of America guidelines for developing an institutional program to

enhance antimicrobial stewardship. Clin Infect Dis 2007; 44:159–177.

38 CDC. Get Smart for Healthcare Campaign. http://www.cdc.gov/getsmart/healthcare/. Accessed 8

October 2013

39

Fishman N. Antimicrobial Stewardship. Am J Infect Control 2006;34(5 Suppl 1):S55-63

40 Newland JG, Banerjee R, Gerber JS et al. Antimicrobial Stewardship in Pediatric Care: Strategies

and Future Directions. Pharmacotherapy 2012;32(8):735–743

41

Tamma PD, Cosgrove SE. Antimicrobial stewardship. Infect Dis Clin North Am 2011;25:245–260

42 Belongia EA, Knobloch MJ, Kieke BA, et al. Impact of statewide program to promote appropriate

antimicrobial drug use. Emerg Infect Dis 2005;11(6):912–20.

43 Dubrovskaya Y, Papadopoulos J, Scipione MR et al. Antibiotic Stewardship for Intra-abdominal

Infections: Early Impact on Antimicrobial Use and Patient Outcomes. Infect Control Hosp Epidemiol

2012; 33(4): 427-429

44 Shojania, KG, Yokoe D, Platt R et al. Reducing vancomycin use utilizing a computer guideline: results

of a randomized controlled trial. J Am Med Inform Assoc 1998; 5:554–562

45 MacDougall C, Polk ER. Antimicrobial Stewardship Programs in Health Care Systems. Clin Microbiol

Rev 2005;18:638–656

46 Mullett CJ, Evans RS, Cristenson JC et al. Development and impact of a computerized pediatric

antiinfective decision support system. Pediatrics 2001; 108:e75

47 Chin T. Doctors pull plug on paperless system. Am Med News 2003; 46:5–8.

48 Johannsson B, Beekmann SE, Srinivasan A et al. Improving antimicrobial stewardship: the

evolution of programmatic strategies and barriers. Infect Control Hosp Epidemiol 2011;32(4):367–

374

49 Sunenshine RH, Liedtke LA, Jernigan DB et al. Role of infectious diseases consultants in

management of antimicrobial use in hospitals. Clin Infect Dis 2004;38:934–938.

50 Trivedi KK, Rosenberg J. The State of Antimicrobial Stewardship Programs in California. Infect

Control Hosp Epidemiol 2013;34(4):379-384

97

51 Hersh AL, Beekmann SE, Polgreen PM et al.Antimicrobial stewardship programs in pediatrics.

Infect Control Hosp Epidemiol 2009 ;30(12):1211-7.

52 Ashiru-Oredope D, Sharland M, Charani E et al. Improving the quality of antibiotic prescribing in

the NHS by developing a new Antimicrobial Stewardship Programme: Start Smart—Then Focus. J

Antimicrob Chemother 2012; 67 Suppl 1: i51–i63

53 Molstad S, Erntell M, Hanberger H et al. Sustained reduction of antibiotic use and low bacterial

resistance: 10-year follow-up of the Swedish Strama programme. Lancet Infect Dis 2008; 8: 125–32

54 Chang MT, Wu TH, Wang CY et al. The Impact of an intensive antimicrobial control program in a

Taiwanese medical center. Pharm World Sci 2006;28(4):257-64

55 Cheng VCC, To KKW, Li IWS et al. Antimicrobial Stewardship Program Directed at Broad-

Spectrum Intravenous Antibiotics Prescription in a Tertiary Hospital. Eur J Clin Microbiol Infect Dis

2009;28:1447-56

56 Ghafur A, Nagvekar V, Thilakavathy S et al. “Save antibiotics, save lives”:an indian success story

of infection control through persuasive diplomacy. Antimicrob Resist Infect Control 2012, 1:29

57 Calrns KA, Jenney AW, Abbott IJ et al. Prescribing trends before and after implementation of an

antimicrobial stewardship program. MJA 2013; 198: 262–266

58 Srinivasan A, Fishman N. Antimicrobial stewardship 2012: science driving practice. Infect Control

Hosp Epidemiol 2012 ;33(4):319-21.

59 Avdic E, Cushinotto L, Hughes AH et al. Impact of an Antimicrobial Stewardship Intervention on

Shortening the Duration of Therapy for Community-Acquired Pneumonia. Clin Infect Dis

2012 ;54(11):1581-7

60 Willemsen I, Groenhuijzen A, Bogaers D et al. Appropriateness of antimicrobial therapy measured

by repeated prevalence surveys. Antimicrob Agents Chemother 2007;51:864–867

61 Kaki R, Elligsen M, Walker S et al. Impact of antimicrobial stewardship in critical care: a

systematic review. J Antimicrob Chemother 2011;66:1223–1230

62 Paul M, Andreassen S, Tacconelli E et al. Improving empirical antibiotic treatment using TREAT, a

computerized decision support system: cluster randomized trial. J Antimicrob Chemother

2006;58:1238–1245

98

63 Bantar C, Sartori B, Vesco E et al. A hospitalwide intervention program to optimize the quality of

antibiotic use: impact on prescribing practice, antibiotic consumption, cost savings, and bacterial

resistance. Clin Infect Dis 2003;37:180-6

64 White AC Jr, Atmar RL, Wilson J et al.Effects of requiring prior authorization for selected

antimicrobials: expenditures, susceptibilities, and clinical outcomes. Clin Infect Dis 1997;25:230-9.

65 McGowan JE Jr, Gerding DN. Does antibiotic restriction prevent resistance? New Horiz

1996;4:370–376.

66 Lipworth AD, Hyle EP, Fishman NO et al. Limiting the emergence of extended-spectrum Beta-

lactamase-producing enterobacteriaceae: influence of patient population characteristics on the

response to

antimicrobial formulary interventions. Infect Control Hosp Epidemiol 2006; 27:279–286

67 McGowan JE. Antimicrobial stewardship--the state of the art in 2011: focus on outcome and

methods. Infect Control Hosp Epidemiol 2012; 33:331-7

68 Standiford HC, Chan S, Tripoli M. Antimicrobial Stewardship at a Large Tertiary Care Academic

Medical Center: Cost Analysis Before, During, and After a 7-Year Program. Infect Control Hosp

Epidemiol 2012;33(4):338-345

69 Beardsley JR, Williamson JC, Johnson JW et al. Show Me the Money: Long-Term Financial Impact

of an Antimicrobial Stewardship Program. Infect Control Hosp Epidemiol 2012;33(4):398-400

70 McGowan JE Jr, Finland M. Usage of antibiotics in a general hospital: effect of requiring

justification. J Infect Dis 1974;130:165-8.

71 Reed EE, Stevenson KB, West JE et al. Impact of formulary restriction with prior authorization by

an antimicrobial stewardship program. Virulence 2013; 4(2): 158–162

72 Martin C, Ofotokun I, Rapp R, et al. Results of an antimicrobial control program at a university

hospital. Am J Health Syst Pharm 2005;62(7):732-38

73 Pakyz AL, Oinonen M, Polk RE. Relationship of carbapenem restriction in 22 university teaching

hospitals to carbapenem use and carbapenem-resistant Pseudomonas aeruginosa. Antimicrob

Agents Chemother 2009 ;53(5):1983-6

74 Cosgrove SE, Seo SK, Bolon MK et al. Evaluation of postprescription review and feedback as a

method of promoting rational antimicrobial use: A multicenter intervention. Infect Control Hosp

Epidemiol 2012;33(4):374-380

99

75 Burke JP. Antibiotic resistance--squeezing the balloon? JAMA 1998; 280:1270-1

76 Seemungal IA, Bruno CJ. Attitudes of Housestaff toward a Prior-Authorization-Based Antibiotic

Stewardship Program. Infect Control Hosp Epidemiol 2012; 33(4):429-431

77 Calfee DP, Brooks J, Zirk NM et al. A pseudo-outbreak of nosocomial infections associated with the

introduction of an antibiotic management programme. J Hosp Infect 2003;55: 26–32.

78 Linkin DR, Paris S, Fishman NO et al. Inaccurate communications in telephone calls to an

antimicrobial stewardship program. Infect Control Hosp Epidemiol 2006;27:688–694.

79 LaRosa LA, Fishman NO, Lautenbach E et al. Evaluation of antimicrobial therapy orders

circumventing an antimicrobial stewardship program: investigating the strategy of “stealth dosing”.

Infect Control Hosp Epidemiol 2007;28(5): 551-556.

80 Linkin DR, Fishman NO, Landis R et al. Effect of communication errors during calls to an

antimicrobial stewardship program. Infect Control Hosp Epidemiol 2007;28:1374-1381.

81 Cosgrove SE, Patel A, Song X et al. Impact of different methods of feedback to clinicians after

postprescription antimicrobial review based on the Centers For Disease Control and Prevention's

12 Steps to Prevent Antimicrobial Resistance Among Hospitalized Adults. Infect Control Hosp

Epidemiol 2007; 28:641-646

82 Cotton CM, Taylor S, Stoll B et al. Prolonged Duration of Initial Empirical Antibiotic

Treatment Is Associated With Increased Rates of Necrotizing Enterocolitis and Death for Extremely

Low Birth Weight Infants. Pediatrics 2009;123:58–66

83 Solomon, D H, Houten VL,Glynn RJ et al. Academic detailing to improve use of broad spectrum

antibiotics at an academic medical center. Arch Intern Med 2001;161:1897–1902

84 Camins BC, King MD, Wells JB et al. Impact of an antimicrobial utilization program on

antimicrobial use at a large teaching hospital: a randomized controlled trial. Infect Control Hosp

Epidemiol 2009;30:931–8.

85 Toth NR,Chambers RM, Davis SL. Implementation of a care bundle for antimicrobial stewardship.

Am J Health-Syst Pharm 2010; 67:746-9

86 Gross R, Morgan AS, Kinky DE et al. Impact of a hospital-based antimicrobial management

program on clinical and economic outcomes. Clin Infect Dis 2001; 33:289–95

100

87 Elligsen M, Walker SAN, Pinto R, et al. Audit and feedback to reduce broad-spectrum antibiotic

use among intensive care unit patients: a controlled interrupted time series analysis. Infect Control

Hosp Epidemiol 2012;33(4):354–361

88 Pate PG, Storey DF, Baum DL. Implementation of an Antimicrobial Stewardship Program at a 60-

Bed Long-Term Acute Care. Hospital. Infect Control Hosp Epidemiol 2012;33(4):405-408

89 Jump RLP, Olds DM, Seifi N et al. Effective Antimicrobial Stewardship in a Long-Term Care Facility

through an Infectious Disease Consultation Service: Keeping a LID on Antibiotic Use. Infect Control

Hosp Epidemiol 2012;33(12):1185-1192

90 LaRocco A Jr. Concurrent antibiotic review programs—a role for infectious diseases specialists at

small community hospitals. Clin Infect Dis 2003; 37:742–3.

91 Gerber JS, Prasad PA, Fiks AG et al. Effect of an Outpatient Antimicrobial Stewardship

Intervention on Broad-Spectrum Antibiotic Prescribing by Primary Care Pediatricians. JAMA

2013;309(22):2345-2352

92 Cursio D. Antibiotic Stewardship: The “Real World” When Resources Are Limited. Infect control

Hospital Epidemiol 2010; 31(6): 666-668.

93 Patel SJ, Larson EL, Kubin CJ et al. A review of antimicrobial control strategies in hospitalized and

ambulatory pediatric populations. Pediatr Infect Dis J 2007 ;26(6):531-7.

94 Keren R, Chan E. A meta-analysis of randomized, controlled trials comparing short- and long-

course antibiotic therapy for urinary tract infections in children. Pediatrics 2002; 109:E70

95 Hall CB. Respiratory syncytial virus and parainfluenza virus. N Engl J Med 2001;344:1917-1928.

96 Bradley JS, Byington CL, Shah SS et al. Executive summary: the management of community-

acquired pneumonia in infants and children older than 3 months of age: clinical practice guidelines

by the Pediatric Infectious Diseases Society and the Infectious Diseases Society of America. Clin

Infect Dis 2011; 53:617–30.

97 Di Pentima C, Chan S, Eppes SC et al. Antimicrobial Prescription Errors in Hospitalized Children:

Role of Antimicrobial Stewardship Program in Detection and Intervention. Clinical Pediatrics

2009;48:505-512.

98 Gerber JS, Newland JG, Coffin SE et al. Variability in antibiotic use at Children’s Hospitals.

Pediatrics 2010;126:1067–73

101

99 Pakyz AL, Gurgle HE, Ibrahim OM et al. Trends in antibacterial use in hospitalized pediatric

patients in United States academic health centers. Infect Control Hosp Epidemiol 2009;30:600–3.

100 Grohskopf LA, Huskins WC, Sinkowitz-Cochran RL et al. Use of antimicrobial agents in United

States neonatal and pediatric intensive care patients. Pediatr Infect Dis J 2005;24:766–73

101 Hersh AL, Shapiro DJ, Pavia AT et al. Antibiotic prescribing in ambulatory pediatrics in the

United States. Pediatrics 2011;128(6):1053-1061

102 Metjian TA, Prasad PA, Kogon A et al. Evaluation of an antimicrobial stewardship program at a

pediatric teaching hospital. Pediatr Infect Dis J 2008 ;27(2):106-11.

103 Patel SJ, Oshodi A, Prasad P et al. Antibiotic use in neonatal intensive care units and adherence

with Centers for Disease Control and Prevention 12 Step Campaign to Prevent Antimicrobial

Resistance. Pediatr Infect Dis J 2009 ;28(12):1047-51.

104 Levy ER, Swami S, Dubois SG. Rates and Appropriateness of Antimicrobial Prescribing at an

Academic Children’s Hospital, 2007–2010. Infect Control Hosp Epidemiol 2012; 33(4): 346-353

105 Talpaert MJ, Gopal Rao G, Cooper BS et al. Impact of guidelines and enhanced antibiotic

stewardship on reducing broad-spectrum antibiotic usage and its effect on incidence of Clostridium

difficile infection. J Antimicrob Chemother 2011;66:2168–74.

106 Di Pentima MC, Chan S, Hossain J. Benefits of a pediatric antimicrobial stewardship program at a

Children’s Hospital. Pediatrics 2011;128:1062–70

107 Agwu AL, Lee CKK, Jain SK et al. A World Wide Web-based antimicrobial stewardship program

improves efficiency, communication, and user satisfaction and reduces cost in a tertiary care

pediatric medical center. Clin Infect Dis 2008;47:747-753.

108 Ho, M, Hsiung, CA, Yu, HT et al. Antimicrobial usage in ambulatory patients with respiratory

infections in Taiwan. J Formos Med Assoc 2004; 103:96-103

109 Huang YT, Hsueh PR. Antimicrobial drug resistance in Taiwan. Int J Antimicrob Agents 2008;32

Suppl 3:S174-8

110 Owen RC, Shorr AF, Deschambeault AL. Antimicrobial stewardship: Shepherding precious

resources. Am J Health-Syst Pharm 2009; 66(Suppl 4):S15-22

102

111 Lin MY, Bonten MJM. The Dilemma of Assessment Bias in Infection Control Research. Clin Infect

Dis 2012;54(9):1342–7

112 McDonnell Norms Group. Antibiotic overuse: the influence of social norms. J Am Coll Surg. 2008

Aug;207(2):265-75.

113 Valcourt K, Norozian F, Lee H et al. Drug use density in critically ill children and newborns:

Analysis of various methodologies. Pediatr Crit Care Med 2009; 10:495– 499

114 Polk RE, Fox C, Mahoney A et al. Measurement of Adult Antibacterial Drug Use in 130 US

Hospitals: Comparison of Defined Daily Dose and Days of Therapy. Clin Infect Dis 2007; 44:664–70

115 Noether GE. Sample size determination for some common nonparametric tests. J Am Stat Assoc

1987;82:645–7

116 Charani E, Edwards R, Sevdalis N et al. Behavior Change Strategies to Influence Antimicrobial

Prescribing in Acute Care: A Systematic Review. Clin InfectvDis 2011;53(7):651–662

117 McDonnell Norms Group. Antibiotic overuse: the influence of social norms. J Am Coll Surg. 2008

Aug;207(2):265-75.

118 Agarwal R, Schwartz DN. Procalcitonin to guide duration of antimicrobial therapy in intensive

care units: a systematic review.Clin Infect Dis. 2011 Aug;53(4):379-87

119 Bradley JS, Byington CL, Shah SS et al. Executive summary: the management of community-

acquired pneumonia in infants and children older than 3 months of age: clinical practice guidelines

by the Pediatric Infectious Diseases Society and the Infectious Diseases Society of America.Clin

Infect Dis. 2011 Oct;53(7):617-30

120 Ohl CA, Dodds Ashley ES. Antimicrobial stewardship programs in community hospitals: the

evidence base and case studies. Clin Infect Dis 2011;53(suppl 1):S23–S28.

121 Tamma PD, Tan K, Nussenblatt VR et al. Can matrix-assisted laser desorption ionization time-of-

flight mass spectrometry (MALDI-TOF) enhance antimicrobial stewardship efforts in the acute care

setting? Infect Control Hosp Epidemiol 2013 ;34(9):990-5

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Curriculum Vita

Chou-Cheng Lai Birthday: March 28th, 1969

Birth place:Taiwan lai6700@gmail.com

Education

NATIONAL YANG MING UNIVERSITY | Taipei, Taiwan 9/1988-6/1995 MD Program HARVARD UNIVERSITY | Boston, U.S. 9/2006-6/2007

MS, Infectious disease, Epidemiology Department JOHNS HOPKINS UNIVERSITY | Baltimore, U.S. 9/2007-present PhD Candidate, GDEC Program, International Health Department Professional Experience

JOHNS HOPKINS UNIVERSITY 5/2011-present

Thesis research: Evaluation of a Pediatric Antimicrobial Stewardship Program in a Tertiary Care Medical Center. Adding an intervention to evaluate its impact on reduction of inappropriate antimicrobial use and improvement of patient care. Advisor: Dr. Ruth Karron. PI: Dr. Sara Cosgrove

JOHNS HOPKINS UNIVERSITY 7/2012-1/2013

Research Assistant, Medical Intervention for Primary Open Angle Glaucoma Network Meta-analysis

PI: Dr. Tianjin Li JOHNS HOPKINS UNIVERSITY 12/2009-6/2012

Research Assistant, AGEDD Pneumococcal and Meningococcal diseases burden study

PI: Dr. Hope Johnson PEDIATRIC CENTER, CHU-DONG, TAIWAN 8/2003-6/2006

Attending Pediatrician VETERAN GENERAL HOSPITAL, TAIPEI, TAIWAN 8/1997-7/2003

Attending physician, Chief Resident, Resident physician

Fellowship Training: Pediatric allergy and immunology, Pediatric infectious diseases R.O.C. MILITARY 9/1995-6/1997

Medical Officer Publications:

Lai CC, Chen SJ, Tang RB, Huang B, Tsou KY. Sepsis in the Very Low Birth Weight Infants in Taiwan. Clinical Neonatology 2001;8(1):1-5

104

Tang RB, Chao T, Chen SJ, Lai CC. Pulmonary Function During Exercise in Obese Children. Chinese Medical Journal (Taipei) 2001;64:403-407

Lai CC, Tai HY, Shen HD, Chung WT, Chung RL, Tang RB. Elevated Levels of Soluble Adhesion Molecules in Sera of Patients with Acute Bronchiolitis. J Microbiol Immunol Infect 2004;37(3):153-6

Garcia CR, Johnson HL, Summers A, Wang X, Lai CC, Pongpirul K, Levine OS, Deloria-Knoll M, O’Brien KL. Pneumococcal Disease in Older Children and Adults Globally: Results from the AGEDD Project. Presented at: 8th International Symposium on Pneumococcal Diseases; March 11-15, 2012; Iguacu Falls, Brazil

Certifications and Award

Pediatric Allergy and Immunology Board Certification

Pediatric Board Certification

Government Scholarship for Studying Abroad, Taiwan

Professor Laura C.C. Meng Scholarship