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/
10
00
pt-
da
ys
Control Intervention
Restricted antibiotic use (DOTs) between two arms
0
1,0
00
2,0
00
3,0
00
4,0
00
DO
Ts/
10
00
pt-
da
ys
Control Intervention
Total antibiotic use (DOTs) between two arms
Without outliers
0
500
1,0
00
1,5
00
2,0
00
2,5
00
DO
Ts/
10
00
pt-
da
ys
Control Intervention
Restricted antibiotic use (DOTs) between two arms
Without outliers
0
1,0
00
2,0
00
3,0
00
4,0
00
DO
Ts/
10
00
pt-
da
ys
Control Intervention
Total antibiotic use (DOTs) between two arms
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/
10
00
pt-
da
ys
Control Intervention
Inappropriate restricted antibiotic use (DOTs) between two arms Without outliers
0
500
1,0
00
1,5
00
2,0
00
DO
Ts/
10
00
pt-
da
ys
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
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Curriculum Vita
Chou-Cheng Lai Birthday: March 28th, 1969
Birth place:Taiwan [email protected]
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
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