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© Managed Care &Healthcare Communications, LLC

A recent study analyzing a large cross-sectional data set from the National Surgical Quality Improvement Project found that postsurgical complications—many of them acute medical con-

ditions—occur at similar rates among hospitals regardless of inpatient mortality rates.1 This finding suggests that the best hospitals distinguish themselves less by preventing complications than by their apparent ability to rescue patients who have suffered complications during hospitalization. This and other studies2-4 support the view that many adverse events in sur-gical patients are due to problems in medical management rather than in-traoperative care, and suggest the possibility of reducing adverse outcomes in this population by improving care outside of the operating room.

Medical comanagement has been adopted by many surgeons as a poten-tial method to improve the quality of postoperative care.5 Comanagement involves a medical generalist or specialist who manages patient issues that arise on the wards, allowing surgeons to dedicate their time to their specialty-specific activities. Comanagement can be understood as a patient referral wherein the comanaging physician participates in hospital care broadly, in contrast to traditional consultations directed toward addressing particu-lar problems.6 This practice arrangement has rapidly been adopted over the last decade, such that about half of all surgical hospitalizations in the Medicare population are now comanaged.5 Although recent studies have shown that focused preoperative consultations by medicine physicians do little to prevent postprocedural complications and mortality,7,8 the effects of postoperative comanagement on clinical outcomes remain uncertain.9,10

To understand the impact of postoperative comanagement on the care of hospitalized surgical patients, we analyzed data from our aca-demic hospital’s electronic medical records. We hypothesized that co-management of hospitalized surgical patients would have a protective effect on risk-adjusted mortality through the rescue phenomenon sug-gested by the National Surgical Quality Improvement Project data,1 as well as by other studies.11-13 To this end, we examined comanagement’s associations with inpatient death among patients who suffered postsur-gical complications. We also assessed the impact of comanagement on risk-adjusted length of stay and hospital cost.

METHODSSettingWe conducted our study at an

urban 854-bed teaching hospital.

Potential Role of Comanagement in “Rescue” of Surgical Patients

Keiki Hinami, MD, MS; Joe Feinglass, PhD; Darlene E. Ferranti, BS;

and Mark V. Williams, MD

Objectives: To evaluate the effect of medical comanagement on outcomes of hospitalized surgical patients who had postoperative complications.

Study Design: Retrospective cohort study.

Methods: We used clinical and administrative data at a large urban hospital to conduct a cohort study of select surgical hospitalizations in 2008 and 2009. We identified patients who suffered postoperative complications using measures developed by the University Health System Consortium. Bivariate and multivariate regression analyses were used to determine the associations of postoperative comanagement with inpatient mortality, length of stay, and cost in surgical pa­tients who had postoperative complications.

Results: From 21,728 total surgical hospitaliza­tions, we identified 4040 hospitalizations involving primary procedures (mainly orthopedic and neurosurgical) that were associated with comanagement at least 25% of the time. After excluding cases with missing data, 501 hospital­izations (13.8%) involved a patient who suffered at least 1 postoperative complication. Patient characteristics between the comanaged (n = 297) and non­comanaged (n = 204) hospitalizations were well matched. Medical comanagement was associated with fewer in­hospital deaths (odds ratio 0.23, 95% confidence interval 0.05­0.99) in adjusted analysis. Comanaged compared with non­comanaged hospitalizations were associated with shorter stay (–2.6 days, P <.01) without sig­nificant differences in total cost.

Conclusions: Comanagement of patients who had perioperative complications was associated with lower mortality, suggesting that comanagement may facilitate effective rescue among medically complex surgical patients.

(Am J Manag Care. 2011;17(9):e333-e339)

For author information and disclosures, see end of text.

In this article Take­Away Points / e334 Published as a Web exclusive www.ajmc.com

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Surgeons on faculty belonged to 1 of 17 surgical specialties. Surgical services were staffed by an attending physician with house staff and medical students. Inpatient services of neu-rosurgery, urology, and orthopedic, transplant, and vascular surgeries were also staffed by nonphysician providers.

The perioperative medicine service was staffed by 7 gen-eral internists from the Division of Hospital Medicine who provided preoperative evaluation and postoperative coman-agement services. Comanagement was available 7 days a week during daytime hours. Infrequently, cross-covering night hos-pitalists performed new urgent consultations during evening hours with handoffs of care to the perioperative physicians in the morning. Some high-risk patients seen in the preoperative medical clinic, including all high-risk spine surgery patients, were comanaged postoperatively at the internists’ discretion. Surgeons also requested comanagement for additional pa-tients based on perceived need. Postoperative involvement by the perioperative physicians is referred to as comanagement in this study because of their exclusive focus on surgical pa-tients, their orientation toward improving postsurgical care quality, and the higher intensity of involvement in their pa-tients’ care reflected by the typically greater frequency of en-counters compared with other medical specialty consultants.

SubjectsAll inpatient adult surgical discharges following a proce-

dure in 2008 and 2009 were identified using the institution’s Enterprise Data Warehouse, which contains all data from the hospital’s electronic administrative and medical records. The use of comanagement varied by individual surgeons’ practice, which led to nonuniform adoption of comanagement in the hospital. We sought to reduce heterogeneity in the popula-tion by matching patients primarily on surgical procedures. Using International Classification of Disease, 9th Revision (ICD-9) procedure codes, we selected hospitalizations with primary procedures associated with comanagement at least 25% of the time. Patient demographic and clinical information such as the American Society of Anesthesiologists (ASA) physi-cal status score,14 ICD-9 data to calculate the Charlson Co-morbidity Score,15 and hospital cost data were obtained. We also obtained information about the total number of non-surgical consultants who were involved in each hospitaliza-

tion. Comanagement was defined as hospitalizations in which at least 1 perioperative internist documented an encounter during the postoperative course.

Postoperative complications for our patient population were identified by cross-linking to the University Health

System Consortium (UHC) database.16 Member institutions submit surgical discharge abstract information to the consor-tium, which then uses diagnosis-related groups and ICD-9 codes to identify rates of 24 specific surgical complications. The in-stitutional review board of Northwestern University approved the study protocols, consents, and data collection mechanisms.

AnalysisWe compared patient demographic and risk characteristics

using the c2 test and paired t test for categorical and continu-ous data, respectively. Postoperative complications were tabu-lated and differences in frequency by comanagement status were compared using the c2 test.

Bivariate analyses were performed to assess associations between inpatient death and potential risk factors. We fit a mixed-effects logistic regression model to determine the adjusted association between comanagement and inpatient mortality, allowing for clustering around individual surgical procedure. Unadjusted median costs and lengths of hospi-talization by comanagement status were compared using the Wilcoxon rank-sum test. Risk-adjusted comparisons were per-formed using generalized linear regression modeling.

RESULTSSelection of Patients Into the Study

The Figure shows a flow diagram of the study subjects. Of the 1048 primary procedures accounting for 21,728 surgical hospitalizations during the study period, 184 primary proce-dures were found to be associated with comanagement at least 25% of the time. Of 4040 hospitalizations, 420 before March 2008 were excluded because ASA scores were not electroni-cally documented. Of the remaining 3620 hospitalizations, 501 (13.8%) involved patients who had at least 1 postopera-tive complication and were included in the analysis.

Characteristics of Patients and Their Hospitalizations

Characteristics of patients and their hospitalizations are shown in Table 1. The 3 subgroups of admission source were am-bulatory recovery, referring to patients whose hospitalization fol-lowed surgery in an ambulatory setting; emergency department;

Take-Away PointsWe evaluated the effect of medical comanagement on outcomes of hospitalized surgical patients who had postoperative complications.

n Comanagement of patients who had perioperative complications was associated with lower mortality.

n The higher cost of comanaged hospitalizations mandates development of tools that allow for the cost­effective application of comanagement.

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source, the number of nonsurgical consultants, and the Charl-son Comorbidity Index (Table 3). Mixed-effects regression analysis with clustering around procedures found no statistical significance in the associations between inpatient death and emergency surgery status and admission source. Moreover, co-management was associated with lower risk-adjusted mortal-ity (odds ratio [OR] 0.23, P = .049) during the postoperative hospitalization.

Length of Stay and CostWhen using unadjusted comparisons, we found that both

median length of hospitalization and cost were higher in co-managed compared with non-comanaged hospitalizations (Table 4). Risk adjustment, however, yielded shorter mean length of hospitalization by 2.6 days (P <.01) among coman-aged compared with non-comanaged hospitalizations. The risk-adjusted cost of hospitalization was not different by co-management status.

DISCUSSIONIn this exploratory observational study, postoperative co-

management by a general internist focused on perioperative care was associated with lower risk-adjusted mortality among surgical patients who had a postoperative complication. Co-managed hospitalizations were associated with shorter risk-adjusted lengths of stay with no difference in risk-adjusted cost. Due to the traditionally low mortality rate in the surgical

and inpatient unit, referring to patients transferred from non-surgical services or from other hospitals. More comanaged than non-comanaged hospitalizations followed procedures performed in the ambulatory setting. Fewer procedures in the comanaged group were designated emergencies. The mean duration of sur-gery was significantly longer for comanaged hospitalizations.

An orthopedic procedure was the primary surgery in the largest number of hospitalizations (n = 280, 55.9%), while spine surgery (n = 128, 25.6%) and neurological surgery (n = 39, 7.8%) were next most commonly involved. The top 6 pro-cedures accounted for 87% of the hospitalizations in the study and included total knee replacement (ICD-9 code 84.54), spine fusion (ICD-9 codes 81.03-81.08, 81.34, 81.35), hip re-placement (ICD-9 codes 70, 72, 73, 81.51, 81.5), reduction of the femur (ICD-9 codes 79.15, 79.35), craniotomy (ICD-9 code 1.31), and radical cystectomy (ICD-9 code 57.71).

Table 2 lists the UHC postoperative complication mea-sures in the study’s hospitalizations in descending order of frequency. Deep vein thrombosis or pulmonary embolism was the most common complication (31.9%). Hospitalizations in-volving pneumonia and other infectious complications were more likely to be comanaged.

Association Between Comanagement and SurvivalBivariate analyses found significant positive associations

between inpatient mortality and the following variables in descending order of magnitude: ASA score greater than or equal to 4, emergency surgery status, inpatient unit admission

n Figure. Flow Diagram of Patients in the Study

21,728 Surgical hospitalizations in 2008-2009

4040 Hospitalizations involving 184 primary procedurescomanaged at least 25% of the time

3620 Hospitalizations with all data including the ASA score

501 Hospitalizations involving at least 1 complication

17,688 Hospitalizations involving 864 primary proceduresnot typically comanaged

420 Hospitalizations missing ASA scores (pre-March 2008)

3119 Hospitalizations without perioperative complications

ASA indicates American Society of Anesthesiologists.

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inpatient population, the effect of interventions to further lower mortality in this population is difficult to demonstrate. Although care quality interventions such as the implementa-tion of comanagement may not be expected to have a dramat-ic impact on mortality, our finding is intriguing and generates a new hypothesis around the failure-to-rescue phenomenon.

The focus of this study on the effects of postoperative co-management is a departure from several recent studies that examined the effects of preoperative consultations.7,8 Al-though results are not shown, our data confirm the absence of a mortality benefit with preoperative consultations, which suggests that preventing many postoperative complications is difficult. Our analysis, however, suggests that the widespread adoption of postoperative comanagement may find its ben-

efit in the role of hospitalists as facilitators of rescue. The ex-act nature of this contribution from comanaging hospitalists was not explored in this study, but may be related to their disease-specific experience in perioperative medicine,17 their availability to respond to emergencies on the wards similarly to rapid response teams,18 and surveillance for emerging signs of complications, as seen with skilled floor nurses and house staff.19 Moreover, hospitalists’ clinical focus on process im-provement activities may have salutary influences on hospital care quality that have been negatively associated with failure to rescue.20

The impact of hospitalist comanagement of a neurosurgi-cal service was explored recently by Auerbach and colleagues using a retrospective interrupted time-series analysis.9 Al-

n Table 1. Characteristics of Study Patients and Their Hospitalizationsa

Characteristic

Total (n = 501)

Not Comanaged (n = 204)

Comanaged (n = 297)

P

Age, y, mean (SD) 64.4 (15.0) 63.6 (17.2) 64.9 (13.3) .34

Male 216 (43.1%) 97 (47.6%) 119 (40.1%) .10

Race (nurse assigned)

Black 73 (15.4%) 28 (14.4%) 45 (16.1%)

Asian 5 (1.1%) 2 (1.0%) 3 (1.1%)

White 363 (76.6%) 150 (77.3%) 213 (76.1%)

Hispanic 21 (4.4%) 11 (5.7%) 10 (3.6%)

Other 12 (2.5%) 3 (1.6%) 9 (3.1%) .40

Payer

Medicaid 922 (4.4%) 9 (4.4%) 13 (4.4%)

Medicare 265 (52.9%) 104 (50.1%) 161 (54.2%)

Indemnity 183 (36.5%) 79 (38.7%) 104 (35.0%)

Other 31 (6.2%) 12 (5.9%) 19 (6.4%) .86

Admission source

Ambulatory recovery 346 (69.1%) 122 (59.8%) 224 (75.4%) <.01

Emergency department 83 (16.6%) 45 (22.1%) 38 (12.8%) <.01

Inpatient unit 68 (13.6%) 36 (17.6%) 32 (10.8%) .03

Charlson Comorbidity score, mean (SD) 1.3 (1.9) 1.4 (2.1) 1.3 (1.8) .32

ASA score >4 20 (4.0%) 12 (5.9%) 8 (2.7%) .07

Emergency surgery 34 (6.8%) 23 (11.3%) 11 (3.7%) <.01

OR time, min, mean (SD) 338.4 (208.0) 288 (165.9) 373.1 (226.4) <.01

No. of surgeries, mean (SD) 1.2 (0.5) 1.2 (0.6) 1.2 (0.5) .94

Transfer to ICU

Recovery room to ICU 55 (11.0%) 19 (9.3%) 36 (12.1%) .32

Floor unit to ICU 111 (22.2%) 43 (21.1%) 68 (22.9%) .63

No. of nonsurgical consults, mean (SD) 1.3 (1.1) 0.4 (0.7) 1.8 (1.0) <.01

Inpatient death 18 (3.6%) 8 (3.9%) 10 (3.4%) .74

ASA indicates American Society of Anesthesiologists; ICU, intensive-care unit; OR, operating room. aValues are number (%) unless otherwise specified. Significant P values are boldfaced.

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though the study by Auerbach et al did not detect a decrease in adjusted mortality with the implementation of comanage-ment, a careful comparison of patient characteristics found that comanaged patients in the study were older and had more medical problems than their non-comanaged counterparts. Comanaging hospitalists in this setting may be playing an important supportive role in facilitating care for an increas-ingly elderly and comorbid surgical population. Although the mortality benefit of comanagement remains hypothetical, fu-ture studies may focus on the impact of comanagement on the most vulnerable surgical patients.

Comanagement is not implemented without added cost. Comanagement of neurosurgical hospitalizations has been as-sociated with a small risk-adjusted cost savings.9 This savings was not replicated in our study, and the higher unadjusted cost may reflect extra testing and treatment administered by hos-

pitalists. Because comanagement benefits a relatively small group of at-risk patients, we see the need for clinical criteria that can guide its targeted implementation to the appropriate patient population.

A few observations are noteworthy from our regression analysis. First, our findings support a previously tested asser-tion that risk prediction scores derived from administrative data such as the Charlson Comorbidity Index are poorer pre- dictors of mortality than clinical instruments like the ASA score.21 Not surprisingly, the number of nonsurgical consul-tants involved in hospitalizations appeared to be a surrogate indicator of clinical complexity, but no other covariate was significantly associated with the outcome in adjusted analysis. We found that the association between comanagement and mortality became stronger and statistically significant only after adjusting for other covariates, particularly the number

n Table 2. Postoperative Complications and Their Association With Medical Comanagement

No. (%)

Postoperative Complication

Not Comanaged (n = 204)

Comanaged (n = 297)

P

DVT or PE (n = 160) 66 (32.4) 94 (31.7) .87

Other complications of procedure (n = 139) 53 (26.0) 86 (29.0) .47

Mechanical complication due to device or implant (n = 74) 27 (13.2) 47 (15.8) .42

Pulmonary compromise (n = 72) 25 (12.3) 47 (15.8) .26

Hemorrhage or hematoma (n = 43) 20 (9.8) 23 (7.7) .42

Complications of nervous system (n = 33) 10 (4.9) 23 (7.7) .21

Pneumonia (n = 29) 4 (2.0) 25 (8.4) <.01

Perforations or lacerations (n = 25) 9 (4.4) 16 (5.4) .62

Miscellaneous complications (n = 25) 9 (4.4) 16 (5.4) .62

Acute MI (n = 21) 6 (2.9) 15 (5.1) .25

Wound infection (n = 14) 8 (3.9) 6 (2.0) .21

Infections not pneumonia or wound (n = 13) 1 (0.5) 12 (4.0) .01

Cellulitis or decubitus ulcer (n = 10) 5 (2.5) 5 (1.7) .55

Urinary tract complications (n = 8) 6 (2.9) 2 (0.7) .05

Postoperative stroke (n = 8) 1 (0.5) 7 (2.4) .1

Aspiration pneumonia (n = 7) 1 (0.5) 6 (2.0) .15

GI hemorrhage or ulcer (n = 7) 4 (2.0) 3 (1.0) .37

Reopening surgical site (n = 6) 4 (2.0) 2 (0.7) .19

Sepsis (n = 3) 2 (0.9) 1 (0.3) .36

Complication related to anesthesia (n = 1) 1 (0.5) 0 .23

Postoperative coma or stupor (n = 1) 1 (0.5) 0 .23

Postoperative cardiac abnormality not acute MI (n = 0) 0 0 NA

Shock or cardiorespiratory arrest (n = 0) 0 0 NA

Metabolic derangement (n = 0) 0 0 NA

DVT indicates deep venous thrombosis; GI, gastrointestinal; MI, myocardial infarction; NA, not applicable; PE, pulmonary embolism.

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of nonsurgical consultants. We believe that a possible expla-nation involves selection bias resulting from some surgeons’ pattern of consulting multiple specialists (at the exclusion of comanaging hospitalists) for patients who manifest early signs of complications. An alternative explanation is that surveil-lance for complications is enhanced under comanagement compared with specialist consultations. Due to the inherent limitations of administrative data, we were unable to adjust for the timing and severity of complications, resulting in re-sidual confounding.

The study is limited in several other ways. The major limi-tation of this observational study is that comanagement was not randomly assigned. We selectively limited our analysis to a group of patients with similar characteristics. The exposure groups varied with respect to admission source, there were fewer emergency surgeries in the comanaged group, and the mean operating room time in the comanaged group was longer due to the preponderance of high-risk complex spine surgeries. Although none of these were independent predictors of mor-tality, we acknowledge the potential for omitted variables and residual confounding in all observational studies. As with any retrospective study, we cannot claim a causal relationship be-tween perioperative hospitalists’ participation in patient care

and lower mortality. The input into care, action, and duration of involvement by the comanagers is also not clear in these clinical and administrative data. We acknowledge that few of the UHC postoperative complication measures are related to anesthesia, but there was no association between individual anesthesiologist and comanagement use. Similarly, we were unable to account for clustering of outcomes around specific surgeons. Additionally, this study focused on a single academic institution, the findings from which may not be directly gener-alized to other care settings. Risk adjustment in observational studies is never perfect, and testing the hypothesis through a prospective experimental design remains future work. In con-sideration of these limitations, the results of this study are most appropriately used to inform additional exploration.

The ways in which providers work collaboratively to achieve desired outcomes represent an area for additional re-search. Whether the mechanisms of rescue involve early de-tection of complications or the facilitation of therapy remains unknown. Although reproducing the effects of practice model interventions like comanagement is challenging, understand-ing the operational characteristics of care delivery structures is an important part of seeking generalizable knowledge and a step toward improving patient safety and care quality.

n Table 3. Unadjusted and Adjusted Odds Ratios of Inpatient Death (n = 501)

Characteristic

Unadjusted OR (95% CI)

P

Adjusted OR (95% CI)

P

Comanagement 0.85 (0.33-2.20) .743 0.23 (0.05-0.99) .049

Age 1.02 (0.99-1.06) .231 1.02 (0.98-1.06) .242

Male 1.33 (0.52-3.42) .549 0.83 (0.27-2.54) .748

Black race 1.71 (0.55-5.36) .354 2.16 (0.54-8.63) .278

Charlson Comorbidity Index score 1.26 (1.07-1.50) .006 1.19 (0.96-1.47) .120

Emergency surgery 8.13 (2.84-23.3) <.001 2.06 (0.45-9.40) .351

ASA score >4 12.00 (3.79-38.0) <.001 4.73 (1.09-20.60) .038

Admission from ED 2.00 (0.69-5.76) .201 1.80 (0.37-8.64) .463

Admission from inpatient unit 4.40 (1.64-11.79) .003 3.66 (0.92-14.56) .065

Number of surgeries 1.50 (0.77-2.94) .231 1.00 (0.42-2.39) >.999

Number of nonsurgical consultants 2.08 (1.48-2.93) <.001 2.78 (1.69-4.58) <.001

ASA indicates American Society of Anesthesiologists; CI, confidence interval; ED, emergency department; OR, odds ratio.

n Table 4. Unadjusted and Adjusted Comparisons of Cost and Length of Stay by Comanagement Status

Hospitalization Variable

Not Comanaged, Median (IQR)

Comanaged, Median (IQR)

P

Adjusted Difference With Comanagementa

P

Length of stay, days 7.3 (4.4, 13.0) 8.2 (5.3, 14.5) .04 −2.6 <.01

Cost, $ 58,132.5 (33,655.5, 95,537) 72,891 (36,022, 159,314) <.01 7779.0 .35

IQR indicates interquartile range. aAdjusted for age, sex, race, Charlson comorbidity score, emergency surgery, American Society of Anesthesiologists score, admission source, and number of nonsurgical consultants involved.

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Author Affiliations: From Division of Hospital Medicine (KH, DEF, MVW), Division of General Internal Medicine and Institute for Healthcare Studies (JF), Northwestern University Feinberg School of Medicine, Chicago, IL.

Funding Source: No funding was provided for this work.

Author Disclosures: The authors (KH, JF, DEF, MVW) report no rela-tionship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (KH, MVW); acquisition of data (KH, DEF); analysis and interpretation of data (KH, JF, DEF, MVW); drafting of the manuscript (KH, JF); critical revision of the manuscript for important intellectual content (KH, JF, MVW); statistical analysis (KH); ad-ministrative, technical, or logistic support (DEF); and supervision (MVW).

Address correspondence to: Keiki Hinami, MD, MS, 211 E Ontario St, Ste 7-727, Chicago, IL 60611. E-mail: khinami@nmh.org.

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