Potential Role of Comanagement in Rescue of Surgical Patients

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 non-comanaged (n = 204) hospitalizations were well matched. Medical comanagement was associated with fewer in-hospital deaths (odds ratio 0.23, 95% confidence interval 0.05-0.99) in adjusted analysis. Comanaged compared with non-comanaged 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)

We evaluated the effect of medical comanagement on outcomes of hospitalized surgical patients who had postoperative complications.

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

• The higher cost of comanaged hospitalizations mandates development of tools that allow for the cost-effective application of comanagement.

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 conditions&mdash;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 studies^2-4 support the view that many adverse events in surgical patients are due to problems in medical management rather than intraoperative 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 potential 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 specialtyspecific 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 particular 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 academic hospital’s electronic medical records. We hypothesized that comanagement of hospitalized surgical patients would have a protective effect on risk-adjusted mortality through the rescue phenomenon suggested 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 postsurgical complications. We also assessed the impact of comanagement on risk-adjusted length of stay and hospital cost.

METHODS

Setting

We conducted our study at an urban 854-bed teaching hospital. 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 neurosurgery, urology, and orthopedic, transplant, and vascular surgeries were also staffed by nonphysician providers.

The perioperative medicine service was staffed by 7 general internists from the Division of Hospital Medicine who provided preoperative evaluation and postoperative comanagement services. Comanagement was available 7 days a week during daytime hours. Infrequently, cross-covering night hospitalists 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 patients

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 patients, their orientation toward improving postsurgical care quality, and the higher intensity of involvement in their patients’ care reflected by the typically greater frequency of encounters compared with other medical specialty consultants.

Subjects

All inpatient adult surgical discharges following a procedure 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 population 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) physical status score,¹⁴ ICD-9 data to calculate the Charlson Comorbidity Score,¹⁵ and hospital cost data were obtained. We also obtained information about the total number of nonsurgical consultants who were involved in each hospitalization. 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 consortium, which then uses diagnosis-related groups and ICD-9 codes to identify rates of 24 specific surgical complications. The institutional review board of Northwestern University approved the study protocols, consents, and data collection mechanisms.

Analysis

We compared patient demographic and risk characteristics using the X² test and paired t test for categorical and continuous data, respectively. Postoperative complications were tabulated and differences in frequency by comanagement status were compared using the X² 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 hospitalization by comanagement status were compared using the Wilcoxon rank-sum test. Risk-adjusted comparisons were performed using generalized linear regression modeling.

RESULTS

Selection of Patients Into the Study

Figure

The 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 procedures 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 electronically documented. Of the remaining 3620 hospitalizations, 501 (13.8%) involved patients who had at least 1 postoperative complication and were included in the analysis.

Characteristics of Patients and Their Hospitalizations

Table 1

Characteristics of patients and their hospitalizations are shown in . The 3 subgroups of admission source were ambulatory recovery, referring to patients whose hospitalization followed surgery in an ambulatory setting; emergency department; and inpatient unit, referring to patients transferred from nonsurgical 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 surgery 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 procedures 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 replacement (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 measures in the study’s hospitalizations in descending order of frequency. Deep vein thrombosis or pulmonary embolism was the most common complication (31.9%). Hospitalizations involving pneumonia and other infectious complications were more likely to be comanaged.

Association Between Comanagement and Survival

Bivariate 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 source, the number of nonsurgical consultants, and the Charlson 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, comanagement was associated with lower risk-adjusted mortality (odds ratio [OR] 0.23, P = .049) during the postoperative hospitalization.

Length of Stay and Cost

Table 4

When using unadjusted comparisons, we found that both median length of hospitalization and cost were higher in comanaged compared with non-comanaged hospitalizations (). Risk adjustment, however, yielded shorter mean length of hospitalization by 2.6 days (P <.01) among comanaged compared with non-comanaged hospitalizations. The risk-adjusted cost of hospitalization was not different by comanagement status.

DISCUSSION

In this exploratory observational study, postoperative comanagement by a general internist focused on perioperative care was associated with lower risk-adjusted mortality among surgical patients who had a postoperative complication. Comanaged hospitalizations were associated with shorter riskadjusted lengths of stay with no difference in risk-adjusted cost. Due to the traditionally low mortality rate in the surgical inpatient population, the effect of interventions to further lower mortality in this population is difficult to demonstrate. Although care quality interventions such as the implementation of comanagement may not be expected to have a dramatic 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 comanagement is a departure from several recent studies that examined the effects of preoperative consultations.^7,8 Although 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 benefit in the role of hospitalists as facilitators of rescue. The exact 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 improvement 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 neurosurgical service was explored recently by Auerbach and colleagues using a retrospective interrupted time-series analysis.⁹ Although the study by Auerbach et al did not detect a decrease in adjusted mortality with the implementation of comanagement, 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 increasingly elderly and comorbid surgical population. Although the mortality benefit of comanagement remains hypothetical, future 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 associated 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 hospitalists. 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 assertion that risk prediction scores derived from administrative data such as the Charlson Comorbidity Index are poorer predictors of mortality than clinical instruments like the ASA score.²¹ Not surprisingly, the number of nonsurgical consultants 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 of nonsurgical consultants. We believe that a possible explanation 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 surveillance 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 residual confounding.

The study is limited in several other ways. The major limitation 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 mortality, 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 between 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 generalized 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 consideration 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 research. Whether the mechanisms of rescue involve early detection of complications or the facilitation of therapy remains unknown. Although reproducing the effects of practice model interventions like comanagement is challenging, understanding 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.

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 relationship 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); administrative, 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.

1. Ghaferi AA, Birkmeyer JD, Dimick JB. Variation in hospital mortality associated with inpatient surgery. N Engl J Med. 2009;361(14): 1368-1375.

2. Gawande AA, Thomas EJ, Zinner MJ, Brennan TA. The incidence and nature of surgical adverse events in Colorado and Utah in 1992. Surgery. 1999;126(1):66-75.

3. Neale G, Woloshynowych M, Vincent C. Exploring the causes of adverse events in NHS hospital practice. J R Soc Med. 2001;94(7): 322-330.

4. Vincent C, Moorthy K, Sarker SK, Chang A, Darzi AW. Systems approaches to surgical quality and safety: from concept to measurement. Ann Surg. 2004;239(4):475-482.

5. Sharma G, Kuo YF, Freeman J, Zhang DD, Goodwin JS. Comanagement of hospitalized surgical patients by medicine physicians in the United States. Arch Intern Med. 2010;170(4):363-368.

6. Whinney C, Michota F. Surgical comanagement: a natural evolution of hospitalist practice. J Hosp Med. 2008;3(5):394-397.

7. Wijeysundera DN, Austin PC, Beattie WS, Hux JE, Laupacis A. Outcomes and processes of care related to preoperative medical consultation. Arch Intern Med. 2010;170(15):1365-1374.

8. Auerbach AD, Rasic MA, Sehgal N, Ide B, Stone B, Maselli J. Opportunity missed: medical consultation, resource use, and quality of care of patients undergoing major surgery. Arch Intern Med. 2007;167(21): 2338-2344.

9. Auerbach AD, Wachter RM, Cheng HQ, et al. Comanagement of surgical patients between neurosurgeons and hospitalists. Arch Intern Med. 2010;170(22):2004-2010.

10. Huddleston JM, Long KH, Naessens JM, et al. Medical and surgical comanagement after elective hip and knee arthroplasty: a randomized, controlled trial. Ann Intern Med. 2004;141(1):28-38.

11. Ghaferi AA, Birkmeyer JD, Dimick JB. Complications, failure to rescue, and mortality with major inpatient surgery in medicare patients. Ann Surg. 2009;250(6):1029-1034.

12. Silber JH, Rosenbaum PR, Schwartz JS, Ross RN, Williams SV. Evaluation of the complication rate as a measure of quality of care in coronary artery bypass graft surgery. JAMA. 1995;274(4):317-323.

13. Silber JH, Williams SV, Krakauer H, Schwartz JS. Hospital and patient characteristics associated with death after surgery: a study of adverse occurrence and failure to rescue. Med Care. 1992;30(7):615-629.

14. American Society of Anesthesiologists. ASA Physical Status Classification System. http://www.asahq.org/clinical/physicalstatus.htm. Accessed March 16, 2010.

15. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130-1139.

16. University Health Systems Consortium. Home page. http://www.uhc.edu. Accessed March 16, 2010.

17. Meltzer DO. Social science insights into improving workforce effectiveness: examples from the developing field of hospital medicine. J Public Health Manag Pract. 2009;15(6 suppl):S18-S23.

18. Bellomo R, Goldsmith D, Uchino S, et al. Prospective controlled trial of effect of medical emergency team on postoperative morbidity and mortality rates. Crit Care Med. 2004;32(4):916-921.

19. Silber JH, Rosenbaum PR, Ross R. Comparing the contributions of groups of predictors: which outcomes vary with hospital rather than patient characteristics. J Am Stat Assoc. 1995;90(429):7-18.

20. Ghaferi AA, Osborne NH, Birkmeyer JD, Dimick JB. Hospital characteristics associated with failure to rescue from complications after pancreatectomy. J Am Coll Surg. 2010;211(3):325-330.

21. Quach S, Hennessy DA, Faris P, Fong A, Quan H, Doig C. A comparison between the APACHE II and Charlson Index Score for predicting hospital mortality in critically ill patients. BMC Health Serv Res. 2009;9:129.

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