Identifying High and Low Performing Emergency General Surgery Hospitals Using Direct Standardization

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Kelz doi: https://doi.org/10.1101/2024.02.23.24303292 Drew Goldberg 1 Center for Surgery and Health Economics, Department of Surgery, University of Pennsylvania , Philadelphia PA 2 Perelman School of Medicine, University of Pennsylvania , Philadelphia PA 4 Department of Surgery, Perelman School of Medicine, University of Pennsylvania , Philadelphia PA MD, MSTR Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Drew Goldberg Luke Keele 1 Center for Surgery and Health Economics, Department of Surgery, University of Pennsylvania , Philadelphia PA 3 Leonard Davis Institute of Health Economics, University of Pennsylvania , Philadelphia PA 4 Department of Surgery, Perelman School of Medicine, University of Pennsylvania , Philadelphia PA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luke Keele Chris Wirtalla 1 Center for Surgery and Health Economics, Department of Surgery, University of Pennsylvania , Philadelphia PA MBMI Find this author on Google Scholar Find this author on PubMed Search for this author on this site Solomiya Syvyk 1 Center for Surgery and Health Economics, Department of Surgery, University of Pennsylvania , Philadelphia PA 4 Department of Surgery, Perelman School of Medicine, University of Pennsylvania , Philadelphia PA BA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rachel R. Kelz 1 Center for Surgery and Health Economics, Department of Surgery, University of Pennsylvania , Philadelphia PA 2 Perelman School of Medicine, University of Pennsylvania , Philadelphia PA 3 Leonard Davis Institute of Health Economics, University of Pennsylvania , Philadelphia PA 4 Department of Surgery, Perelman School of Medicine, University of Pennsylvania , Philadelphia PA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: Rachel.Kelz{at}pennmedicine.upenn.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Importance Variation in outcomes for emergency general surgery conditions has been shown at the hospital level. Few have examined difference across hospitals for older adults who often present with the greatest risk. To date, no one has examined differences in the outcome for those undergoing operative and nonoperative treatment. Objective Identify high and low performing emergency general surgery (EGS) hospitals with risk-standardization to determine clinical performance differences as well as correlation between patients treated operatively and non-operatively. Design A retrospective cohort study with 30-day outcomes. Setting Nationwide study of acute care hospitals. Participants Medicare beneficiaries > 65.5 years old hospitalized for an emergency general surgery condition admitted from July 1, 2015 to June 30, 2018. Exposure Unique hospital identification. Main outcome A composite metric of 30-day mortality, adverse events, prolonged length of stay, and readmission. Results There were 536,284 total patients with a mean age of 74.4 ± 12.2 years, 55% female, 84% white with average claims-based frailty index of 0.16 ± 0.06 and mean comorbidity count of 3.57 ± 2.46. Amongst the 1866 hospitals identified, there were 3 best performing and 11 worst performing hospitals. There were weak correlations between operative and non-operative for mortality (0.10), adverse events rates (0.21), prolonged length of stay (0.32), and readmissions (0.18) at the hospital level (all p<0.001). Conclusions and Relevance Significant variation exists in EGS hospital performance with best ranked hospitals out-performing worst ranked hospitals on adverse event, mortality, prolonged length of stay and readmission. There is little association between patient outcomes for those treated with operative and non-operative care. Introduction Emergency general surgery (EGS) hospitalizations comprise a significant proportion of total admissions in the United States with both operative and non-operative treatment options available to EGS patients. 1 Given variation across both patient-level and hospital-level risk means, certain patients may do better being directed to particular hospitals. 2 As such, measuring quality amongst EGS hospitals can provide insights into where patients would have the best outcome. One method to capture variability across EGS hospitals performance includes ranking hospitals according to both mortality and non-mortality measures. 3 Given the heterogeneity in both patient and hospital characteristics of EGS-treated populations, such an analysis provides meaningful insight into consistencies or deviations in both treatment pathways and resource utilization. We sought to use a novel patient risk-adjustment method to rank EGS hospitals according to mortality, readmission, length of stay, and adverse events. We looked to identify both high and low performers amongst hospitals to understand unique attributes of those hospitals to serve as signals for hospital selection. We then sought to determine if patient outcomes were correlated among operative and non-operative patient populations. Methods Patients with a principal diagnosis of an EGS conditions who were Medicare (fee-for-service) beneficiaries 65.5 years of age and older, and admitted through the emergency department to a non-federal, acute care hospital between July 1, 2015 and June 30, 2018 were included. EGS conditions were identified using International Classification of Disease, Ninth or Tenth Revision, Clinical Modification codes. 4 Patient comorbidities were classified and analyzed according to previously published methods. 4 – 6 The main outcome was a composite of 30-day adverse events defined by death, prolonged length of stay or readmission within 30-days of hospital discharge. 7 Death, prolonged length of stay and readmission within 30-days were examined as secondary outcomes. Hospital characteristics were extracted from the American Hospital Association and mean star rating from the Medicare Hospital Compare tool. 8 , 9 To control for differences in patient populations across hospitals that could account for estimated differences in hospital outcomes, we performed direct standardization using balancing weights on 71 indicators. Specifically, we standardize each hospitals’ patient population to the overall patient population. 10 Balancing weights are a computational generalization of inverse propensity score weight that we use to create hospitals with patient populations with similar covariate distributions. Balancing weights assign a scalar weight to each patient such that we can take hospital level weighted averages that account for differences across hospital populations. We then generated hospital level rankings using an Empirical Bayes estimator that corrects for variation in the size of each hospital’s patient population. Prior research has shown that a balancing weights approach to direct standardization reduces bias due to observed differences between the comparison in patient populations which increasing precision by reducing the loss of sample size. 10 We also identified the set of hospitals with a high probability of having a high or low rank relative to the overall distribution. Standard and rank correlations of hospitals were examined between performance for operative and nonoperative treatments. All statistical analyses were conducted in R version 4.1.1. Results There were 536,284 total patients with a mean age of 74.4 ± 12.2 years and frailty index of 0.16 ± 0.06. Most patients were female (55%), white (84%), and had 3+ comorbid conditions (61%). Ten percent of the population was Black and 2% Hispanic. Among the 1866 hospitals identified, 3 best performing hospitals were identified for all patients. The mean hospital compare star rating was 3.7 ± 0.9. There were 11 identified worst performing hospitals with a mean hospital star rating of 2.4 ± 1.1. For operative treatment, there were no distinctly best performing hospitals and 3 worst performing hospitals identified. Of these 3 hospitals, the average hospital stars rating was 2.9 ± 0.8. For non-operative treatment, there were 6 identified best performing and 2 worst performing hospitals. The best performing non-operative hospitals had a mean star rating of 2.8 ± 0.5. Amongst the hospitals that were worst performing at non-operative treatment (n=2), there was an average star rating of 3.0 ± 1.4. There were no apparent differences in hospital characteristics across hospitals by performance. ( Table 1 ) View this table: View inline View popup Table 1: Patient and Hospital Characteristics Across Hospitals, by Treatment Group * Unadjusted results are presented in Table 2 . A caterpillar plot ( Figure 1 ) demonstrates individual hospital performance as measured by risk standardized adverse event rates relative to the remainder of the hospitals. There are only a select few hospitals that either performed statistically better (at the right tail) or worse than (at the left tail) the remainder of the hospital dataset. After adjustment, there was a significantly higher rate of adverse events at the worst hospitals (57% ± 4%)) when compared with the best hospitals (25% ± 5%). There were no best performing hospitals for operative treatment. The worst hospitals for operative treatment displayed adjusted rates of adverse events that were similar to those seen for the hospitals performing worst in non-operative treatment (57% ± 10%). For non-operative treatment, there was a higher rate of adverse events at the worst hospitals (60% ± 2%) when compared with the best hospitals (29% ± 6%). Secondary outcomes demonstrated similar findings. ( Table 2 ) View this table: View inline View popup Table 2: Outcomes by Hospital Ranking and Treatment Group* View this table: View inline View popup Download powerpoint Table 4: Standard and Rank Correlations Between Operative and Non-Operative Performance Measures Download figure Open in new tab Figure 1: Caterpillar Plot Indicating Individual Hospital Risk-Standardized Adverse Event Rates. The standard correlation between operative and non-operative outcomes at the hospital level was 0.10 for overall mortality, 0.27 for prolonged length of stay, 0.17 for readmission and 0.20 for adverse event. The Spearman rank correlation between the two subsets of patients was 0.10 for overall mortality, 0.32 for prolonged length of stay, 0.18 for readmission and 0.21 for adverse events (all p<0.001). Discussion Using direct standardization to rank hospitals on EGS performance in this nationwide study of older adults, we demonstrate a more than two-fold difference in adverse event rates between the best and worst performing hospitals. Hospital performance differed similarly on 30-day death, prolonged length of stay and readmissions. There was also significant variation in hospital performance in the non-operative treatment subgroup. The worst hospitals for operative treatment achieved outlier status while there were no hospitals that distinguished themselves as the best in operative treatment. Ingraham et al. found that EGS performance across conditions was only weakly correlated within hospitals. 11 Further, Zogg et al. demonstrated that within EGS conditions, hospital performance was consistent across multiple outcome types. 3 We build on these findings to demonstrate that hospital performance on several metrics in operative and nonoperative treatment is only weakly correlated, and that there are a limited number of hospitals within the United States that are distinguished in EGS care of older adults. Limitations This study has several limitations. First, variation in coding practices across hospitals may impact the capture of adverse events. However, for this study, we captured death, prolonged length of stay, and readmissions which are all known to be valid and reliable in Medicare claims. Second, direct standardization can only adjust for observable covariates which may explain the paucity of outlier hospitals. That is, if there are key patient covariates that are unobserved which contribute to hospital quality, we cannot account for these covariates under direct standardization. However, the number of best and worst performing hospitals in our study is similar to other published reports. 11 , 12 Conclusion We have confirmed variation in EGS performance among hospitals treating older adults with the best hospitals significantly outperforming the worst hospitals on all outcome measures examined. These data suggest that EGS outcomes may be optimized with targeted hospital selection. Weak correlation between hospital performance on operative and non-operative care suggests that finding the right hospital for each patient is more complicated than defining a Center of Excellence designation. Further, over 14 years, there has only been a 13% increase in adoption of acute care surgery models that could improve EGS outcomes within hospitals. 13 As such, alternative approaches to supplement quality and safety initiatives are needed until best practices become universal. Data Availability All data produced in the present study are available upon reasonable request to the authors Acknowledgements Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number R01AG060612. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. References 1. ↵ Ogola GO , Crandall ML , Shafi S. Variations in outcomes of emergency general surgery patients across hospitals: A call to establish emergency general surgery quality improvement program . J Trauma Acute Care Surg . 2018 ; 84 ( 2 ): 280 – 286 . doi: 10.1097/TA.0000000000001755 OpenUrl CrossRef PubMed 2. ↵ Teng CY , Davis BS , Rosengart MR , Carley KM , Kahn JM . Assessment of Hospital Characteristics and Interhospital Transfer Patterns of Adults With Emergency General Surgery Conditions . JAMA Netw Open . 2021 ; 4 ( 9 ): e2123389 . doi: 10.1001/jamanetworkopen.2021.23389 OpenUrl CrossRef 3. ↵ Zogg CK , Staudenmayer KL , Kodadek LM , Davis KA . Reconceptualizing high-quality emergency general surgery care: Non–mortality-based quality metrics enable meaningful and consistent assessment . J Trauma Acute Care Surg . 2023 ; 94 ( 1 ): 68 – 77 . doi: 10.1097/TA.0000000000003818 OpenUrl CrossRef 4. ↵ Kaufman EJ , Keele LJ , Wirtalla CJ , et al. Operative and Nonoperative Outcomes of Emergency General Surgery Conditions: An Observational Study Using a Novel Instrumental Variable . Ann Surg . 2023 ; 278 ( 1 ): 72 – 78 . doi: 10.1097/SLA.0000000000005519 OpenUrl CrossRef 5. Elixhauser A , Steiner D , Harris DR , Coffey RM . Comorbidity Measures For Use with Administrative Data . Med Care . 1998 ; 36 ( 1 ): 8 – 27 . OpenUrl CrossRef PubMed Web of Science 6. ↵ Ramadan OI , Rosenbaum PR , Reiter JG , et al. Redefining Multimorbidity in Older Surgical Patients . J Am Coll Surg . 2023 ; 236 ( 5 ): 1011 – 1022 . doi: 10.1097/XCS.0000000000000659 OpenUrl CrossRef 7. ↵ Rosen CB , Roberts SE , Wirtalla CJ , et al. The Conditional Effects of Multimorbidity on Operative Versus Nonoperative Management of Emergency General Surgery Conditions: A Retrospective Observational Study Using an Instrumental Variable Analysis . Ann Surg . 2023 ; 278 ( 4 ): e855 – e862 . doi: 10.1097/SLA.0000000000005901 OpenUrl CrossRef 8. ↵ AHA Annual Survey Database . American Hospital Association . 9. ↵ Overall Hospital Quality Star Rating . Accessed June 15, 2023 . https://www.medicare.gov/care-compare/?redirect=true&providerType=Hospital 10. ↵ Keele LJ , Ben-Michael E , Feller A , Kelz R , Miratrix L. Hospital quality risk standardization via approximate balancing weights . Ann Appl Stat . 2023 ; 17 ( 2 ): 901 – 928 . doi: 10.1214/22-AOAS1629 OpenUrl CrossRef 11. ↵ Ingraham AM , Cohen ME , Bilimoria KY , et al. Comparison of 30-day outcomes after emergency general surgery procedures: Potential for targeted improvement . Surgery . 2010 ; 148 ( 2 ): 217 – 238 . doi: 10.1016/j.surg.2010.05.009 OpenUrl CrossRef PubMed Web of Science 12. ↵ Becher RD , Sukumar N , DeWane MP , et al. Hospital Variation in Geriatric Surgical Safety for Emergency Operation . J Am Coll Surg . 2020 ; 230 ( 6 ): 966 – 973e10 . doi: 10.1016/j.jamcollsurg.2019.10.018 OpenUrl CrossRef 13. ↵ Khubchandani JA , Ingraham AM , Daniel VT , Ayturk D , Kiefe CI , Santry HP . Geographic Diffusion and Implementation of Acute Care Surgery: An Uneven Solution to the National Emergency General Surgery Crisis . JAMA Surg . 2018 ; 153 ( 2 ): 150 . doi: 10.1001/jamasurg.2017.3799 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted March 02, 2024. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Identifying High and Low Performing Emergency General Surgery Hospitals Using Direct Standardization Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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