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Electronically Available Comorbidities Should Be Used in Surgical Site Infection Risk Adjustment
Sarah S Jackson1, Surbhi Leekha1, Laurence S Magder1
1Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore.
Background:
Healthcare-associated infections such as surgical site infections (SSIs) are used by the Centers for Medicare and Medicaid Services (CMS) as pay-for-performance metrics. Risk adjustment allows a fairer comparison of SSI rates across hospitals. Until 2016, Centers for Disease Control and Prevention (CDC) risk adjustment models for pay-for-performance SSI did not adjust for patient comorbidities. New 2016 CDC models only adjust for body mass index and diabetes.
Methods:
We performed a multicenter retrospective cohort study of patients undergoing surgical procedures at 28 US hospitals. Demographic data and International Classification of Diseases, Ninth Revision codes were obtained on patients undergoing colectomy, hysterectomy, and knee and hip replacement procedures. Complex SSIs were identified by infection preventionists at each hospital using CDC criteria. Model performance was evaluated using measures of discrimination and calibration. Hospitals were ranked by SSI proportion and risk-adjusted standardized infection ratios (SIR) to assess the impact of comorbidity adjustment on public reporting.
Results:
Of 45394 patients at 28 hospitals, 573 (1.3%) developed a complex SSI. A model containing procedure type, age, race, smoking, diabetes, liver disease, obesity, renal failure, and malnutrition showed good discrimination (C-statistic, 0.73) and calibration. When comparing hospital rankings by crude proportion to risk-adjusted ranks, 24 of 28 (86%) hospitals changed ranks, 16 (57%) changed by ≥2 ranks, and 4 (14%) changed by >10 ranks.
Conclusions:
We developed a well-performing risk adjustment model for SSI using electronically available comorbidities. Comorbidity-based risk adjustment should be strongly considered by the CDC and CMS to adequately compare SSI rates across hospitals.
Insights
A new risk adjustment model for surgical site infections (SSIs) incorporating patient comorbidities significantly impacts hospital performance rankings. This improved model is crucial for accurate pay-for-performance metrics and fair public reporting of infection rates.
Area of Science:
- Healthcare epidemiology
- Health services research
- Infectious disease prevention
Background:
- Healthcare-associated infections, including surgical site infections (SSIs), are key performance metrics for the Centers for Medicare and Medicaid Services (CMS).
- Existing Centers for Disease Control and Prevention (CDC) risk adjustment models for SSIs have historically lacked comprehensive adjustment for patient comorbidities, potentially leading to unfair comparisons.
- Recent CDC models (post-2016) offer limited comorbidity adjustment, focusing only on body mass index and diabetes.
Purpose of the Study:
- To develop and evaluate a robust risk adjustment model for complex surgical site infections (SSIs) that incorporates a wider range of patient comorbidities.
- To assess the impact of comorbidity-adjusted risk stratification on the public reporting and comparative ranking of hospital performance.
Main Methods:
- A multicenter retrospective cohort study involving 45,394 patients across 28 US hospitals undergoing colectomy, hysterectomy, or knee/hip replacement procedures.
- Utilized International Classification of Diseases, Ninth Revision (ICD-9) codes for demographic and comorbidity data, with complex SSIs identified by hospital infection preventionists.
- Evaluated model performance using discrimination (C-statistic) and calibration, and compared hospital rankings based on crude SSI proportions versus risk-adjusted standardized infection ratios (SIRs).
Main Results:
- A total of 573 (1.3%) complex SSIs were identified among the study cohort.
- The developed risk adjustment model, incorporating comorbidities like diabetes, liver disease, obesity, renal failure, and malnutrition, demonstrated strong performance (C-statistic = 0.73).
- Risk adjustment significantly altered hospital rankings: 86% of hospitals changed ranks, with 57% shifting by two or more positions, and 14% by over ten positions.
Conclusions:
- A well-performing risk adjustment model for SSIs has been developed using readily available electronic comorbidity data.
- The findings strongly advocate for the adoption of comorbidity-based risk adjustment by the CDC and CMS to ensure equitable and accurate comparisons of hospital SSI rates.
- Enhanced risk adjustment is essential for fair pay-for-performance evaluations and transparent public reporting of healthcare quality metrics.
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