Comparison of Comorbidity Scores in Predicting Surgical Outcomes

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Abstract

Introduction:

The optimal methodology for assessing comorbidity to predict various surgical outcomes such as mortality, readmissions, complications, and failure to rescue (FTR) using claims data has not been established.

Objective:

Compare diagnosis-based and prescription-based comorbidity scores for predicting surgical outcomes.

Methods:

We used 100% Texas Medicare data (2006–2011) and included patients undergoing coronary artery bypass grafting, pulmonary lobectomy, endovascular repair of abdominal aortic aneurysm, open repair of abdominal aortic aneurysm, colectomy, and hip replacement (N=39,616). The ability of diagnosis-based [Charlson comorbidity score, Elixhauser comorbidity score, Combined Comorbidity Score, Centers for Medicare and Medicaid Services-Hierarchical Condition Categories (CMS-HCC)] versus prescription-based Chronic disease score in predicting 30-day mortality, 1-year mortality, 30-day readmission, complications, and FTR were compared using c-statistics (c) and integrated discrimination improvement (IDI).

Results:

The overall 30-day mortality was 5.8%, 1-year mortality was 17.7%, 30-day readmission was 14.1%, complication rate was 39.7%, and FTR was 14.5%. CMS-HCC performed the best in predicting surgical outcomes (30-d mortality, c=0.797, IDI=4.59%; 1-y mortality, c=0.798, IDI=9.60%; 30-d readmission, c=0.630, IDI=1.27%; complications, c=0.766, IDI=9.37%; FTR, c=0.811, IDI=5.24%) followed by Elixhauser comorbidity index/disease categories (30-d mortality, c=0.750, IDI=2.37%; 1-y mortality, c=0.755, IDI=5.82%; 30-d readmission, c=0.629, IDI=1.43%; complications, c=0.730, IDI=3.99%; FTR, c=0.749, IDI=2.17%). Addition of prescription-based scores to diagnosis-based scores did not improve performance.

Conclusions:

The CMS-HCC had superior performance in predicting surgical outcomes. Prescription-based scores, alone or in addition to diagnosis-based scores, were not better than any diagnosis-based scoring system.

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