title: Comparison of Comorbidity Scores in Predicting Surgical Outcomes.
abstract: BACKGROUND
  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.
authors.affiliation: *Department of Surgery, The University of Texas Medical Branch, Galveston, TX †Department of Surgery, The University of South Florida, Tampa, FL ‡Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, Galveston, TX §Department of Surgery, University of Arizona, Tucson, AZ.
authors.forename: Hemalkumar B
authors.initials: HB
authors.lastname: Mehta
authors.forename: Francesca
authors.initials: F
authors.lastname: Dimou
authors.forename: Deepak
authors.initials: D
authors.lastname: Adhikari
authors.forename: Nina P
authors.initials: NP
authors.lastname: Tamirisa
authors.forename: Eric
authors.initials: E
authors.lastname: Sieloff
authors.forename: Taylor P
authors.initials: TP
authors.lastname: Williams
authors.forename: Yong-Fang
authors.initials: YF
authors.lastname: Kuo
authors.forename: Taylor S
authors.initials: TS
authors.lastname: Riall
mesh_terms: D000367:Age Factors; D015897:Comorbidity; D000067248:Failure to Rescue, Health Care / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D054524:Medicare Part D / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data; D011183:Postoperative Complications / Q000453:epidemiology*; D011379:Prognosis; D020379:Risk Adjustment / Q000379:methods*; D012737:Sex Factors; D013514:Surgical Procedures, Operative / Q000706:statistics & numerical data*; D014481:United States