• George F Chamoun, Linyan Li, Nassib G Chamoun, Vikas Saini, and Daniel I Sessler.
• The Lown Institute, Boston, Massachusetts (G.F.C., N.G.C., V.S.); Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts (L.L.); and Department of Outcomes Research, Cleveland Clinic, Cleveland, Ohio (D.I.S.).
• Anesthesiology. 2018 Jan 1; 128 (1): 109-116.

BackgroundThe Risk Stratification Index and the Hierarchical Condition Categories model baseline risk using comorbidities and procedures. The Hierarchical Condition categories are rederived yearly, whereas the Risk Stratification Index has not been rederived since 2010. The two models have yet to be directly compared. The authors thus rederived the Risk Stratification Index using recent data and compared their results to contemporaneous Hierarchical Condition Categories.MethodsThe authors reimplemented procedures used to derive the original Risk Stratification Index derivation using the 2007 to 2011 Medicare Analysis and Provider review file. The Hierarchical Condition Categories were constructed on the entire data set using software provided by the Center for Medicare and Medicaid Services. C-Statistics were used to compare discrimination between the models. After calibration, accuracy for each model was evaluated by plotting observed against predicted event rates.ResultsDiscrimination of the Risk Stratification Index improved after rederivation. The Risk Stratification Index discriminated considerably better than the Hierarchical Condition Categories for in-hospital, 30-day, and 1-yr mortality and for hospital length-of-stay. Calibration plots for both models demonstrated linear predictive accuracy, but the Risk Stratification Index predictions had less variance.ConclusionsRisk Stratification discrimination and minimum-variance predictions make it superior to Hierarchical Condition Categories. The Risk Stratification Index provides a solid basis for care-quality metrics and for provider comparisons.

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