The importance of severity of illness adjustment in predicting adverse outcomes in the Medicare population

UMMS Affiliation

Department of Quantitative Health Sciences

Publication Date


Document Type



Aged; Aged, 80 and over; Angioplasty, Transluminal, Percutaneous Coronary; Cholecystectomy; Coronary Artery Bypass; Data Interpretation, Statistical; Demography; Female; Humans; Male; Models, Statistical; Postoperative Complications; Predictive Value of Tests; Prostatectomy; Quality of Health Care; Risk Factors; *Severity of Illness Index; Surgical Procedures, Operative


Biostatistics | Epidemiology | Health Services Research


The importance of using risk-adjusted mortality rates to measure quality of care is well-established. However, mortality rates may be an insensitive measure of quality for surgical patients since death is a relatively rare outcome. This study used Medicare files to identify, through chart abstraction, clinical postoperative complications of four surgical procedures (n = 8126) that could serve as measures of quality. Disease-specific severity of illness models using a moderate number of clinical variables and admission MedisGroups score models computed from approximately 250 clinical variables were compared in predicting postoperative adverse events. Initial differences between the two models disappeared upon cross-validation. Validated R-squareds and C statistics from models using half the data were generally positive, suggesting that these models had real, although modest, predictive power. We have shown that severity of illness on admission plays a role in predicting adverse events of surgery. Risk-adjusted outcomes may potentially be useful in screening for quality shortfalls.

DOI of Published Version



J Clin Epidemiol. 1995 May;48(5):631-43. Link to article on publisher's site

Journal/Book/Conference Title

Journal of clinical epidemiology

PubMed ID


Related Resources

Link to Article in PubMed