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.
Mots-clés Pascal : Complication, Postopératoire, Prédiction, Qualité, Soin, Hôpital, Homme, Méthode étude, Modèle, Indice gravité, Maladie, Système santé
Mots-clés Pascal anglais : Complication, Postoperative, Prediction, Quality, Care, Hospital, Human, Investigation method, Models, Severity score, Disease, Health system
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 95-0259750
Code Inist : 002B30A04A. Création : 01/03/1996.