BACKGROUND: Interest in the reporting of risk-adjusted outcomes for patients with acute myocardial infarction is growing. A useful risk-adjustment model must balance parsimony and ease of data collection with predictive ability. METHODS AND RESULTS: From our analysis of 82 359 patients >/=65 years of age admitted with acute myocardial infarction to 2401 hospitals, we derived a parsimonious model that predicts 30-day mortality. The model was validated on a similar group of 78 699 patients from 2386 hospitals. Of the 73 candidate predictor variables examined, 7 variables describing patient characteristics on arrival were selected for inclusion in the final model: age, cardiac arrest, anterior or lateral location of myocardial infarction, systolic blood pressure, white blood cell count, serum creatinine, and congestive heart failure. The area under the receiver-operating characteristic curve for the final model was 0.77 in the derivation cohort and 0.77 in the validation cohort. The rankings of hospitals by performance (in deciles) with this model were most similar to a comprehensive 27-variable model based on medical chart review and least similar to models based on administrative billing codes. CONCLUSIONS: A simple 7-variable risk model performs as well as more complex models in comparing hospital outcomes for acute myocardial infarction. Although there is a continuing need to improve methods of risk adjustment, our results provide a basis for hospitals to develop a simple approach to compare outcomes.
BACKGROUND: Interest in the reporting of risk-adjusted outcomes for patients with acute myocardial infarction is growing. A useful risk-adjustment model must balance parsimony and ease of data collection with predictive ability. METHODS AND RESULTS: From our analysis of 82 359 patients >/=65 years of age admitted with acute myocardial infarction to 2401 hospitals, we derived a parsimonious model that predicts 30-day mortality. The model was validated on a similar group of 78 699 patients from 2386 hospitals. Of the 73 candidate predictor variables examined, 7 variables describing patient characteristics on arrival were selected for inclusion in the final model: age, cardiac arrest, anterior or lateral location of myocardial infarction, systolic blood pressure, white blood cell count, serum creatinine, and congestive heart failure. The area under the receiver-operating characteristic curve for the final model was 0.77 in the derivation cohort and 0.77 in the validation cohort. The rankings of hospitals by performance (in deciles) with this model were most similar to a comprehensive 27-variable model based on medical chart review and least similar to models based on administrative billing codes. CONCLUSIONS: A simple 7-variable risk model performs as well as more complex models in comparing hospital outcomes for acute myocardial infarction. Although there is a continuing need to improve methods of risk adjustment, our results provide a basis for hospitals to develop a simple approach to compare outcomes.
Authors: Amar R Prabhudesai; M A Srilakshmi; M J Santosh; Gurappa G Shetty; Kiron Varghese; Chandrakant B Patil; Shamanna S Iyengar Journal: Indian Heart J Date: 2012 May-Jun
Authors: Merril L Knudtson; Colleen M Norris; P Diane Galbraith; Jaro Hubacek; William A Ghali Journal: Can J Cardiol Date: 2009-06 Impact factor: 5.223