Simple Bedside Additive Tool for Prediction of In-Hospital Mortality After Percutaneous Coronary Interventions

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Abstract
Background Risk-adjustment models for percutaneous coronary intervention (PCI) mortality have been recently reported, but application in bedside prediction of prognosis for individual patients remains untested. Methods and Results Between July 1, 1997 and September 30, 1999, 10 796 consecutive procedures were performed in a consortium of 8 hospitals. Predictors of in-hospital mortality were identified by use of multivariate logistic regression analysis. The final model was validated by use of the bootstrap technique. Additional validation was performed on an independent data set of 5863 consecutive procedures performed between October 1, 1999, and August 30, 2000. An additive risk-prediction score was developed by rounding coefficients of the logistic regression model to the closest half-integer, and a visual bedside tool for the prediction of individual patient prognosis was developed. In this patient population, the in-hospital mortality rate was 1.6%. Multivariate regression analysis identified acute m...

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