(Circulation. 2001;104:263.)
© 2001 American Heart Association, Inc.
Clinical Investigation and Reports |
Simple Bedside Additive Tool for Prediction of In-Hospital Mortality After Percutaneous Coronary Interventions
From the Division of Cardiology (M.M., E.K.-R., K.A.E.), University of Michigan, Blue Cross Blue Shield of Michigan Cardiovascular Consortium Coordinating Center, Ann Arbor; Blue Cross Blue Shield Center for Health Care Quality (D.S.), Detroit; St. Joseph Mercy Hospital (M.O.), Ann Arbor; Spectrum Health (A.M.-E.), Grand Rapids; Northern Michigan Hospital (W.L.M.), Petoskey; Henry Ford Hospital (P.K.), Detroit; McLaren Regional Medical Center (A.C.D, J.L.C.), Flint; St. Joseph Hospital (K.P.), Pontiac; and Harper Hospital-Wayne State University (J.G.M.), Detroit, Mich.
Correspondence to Mauro Moscucci, MD, University of Michigan Medical Center, Taubman 3910, 1500 E Medical Ctr Dr, Ann Arbor, MI 48109-0022. E-mail moscucci{at}umich.edu
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 myocardial infarction, cardiogenic shock, history of cardiac arrest, renal insufficiency, low ejection fraction, peripheral vascular disease, lesion characteristics, female sex, and advanced age as independent predictors of death. The model had excellent discrimination (area under the receiver operating characteristic curve, 0.90) and was accurate for prediction of mortality among different subgroups. Near-perfect correlation existed between calculated scores and observed mortality, with higher scores associated with higher mortality.
Conclusions— Accurate predictions of individual patient risk of mortality associated with PCI can be achieved with a simple bedside tool. These predictions could be used during discussions of prognosis before and after PCI.
Key Words: angioplasty • risk factors • coronary disease
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