Background: Benchmarking the performance of providers is an increasing priority in many health care economies. In-hospital mortality represents an important and uniformly assessed measure on which to examine the outcome of percutaneous coronary intervention (PCI). Most existing prediction models of in-hospital mortality after PCI were derived from 1990s data, and their current relevance is uncertain.
Methods: From consecutive PCIs performed during 2000-2008, derivation and validation cohorts of 10,694 and 5,347 patients, respectively, were analyzed. Logistic regression for in-hospital death yielded integer risk weights for each independent predictor variable. These were summed for each patient to create the Toronto PCI risk score.
Results: Death occurred in 1.3% of patients. Independent predictors with associated risk weights in parentheses were as follows: age 40 to 49 y (1), 50 to 59 y (2), 60 to 69 y (3), 70 to 79 y (4), and > or =80 y (5); diabetes (2); renal insufficiency (2); New York Heart Association class 4 (3); left ventricular ejection fraction <20% (3); myocardial infarction in the previous month (3); multivessel disease (1); left main disease (2); rescue or facilitated PCI (3); primary PCI (4); and shock (6). The model had a receiver operator curve of 0.96 and Hosmer-Lemeshow goodness-of-fit P = .16 in the validation set. Four previously published external models were tested in the entire data set. Three models had ROC curves significantly less than the Toronto PCI score, and all 4 showed significant levels of imprecision.
Conclusions: The Toronto PCI mortality score is an accurate and contemporary predictive tool that permits evaluation of risk-stratified outcomes and aids counseling of patients undergoing PCI.