Objectives: This study investigated the impact of adding novel elements to models predicting in-hospital mortality after percutaneous coronary interventions (PCIs).
Background: Massachusetts mandated public reporting of hospital-specific PCI mortality in 2003. In 2006, a physician advisory group recommended adding to the prediction models 3 attributes not collected by the National Cardiovascular Data Registry instrument. These "compassionate use" (CU) features included coma on presentation, active hemodynamic support during PCI, and cardiopulmonary resuscitation at PCI initiation.
Methods: From October 2005 through September 2007, PCI was performed during 29,784 admissions in Massachusetts nonfederal hospitals. Of these, 5,588 involved patients with ST-segment elevation myocardial infarction or cardiogenic shock. Cases with CU criteria identified were adjudicated by trained physician reviewers. Regression models with and without the CU composite variable (presence of any of the 3 features) were compared using areas under the receiver-operator characteristic curves.
Results: Unadjusted mortality in this high-risk subset was 5.7%. Among these admissions, 96 (1.7%) had at least 1 CU feature, with 69.8% mortality. The adjusted odds ratio for in-hospital death for CU PCIs (vs. no CU criteria) was 27.3 (95% confidence interval: 14.5 to 47.6). Discrimination of the model improved after including CU, with areas under the receiver-operating characteristic curves increasing from 0.87 to 0.90 (p < 0.01), while goodness of fit was preserved.
Conclusions: A small proportion of patients at extreme risk of post-PCI mortality can be identified using pre-procedural factors not routinely collected, but that heighten predictive accuracy. Such improvements in model performance may result in greater confidence in reporting of risk-adjusted PCI outcomes.
Copyright © 2011 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.