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J Am Coll Cardiol, 2002; 39:1104-1112 © 2002 by the American College of Cardiology Foundation |
* San Francisco Heart Institute at Seton Medical Center, Daly City, California 94015, USA
Manuscript received August 13, 2001; revised manuscript received November 29, 2001, accepted January 9, 2002.
* Reprint requests and correspondence: Richard E. Shaw, PhD, FACC, San Francisco Heart Institute at Seton Medical Center, 1900 Sullivan Avenue, Daly City, California 94015, USA.
RichardShaw{at}dochs.org
OBJECTIVES: We sought to develop and evaluate a risk adjustment model for in-hospital mortality following percutaneous coronary intervention (PCI) procedures using data from a large, multi-center registry.
BACKGROUND: The 1998–2000 American College of Cardiology–National Cardiovascular Data Registry (ACC–NCDR) dataset was used to overcome limitations of prior risk-adjustment analyses.
METHODS: Data on 100,253 PCI procedures collected at the ACC–NCDR between January 1, 1998, and September 30, 2000, were analyzed. A training set/test set approach was used. Separate models were developed for presentation with and without acute myocardial infarction (MI) within 24 h.
RESULTS: Factors associated with increased risk of PCI mortality (with odds ratios in parentheses) included cardiogenic shock (8.49), increasing age (2.61 to 11.25), salvage (13.38) urgent (1.78) or emergent PCI (5.75), pre-procedure intra-aortic balloon pump insertion (1.68), decreasing left ventricular ejection fraction (0.87 to 3.93), presentation with acute MI (1.31), diabetes (1.41), renal failure (3.04), chronic lung disease (1.33); treatment approaches including thrombolytic therapy (1.39) and non-stent devices (1.64); and lesion characteristics including left main (2.04), proximal left anterior descending disease (1.97) and Society for Cardiac Angiography and Interventions lesion classification (1.64 to 2.11). Overall, excellent discrimination was achieved (C-index = 0.89) and application of the model to high-risk patient groups demonstrated C-indexes exceeding 0.80. Patient factors were more predictive in the MI model, while lesion and procedural factors were more predictive in the analysis of non-MI patients.
CONCLUSIONS: A risk adjustment model for in-hospital mortality after PCI was successfully developed using a contemporary multi-center registry. This model is an important tool for valid comparison of in-hospital mortality after PCI.
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