Clinical Research
Interventional Cardiology
Improvement in Mortality Risk Prediction After Percutaneous Coronary Intervention Through the Addition of a “Compassionate Use” Variable to the National Cardiovascular Data Registry CathPCI Dataset: A Study From the Massachusetts Angioplasty Registry

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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.

Key Words

American College of Cardiology National Cardiovascular Data Registry CathPCI
hierarchical risk prediction models
percutaneous coronary intervention
predictive models

Abbreviations and Acronyms

CABG
coronary artery bypass graft
CU
compassionate use
Mass-DAC
Massachusetts Data Analysis Center
NCDR
National Cardiovascular Data Registry
PCI
percutaneous coronary intervention
STEMI
ST-segment elevation myocardial infarction

Cited by (0)

Dr. Resnic was supported, in part, by the National Library of Medicine (grant NIH R01-LM008142); has received research funding support from The Medicines Company; and has received modest consulting support from St. Jude Medical, Medtronic, and Agfa Medical Imaging. The work of Dr. Normand, Ann Lovett, and Katya Zelevinsky was supported through a contract with the Department of Public Health of the Commonwealth of Massachusetts (620022A624PRE). Dr. Normand is the principle investigator of Mass-DAC; receives modest consulting income from Kaiser Permamente, the Institute of Clinical Evaluative Sciences, American Heart Association, St. Luke's Hospital System, The Medicines Company, Best Practices Project Management, Ingenix Inc.; and receives significant support from Yale Medical School. Ms. Zelevinsky reports being employed (50%) by Mass-DAC, the sponsoring research organization for this study. Dr. Ho receives moderate consulting support as a member of an independent clinical events committee administered by the Harvard Clinical Research Institute that provides adjudication services for patients enrolled in clinical trials sponsored by Boston Scientific. All other authors have reported that they have no relationships to disclose.