CLINICAL STUDIES
Multivariate prediction of in-hospital mortality after percutaneous coronary interventions in 1994–1996
Gerald T. O’Connor, PhD, DSc, FACC*  ,
David J. Malenka, MD, FACC*  ,
Hebe Quinton, MS,
John F. Robb, MD, FACC* ,
Mirle A. Kellett, Jr., MD, FACC ||,
Samuel Shubrooks, MD, FACC¶,
William A. Bradley, MD, FACC#,
Michael J. Hearne, MD, FACC#,
Mathew W. Watkins, MD, FACC**,
David E. Wennberg, MD ||,
Bruce Hettleman, MD, FACC* ,
Daniel J. O’Rourke, MD, MS* ,
Paul D. McGrath, MD,
Thomas Ryan, Jr., MD, FACC ||,
Peter VerLee, MD, FACC for the Northern New England Cardiovascular Disease Study Group
* Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
Section of Cardiology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
Center for the Evaluative Clinical Sciences, Dartmouth Medical School, Hanover, New Hampshire, USA
Section of Cardiology, Maine Medical Center, Portland, Maine, USA
|| Section of Health Services Research, Maine Medical Center, Portland, Maine, USA
¶ Beth Israel–Deaconess Medical Center, Boston, Massachusetts, USA
# Catholic Medical Center, Manchester, New Hampshire, USA
** Section of Cardiology, Fletcher-Allen Health Care, Burlington, Vermont, USA
Eastern Maine Medical Center, Bangor, Maine, USA
Manuscript received August 28, 1998;
revised manuscript received April 7, 1999,
accepted May 16, 1999.
Reprint requests and correspondence: Dr. Gerald T. O’Connor, Center for the Evaluative Clinical Sciences, Dartmouth Medical School, 7251 Strasenburgh Hall, Room 330, Hanover, New Hampshire 03755-3863
OBJECTIVES
Using recent data, we sought to identify risk factors associated with in-hospital mortality among patients undergoing percutaneous coronary interventions.
BACKGROUND
The ability to accurately predict the risk of an adverse outcome is important in clinical decision making and for risk adjustment when assessing quality of care. Most clinical prediction rules for percutaneous coronary intervention (PCI) were developed using data collected before the broader use of new interventional devices.
METHODS
Data were collected on 15,331 consecutive hospital admissions by six clinical centers. Logistic regression analysis was used to predict the risk of in-hospital mortality.
RESULTS
Variables associated with an increased risk of in-hospital mortality included older age, congestive heart failure, peripheral or cerebrovascular disease, increased creatinine levels, lowered ejection fraction, treatment of cardiogenic shock, treatment of an acute myocardial infarction, urgent priority, emergent priority, preprocedure insertion of an intraaortic balloon pump and PCI of a type C lesion. The receiver operating characteristic area for the predicted probability of death was 0.88, indicating a good ability to discriminate. The rule was well calibrated, predicting accurately at all levels of risk. Bootstrapping demonstrated that the estimate was stable and performed well among different patient subsets.
CONCLUSIONS
In the current era of interventional cardiology, accurate calculation of the risk of in-hospital mortality after a percutaneous coronary intervention is feasible and may be useful for patient counseling and for quality improvement purposes.
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Abbreviations and Acronyms
| | CABG | = coronary artery bypass graft surgery | | CI | = confidence interval | | COPD | = chronic obstructive pulmonary disease | | EF | = ejection fraction | | IABP | = intraaortic balloon pump | | LVEDP | = left ventricular end-diastolic pressure | | MI | = myocardial infarction | | OR | = odds ratio | | PCI | = percutaneous coronary intervention | | PTCA | = percutaneous transluminal coronary angioplasty | | PVD | = peripheral vascular disease | | ROC | = receiver operating characteristic |  2 LR | = likelihood ratio chi-square test |
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