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J Am Coll Cardiol, 2003; 42:1890-1895, doi:10.1016/j.jacc.2003.06.014 © 2003 by the American College of Cardiology Foundation |
* Division of Cardiology, William Beaumont Hospital, Royal Oak, Michigan, USA
Manuscript received June 25, 2002; revised manuscript received May 20, 2003, accepted June 10, 2003.
* Reprint requests and correspondence: Dr. William W. O'Neill, Division of Cardiology, William Beaumont Hospital, 3601 West Thirteen Mile Road, Royal Oak, Michigan 48073-6769, USA.
woneill{at}beaumont.edu
OBJECTIVES: We sought to develop a simplified scoring system based on pre-intervention clinical characteristics to predict in-hospital mortality after percutaneous coronary intervention (PCI).
BACKGROUND: Percutaneous coronary intervention is associated with variety of complications, including the risk of death. Factors leading to poor outcomes need to be identified. Currently available indexes are cumbersome and therefore seldom used.
METHODS: Crude mortality and univariate odds ratios (ORs) for mortality associated with multiple clinical characteristics were calculated for 9,954 patients undergoing PCI at the William Beaumont Hospital during 1996 to 1998. Based on the OR, each factor was assigned a weighted score. Using these scores, a classification was constructed to determine the probability of death after PCI, with classes I through IV representing an increasing probability of procedural mortality. This classification was validated in a separate group of patients.
RESULTS: The factors with the highest univariate odds of dying and their scores were: myocardial infarction <14 days = 7; elevated creatinine = 4; multivessel disease = 4; and age >65 years = 3. Classes were created based on the presence of these factors in a given patient. The odds of dying and mortality increased significantly with each class. These results were reproduced in the validation subset.
CONCLUSIONS: Preprocedural clinical risk factors have a differential influence on the probability of death after PCI. Risk classification based on these factors can be used to accurately predict the procedural outcome. This simple classification can be used by interventionalists to assist in management decisions, to provide an estimate of procedural risk to the patients and relatives, and for quality assurance.
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