Evaluation of comorbidity scores to predict all-cause mortality in patients with established coronary artery disease
Introduction
Coronary artery disease (CAD) remains the leading cause of death in the United States [1]. However, patients with CAD often have associated health conditions that may profoundly impact their overall mortality risk. Extensive data exist on risk-stratification in patients with CAD based on functional testing for ischemia, the presence and severity of congestive heart failure, the angiographic severity of CAD, biomarkers of plaque vulnerability, myocardial vulnerability and thrombogenicity, and propensity to develop fatal arrhythmias [2], [3], [4], [5], [6], [7], [8], [9]. However, there is limited data on risk-stratification for all-cause mortality in patients with CAD based on the presence of comorbidities [10]. Such data would be very useful for both outcomes researchers and clinicians who wish to undertake an objective assessment of the mortality risk imposed by comorbidities in individual patients.
The Charlson Index, a global index of comorbidity derived from a cohort of general medical patients, [11] has been extensively used to assess comorbidities in different populations [12], [13], [14], [15], [16], [17], [18]. A modified Charlson Index (which removes the points for the CAD complications of myocardial infarction and heart failure from the original index) has been applied in patients with CAD [19]. Recently, a CAD-specific score that weighs comorbid conditions according to their impact on all-cause mortality in patients with CAD has been developed in a population of patients referred for cardiac catheterization at Duke University Medical Center [19]. Both scores along with the weights that they assign to each specific comorbid condition are shown in Table 1.
The performance of the CAD-specific score was at least as good as that of the modified Charlson Index in an independent sample of patients from the same population from which the score was derived [19]. Given the potential variation in underlying disease and other population characteristics, direct use of this score cannot be assumed to be valid for risk prediction in other populations. Critically important for the evaluation of the performance of these scoring systems is to test them in independent populations, and confirmatory studies are needed to judge the value of the newly proposed CAD-specific comorbidity score. In this study, we aimed to test the ability of the CAD-specific score and the modified Charlson score to predict all-cause mortality in patients with established CAD.
Section snippets
Study population
We studied a cohort of 420 male veterans undergoing coronary angiography at the Miami Veterans Administration Medical Center between October 1998 and February 2000. The study was approved by the Hospital’s Institutional Review Board and written informed consent was obtained from all patients. In the entire cohort study, indications for angiography included stable angina, abnormal cardiac stress test, acute coronary syndromes, cardiomyopathy, and valvular disease. Only subjects with at least one
Results
Among the 420 patients who signed the informed consent, 315 had hemodynamically significant CAD. Nine patients were lost for follow and one patient died one day after cardiac catheterization and was excluded from the analysis. The final analysis was performed with data from 305 patients. The baseline characteristics of our patient population are shown in Table 2. The population consisted predominantly of Caucasian males; 29.5% had a prior myocardial infarction, and 50% had triple-vessel
Discussion
We performed an independent evaluation of the performance of 2 different comorbidity scores in patients with established CAD referred for cardiac catheterization at our institution. We prospectively followed the cohort for 5 years and correlated both scores with the risk of all-cause mortality. Both scores were shown to be strong and independent predictors of mortality. In multivariate analysis, these scores provided a stronger prediction of death than age, left ventricular systolic function,
Acknowledgments
This work was partially funded by support from the American Heart Association, Grant in Aid (Grant # 9950534N to AJM) and The Retirement Research Foundation (RBG and AJM)
References (21)
- et al.
American College of Cardiology/American Heart Association Expert Consensus Document on electron-beam computed tomography for the diagnosis and prognosis of coronary artery disease
J Am Coll Cardiol
(2000) Comorbidity and outcome in patients with coronary artery disease
J Am Coll Cardiol
(2004)- et al.
A new method of classifying prognostic comorbidity in longitudinal studies: development and validation
J Chronic Dis
(1987) - et al.
Evaluation of Charlson-age comorbidity index as predictor of morbidity and mortality in patients with colorectal carcinoma
J Gastrointest Surg
(2004) - et al.
Common comorbidity scales were similar in their ability to predict health care costs and mortality
J Clin Epidemiol
(2004) - et al.
How to measure comorbidity. A critical review of available methods
J Clin Epidemiol
(2003) - et al.
The prognostic importance of comorbidity for mortality in patients with stable coronary artery disease
J Am Coll Cardiol
(2004) - et al.
How to adjust for comorbidity in survival studies in ESRD patients: a comparison of different indices
Am J Kidney Dis
(2002) 2003 heart and stroke statistical update
(2002)2002 guideline update for the management of patients with chronic stable angina — summary article: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on the Management of Patients With Chronic Stable Angina)
J Am Coll Cardiol
(2003)
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