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Meta-Analysis
. 2001 Dec;38(7):1902-11.
doi: 10.1016/s0735-1097(01)01667-9.

Utility of current risk stratification tests for predicting major arrhythmic events after myocardial infarction

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Free article
Meta-Analysis

Utility of current risk stratification tests for predicting major arrhythmic events after myocardial infarction

J J Bailey et al. J Am Coll Cardiol. 2001 Dec.
Free article

Abstract

Objectives: We surveyed the literature to estimate prediction values for five common tests for risk of major arrhythmic events (MAEs) after myocardial infarction. We then determined feasibility of a staged risk stratification using combinations of noninvasive tests, reserving an electrophysiologic study (EPS) as the final test.

Background: Improved approaches are needed for identifying those patients at highest risk for subsequent MAE and candidates for implantable cardioverter-defibrillators.

Methods: We located 44 reports for which values of MAE incidence and predictive accuracy could be inferred: signal-averaged electrocardiography; heart rate variability; severe ventricular arrhythmia on ambulatory electrocardiography; left ventricular ejection fraction; and EPS. A meta-analysis of reports used receiver-operating characteristic curves to estimate mean values for sensitivity and specificity for each test and 95% confidence limits. We then simulated a clinical situation in which risk was estimated by combining tests in three stages.

Results: Test sensitivities ranged from 42.8% to 62.4%; specificities from 77.4% to 85.8%. A three-stage stratification yielded a low-risk group (80.0% with a two-year MAE risk of 2.9%), a high-risk group (11.8% with a 41.4% risk) and an unstratified group (8.2% with an 8.9% risk equivalent to a two-year incidence of 7.9%).

Conclusions: Sensitivities and specificities for the five tests were relatively similar. No one test was satisfactory alone for predicting risk. Combinations of tests in stages allowed us to stratify 91.8% of patients as either high-risk or low-risk. These data suggest that a large prospective study to develop a robust prediction model is feasible and desirable.

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