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Am Heart J. 2011 Nov;162(5):875-883.e1. doi: 10.1016/j.ahj.2011.08.010. Epub 2011 Oct 5.

Predicting long-term mortality in older patients after non-ST-segment elevation myocardial infarction: the CRUSADE long-term mortality model and risk score.

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1
Duke Clinical Research Institute, Durham, NC, USA. matthew.roe@duke.edu

Abstract

OBJECTIVES:

We sought to develop a long-term mortality risk prediction model and a simplified risk score for use in older patients with non-ST-segment elevation myocardial infarction (NSTEMI).

BACKGROUND:

Limited data are available regarding long-term mortality rates and concomitant risk predictors after acute myocardial infarction in contemporary community practice.

METHODS:

From the CRUSADE registry, a total of 43,239 (NSTEMI) patients aged ≥65 years treated at 448 hospitals in the United States from 2003 to 2006 were linked to Centers for Medicare and Medicaid Services data to track longitudinal all-cause mortality (median follow-up 453 days). Cox proportional hazard modeling was used to determine baseline independent demographic, clinical, and laboratory variables associated with long-term mortality. A simplified long-term mortality risk score was subsequently developed from these results.

RESULTS:

The median age of this population was 77 years, and mortality rates at 1, 2, and 3 years were 24.4%, 33.2%, and 40.3%, respectively. We identified 22 variables independently associated with long-term mortality in a full model (c-statistic 0.754 in the derivation sample and 0.744 in the validation sample). The CRUSADE long-term mortality risk score was limited to the 13 most clinically and statistically significant variables from the full model yet retained comparable discrimination in the derivation and validation samples (c-statistics 0.734 and 0.727, respectively) and had good calibration across the risk spectra.

CONCLUSIONS:

Older patients face substantial long-term mortality risks after NSTEMI that can be accurately predicted from baseline characteristics. These prognostic estimates may support informed treatment decision-making and comparison of long-term provider outcomes.

PMID:
22093204
DOI:
10.1016/j.ahj.2011.08.010
[Indexed for MEDLINE]
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