Prediction of 30-Year Risk for Cardiovascular Mortality by Fitness and Risk Factor Levels: The Cooper Center Longitudinal Study

Jul 01, 2014
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Authors:
Wickramasinghe CD, Ayers CR, Das S, de Lemos JA, Willis BL, Berry JD.
Citation:
Circ Cardiovasc Qual Outcomes 2014;Jul 1:[Epub ahead of print].
Summary By:
Elizabeth A. Jackson, MD, FACC

Study Questions:

Does fitness improve current short- and long-term cardiovascular disease (CVD) risk prediction tools?

Methods:

Data from participants enrolled in the Cooper Center Longitudinal Study who had no CVD at baseline were included. Fitness was measured using the Balke protocol. Sex-specific fitness levels were derived from the Balke treadmill times and categorized into low, intermediate, and high fit according to age- and sex-specific treadmill times. Sex-specific 30-year risk estimates for CVD death adjusted for competing risk of non-CVD death were estimated using the cause-specific hazards model and included age, body mass index, systolic blood pressure, fitness, diabetes mellitus, total cholesterol, and smoking.

Results:

A total of 16,533 participants were included. There were higher levels of traditional risk factors among the men compared to the women participants. Higher levels of traditional risk factors were associated with an increased risk for CVD mortality in both men and women, with expected sex differences for both diabetes mellitus and smoking. During a median follow-up period of 28 years, there were 1,123 CVD deaths. The 30-year risk estimates for CVD mortality derived from the cause-specific hazards model demonstrated overall good calibration and discrimination. Across all risk factor strata, the presence of low fitness was associated with a greater 30-year risk for CVD death.

Conclusions:

The investigators concluded that fitness represents an important additional covariate in 30-year risk prediction functions that may serve as a useful tool in clinical practice.

Perspective:

These data support the benefit of higher levels of fitness in terms of lower risk for CV mortality. Use of information on fitness to prediction tools may add clinical information; however, changes in fitness levels over a patient’s lifetime may reduce the accuracy of fitness when added to risk calculators.


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