Predicting risk on cardiovascular or cerebrovascular disease based on a physical activity cohort: Results from APAC study
Abstract Commonly used prediction models have been primarily constructed without taking physical activity into account. Using the Kailuan physical activity cohorts from Asymptomatic Polyvascular Abnormalities in Community (APAC) study, we developed a 9‐year cardiovascular or cerebrovascular disease...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-04-01
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| Series: | MedComm |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/mco2.220 |
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| Summary: | Abstract Commonly used prediction models have been primarily constructed without taking physical activity into account. Using the Kailuan physical activity cohorts from Asymptomatic Polyvascular Abnormalities in Community (APAC) study, we developed a 9‐year cardiovascular or cerebrovascular disease (CVD) risk prediction equation. Participants in this study were included from APAC cohort, which included 5440 participants from the Kailuan cohort in China. Cox proportional hazard regression model was applied to construct sex‐specific risk prediction equations for the physical activity cohort (PA equation). Proposed equations were compared with the 10‐year risk prediction model, which is developed for atherosclerotic cardiovascular disease risk in Chinese cohorts (China‐PAR equation). C statistics of PA equations were 0.755 (95% confidence interval, 0.750–0.758) for men and 0.801 (95% confidence interval, 0.790–0.813) for women. The estimated area under the receiver operating characteristic curves in the validation set shows that the PA equations perform as good as the China‐PAR. From calibration among four categories of predicted risks, the predicted risk rates by PA equations were almost identical to the Kaplan–Meier observed rates. Therefore, our developed sex‐specific PA equations have effective performance for predicting CVD for physically active cohorts in the physical activity cohort in Kailuan. |
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| ISSN: | 2688-2663 |