Related Experiment Video
Updated: Jan 23, 2026

In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Framingham score and work-related variables for predicting cardiovascular disease in the working population
Albert-Jan van der Zwaard1, Anna Geraedts1, Giny Norder1
1Department of Research and Business Development, HumanTotalCare, Utrecht, The Netherlands.
Background:
The Framingham score is commonly used to estimate the risk of cardiovascular disease (CVD). This study investigated whether work-related variables improve Framingham score predictions of sickness absence due to CVD.
Methods:
Eleven occupational health survey variables (descent, marital status, education, work type, work pace, cognitive demands, supervisor support, co-worker support, commitment to work, intrinsic work motivation and distress) and the Framingham Point Score (FPS) were combined into a multi-variable logistic regression model for CVD sickness absence during 1-year follow-up of 19 707 survey participants. The Net Reclassification Index (NRI) was used to investigate the added value of work-related variables to the FPS risk classification. Discrimination between participants with and without CVD sickness absence during follow-up was investigated by the area under the receiver operating characteristic curve (AUC).
Results:
A total of 129 (0.7%) occupational health survey participants had CVD sickness absence during 1-year follow-up. Manual work and high cognitive demands, but not the other work-related variables contributed to the FPS predictions of CVD sickness absence. However, work type and cognitive demands did not improve the FPS classification for risk of CVD sickness absence [NRI = 2.3%; 95% confidence interval (CI) -2.7 to 9.5%; P = 0.629]. The FPS discriminated well between participants with and without CVD sickness absence (AUC = 0.759; 95% CI 0.724-0.794).
Conclusion:
Work-related variables did not improve predictions of CVD sickness absence by the FPS. The non-laboratory Framingham score can be used to identify health survey participants at risk of CVD sickness absence.
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