Christine Wallisch1, Georg Heinze1, Christoph Rinner2, Gerald Mundigler3, Wolfgang C Winkelmayer4, Daniela Dunkler5. 1. Medical University of Vienna, CeMSIIS, Section for Clinical Biometrics, Vienna, Austria. 2. Medical University of Vienna, CeMSIIS, Section for Medical Information Management, Vienna, Austria. 3. Medical University of Vienna, Department of Internal Medicine 2, Division of Cardiology, Vienna, Austria. 4. Baylor College of Medicine, Section of Nephrology, Houston, TX, USA. 5. Medical University of Vienna, CeMSIIS, Section for Clinical Biometrics, Vienna, Austria. Electronic address: daniela.dunkler@meduniwien.ac.at.
Abstract
BACKGROUND: Cardiovascular prevention guidelines advocate the use of statistical risk equations to predict individual cardiovascular risk. However, predictive accuracy and clinical value of existing equations may differ in populations other than the one used for their development. Using baseline and follow-up data of the Austrian health-screening program, we assessed discrimination, calibration, and clinical utility of three widely recommended equations-the Framingham 1991 and 2008 general cardiovascular disease (CVD) equations, and the Pooled Cohort equations predicting atherosclerotic CVD. METHODS: The validation cohort comprised 1.7 M individuals aged 30-79, without documented CVD history who participated in the program from 2009 to 2014. CVD events were defined by a cardiovascular cause of hospitalization or death. RESULTS: The observed five-year general CVD risk was 4.66%. Discrimination c-indices (0.72-0.78) were slightly lower than those reported for the development cohorts. C-indices for women were always higher than for men. CVD risk was overestimated by the Framingham 2008 equation, but underestimated by the Pooled Cohort equations. The Framingham 1991 equation was well-calibrated, especially for individuals up to 64 years. If applied to recommend health interventions at a predicted five-year risk between 5 and 10%, the equations were clinically useful with their net benefits, weighting true positives against false positives, ranging from 0.13 to 3.43%. CONCLUSION: The equations can discriminate high-risk from low-risk individuals, but predictive accuracy (especially for high-risk individuals) might be improved by recalibration. The Framingham 1991 equation yielded the most accurate predictions.
BACKGROUND: Cardiovascular prevention guidelines advocate the use of statistical risk equations to predict individual cardiovascular risk. However, predictive accuracy and clinical value of existing equations may differ in populations other than the one used for their development. Using baseline and follow-up data of the Austrian health-screening program, we assessed discrimination, calibration, and clinical utility of three widely recommended equations-the Framingham 1991 and 2008 general cardiovascular disease (CVD) equations, and the Pooled Cohort equations predicting atherosclerotic CVD. METHODS: The validation cohort comprised 1.7 M individuals aged 30-79, without documented CVD history who participated in the program from 2009 to 2014. CVD events were defined by a cardiovascular cause of hospitalization or death. RESULTS: The observed five-year general CVD risk was 4.66%. Discrimination c-indices (0.72-0.78) were slightly lower than those reported for the development cohorts. C-indices for women were always higher than for men. CVD risk was overestimated by the Framingham 2008 equation, but underestimated by the Pooled Cohort equations. The Framingham 1991 equation was well-calibrated, especially for individuals up to 64 years. If applied to recommend health interventions at a predicted five-year risk between 5 and 10%, the equations were clinically useful with their net benefits, weighting true positives against false positives, ranging from 0.13 to 3.43%. CONCLUSION: The equations can discriminate high-risk from low-risk individuals, but predictive accuracy (especially for high-risk individuals) might be improved by recalibration. The Framingham 1991 equation yielded the most accurate predictions.
Authors: Christine Wallisch; Asan Agibetov; Daniela Dunkler; Maria Haller; Matthias Samwald; Georg Dorffner; Georg Heinze Journal: BMC Med Res Methodol Date: 2021-12-18 Impact factor: 4.615