PURPOSE: Risk functions can help general practitioners identify patients at high cardiovascular risk, but overprediction inevitably leads to a disproportionate number of patients being targeted for treatment. To assess predicted cardiovascular risk, we analyzed the 10-year performance of the original and REGICOR Framingham coronary risk functions in nondiabetic patients. METHODS: Ours was a longitudinal, observational study of a retrospective cohort of patients observed for 10 years in primary care practices in Badajoz, Spain. Our cohort comprised 447 nondiabetic patients aged 35 to 74 years who had no evidence of cardiovascular disease and were not on lipid-lowering or antihypertensive therapy. We assessed the patients' 10-year coronary risk measurement from the time of their recruitment. We also estimated the percentage of patients who were candidates for antihypertensive and lipid-lowering therapy. RESULTS: The actual incidence rate of coronary events was 6.7%. The original Framingham equation overpredicted risk by 73%, whereas the REGICOR Framingham function underpredicted risk by 64%. The Brier scores were 0.06364 and 0.06093 (P = .365) for the original Framingham and REGICOR Framingham functions, respectively, and the remaining discrimination and calibration parameters were also highly similar for both functions. The original Framingham function classified 14.8% of the population as high risk and the REGICOR Framingham function classified 6.9%. The proportions of patients who, according to the original Framingham and REGICOR functions, would be candidates for lipid-lowering therapy were 14.3% and 6.7%, and for antihypertensive therapy they were 12.5% and 7.8%, respectively. CONCLUSION: The original Framingham equation overestimated coronary risk whereas the REGICOR Framingham function underestimated it. The original Framingham function selected a greater percentage of candidates for antihypertensive and lipid-lowering therapy.
PURPOSE: Risk functions can help general practitioners identify patients at high cardiovascular risk, but overprediction inevitably leads to a disproportionate number of patients being targeted for treatment. To assess predicted cardiovascular risk, we analyzed the 10-year performance of the original and REGICOR Framingham coronary risk functions in nondiabetic patients. METHODS: Ours was a longitudinal, observational study of a retrospective cohort of patients observed for 10 years in primary care practices in Badajoz, Spain. Our cohort comprised 447 nondiabetic patients aged 35 to 74 years who had no evidence of cardiovascular disease and were not on lipid-lowering or antihypertensive therapy. We assessed the patients' 10-year coronary risk measurement from the time of their recruitment. We also estimated the percentage of patients who were candidates for antihypertensive and lipid-lowering therapy. RESULTS: The actual incidence rate of coronary events was 6.7%. The original Framingham equation overpredicted risk by 73%, whereas the REGICOR Framingham function underpredicted risk by 64%. The Brier scores were 0.06364 and 0.06093 (P = .365) for the original Framingham and REGICOR Framingham functions, respectively, and the remaining discrimination and calibration parameters were also highly similar for both functions. The original Framingham function classified 14.8% of the population as high risk and the REGICOR Framingham function classified 6.9%. The proportions of patients who, according to the original Framingham and REGICOR functions, would be candidates for lipid-lowering therapy were 14.3% and 6.7%, and for antihypertensive therapy they were 12.5% and 7.8%, respectively. CONCLUSION: The original Framingham equation overestimated coronary risk whereas the REGICOR Framingham function underestimated it. The original Framingham function selected a greater percentage of candidates for antihypertensive and lipid-lowering therapy.
Authors: José M Baena-Díez; María Grau; Ricard Sánchez-Pérez; Eva Altes-Vaques; Luis H Salas-Gaetjens; María R Hernández-Ibáñez Journal: Rev Esp Cardiol Date: 2009-10 Impact factor: 4.753
Authors: Thomas Pa Debray; Johanna Aag Damen; Richard D Riley; Kym Snell; Johannes B Reitsma; Lotty Hooft; Gary S Collins; Karel Gm Moons Journal: Stat Methods Med Res Date: 2018-07-23 Impact factor: 3.021
Authors: Johanna A Damen; Romin Pajouheshnia; Pauline Heus; Karel G M Moons; Johannes B Reitsma; Rob J P M Scholten; Lotty Hooft; Thomas P A Debray Journal: BMC Med Date: 2019-06-13 Impact factor: 8.775