Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis
Open accessAbstract
BACKGROUND: The Framingham risk models and pooled cohort equations (PCE) are widely used and advocated in guidelines for predicting 10-year risk of developing coronary heart disease (CHD) and cardiovascular disease (CVD) in the general population. Over the past few decades, these models have been extensively validated within different populations, which provided mounting evidence that local tailoring is often necessary to obtain accurate predictions. The objective is to systematically review and summarize the predictive performance of three widely advocated cardiovascular risk prediction models (Framingham Wilson 1998, Framingham ATP III 2002 and PCE 2013) in men and women separately, to assess the generalizability of performance across different subgroups and geographical regions, and to determine sources of heterogeneity in the findings across studies. METHODS: A search was performed in October 2017 to identify studies investigating the predictive performance of the aforementioned models. Studies were included if they externally validated one or more of the original models in the general population for the same outcome as the original model. We assessed risk of bias for each validation and extracted data on population characteristics and model performance. Performance estimates (observed versus expected (OE) ratio and c-statistic) were summarized using a random effects models and sources of heterogeneity were explored with meta-regression. RESULTS: The search identified 1585 studies, of which 38 were included, describing a total of 112 external validations. Results indicate that, on average, all models overestimate the 10-year risk of CHD and CVD (pooled OE ratio ranged from 0.58 (95% CI 0.43-0.73; Wilson men) to 0.79 (95% CI 0.60-0.97; ATP III women)). Overestimation was most pronounced for high-risk individuals and European populations. Further, discriminative performance was better in women for all models. There was considerable heterogeneity in the c-statistic between studies, likely due to differences in population characteristics. CONCLUSIONS: The Framingham Wilson, ATP III and PCE discriminate comparably well but all overestimate the risk of developing CVD, especially in higher risk populations. Because the extent of miscalibration substantially varied across settings, we highly recommend that researchers further explore reasons for overprediction and that the models be updated for specific populations.
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The Framingham predicted 10-year CVD risk equations are well accepted and widely used in clinical practice.
“Furthermore, the Framingham predicted 10-year CVD risk equations are well accepted and widely used in clinical practice.28”
From Tirzepatide compared with semaglutide and 10-year cardiovascular disease risk reduction in obesity: <i>post-hoc</i> analysis of the SURMOUNT-5 trial (2025) · cites this paper as [28]
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“Studies that reported performance of the model before and after update showed that performance indeed improves after update; however, the necessary adjustments vary from setting to setting and thus need to be evaluated locally [8, 11, 36, 47, 48, 54].”
BackgroundImprovement in Cardiovascular Risk Prediction with Electronic Health Records
Pike, M., Decker, P. A., Larson, N. B., Sauver, J. L. S., Takahashi, P. Y., Roger, V. L., Rocca, W. A., Miller, V. M., Olson, J. E., Pathak, J., et al. (2016) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-016-9687-z · Cited by 42 · see citing works
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“The increased use of effective treatments over time aimed at preventing CVD events will have lowered the observed number of events in more recent validation studies, resulting in overestimation of risk in these validation populations [39, 75, 76].”
BackgroundMethodSelf-citationComment on the Reports of Over-estimation of ASCVD Risk Using the 2013 AHA/ACC Risk Equation
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“These reasons have also been extensively discussed previously with regard to the PCE [15, 73].”
BackgroundApplication of the Updated Framingham Risk Score to Japanese Men.
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The Veterans Affairs Cardiac Risk Score
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Estimating Cardiovascular Risk in Spain Using Different Algorithms
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The Framingham Risk Function Underestimated Absolute Coronary Heart Disease Risk in Czech Men
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Heart Disease and Stroke Statistics—2023 Update: A Report From the American Heart Association
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Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association
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“, the PCE tends to over-estimate risk in women) (121).”
BackgroundBlockchain and Artificial Intelligence Technology in E-Health
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“Cardiovascular risk score systems predict the likelihood of a person developing cardiovascular disease within a given time frame, usually 10 to 30 years(Damen et al. 2019).”
BackgroundA framework for meta-analysis of prediction model studies with binary and time-to-event outcomes
Debray, T. P., Damen, J. A., Riley, R. D., Snell, K., Reitsma, J. B., Hooft, L., Collins, G. S., & Moons, K. G. (2019) · Statistical Methods in Medical Research · doi:10.1177/0962280218785504 · Cited by 278 · see citing works
Self-citationPalaniappan, L. P., Allen, N. B., Almarzooq, Z., Anderson, C. A. M., Arora, P. K., Avery, C. L., Baker-Smith, C. M., Bansal, N., Currie, M. E., Earlie, R. S., et al. (2026) · Circulation · doi:10.1161/cir.0000000000001412 · Cited by 312 · see citing works
Snell, K. I. E., Levis, B., Damen, J. A., Dhiman, P., Debray, T. P. A., Hooft, L., Reitsma, J. B., Moons, K. G. M., Collins, P. G. S., & Riley, R. D. (2023) · BMJ · doi:10.1136/bmj-2022-073538 · Cited by 209 · see citing works
Self-citationThe Lancet Commission on rethinking coronary artery disease: moving from ischaemia to atheroma
Zaman, S. J., Wasfy, J. H., Kapil, V., Ziaeian, B., Parsonage, W. A., Sriswasdi, S., Chico, T., Capodanno, D., Colleran, R., Sutton, N. R., et al. (2025) · The Lancet · doi:10.1016/s0140-6736(25)00055-8 · Cited by 228 · see citing works
How to conduct a systematic review and meta-analysis of prognostic model studies
Damen, J. A., Moons, K. G. M., van Smeden, M., & Hooft, L. (2022) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2022.07.019 · Cited by 113 · see citing works
“Systematic reviews are needed to summarize the information from their primary publications [5,6].”
Self-citationDebray, T. P. A., Collins, P. G. S., Riley, R. D., Snell, K. I. E., CALSTER, B. V., Reitsma, J. B., & Moons, K. G. M. (2023) · BMJ · doi:10.1136/bmj-2022-071018 · Cited by 97 · see citing works
“33 34-36 To overcome these problems, interest in prediction model studies using large clustered datasets has grown.”
BackgroundSelf-citationWilliams, S. A., Ostroff, R., Hinterberg, M. A., Coresh, J., Ballantyne, C. M., Matsushita, K., Mueller, C. E., Walter, J., Jonasson, C., Holman, R. R., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abj9625 · Cited by 97 · see citing works
“First, the calibration of the model trained in secondary populations overpredicts risk at the high end of the range in primary or mixed populations with lower event rates (similar to reported calibration errors for the PCE and Framingham risk scores) (45), although predictions in the lower and mid-range, where accurac…”
Debray, T. P. A., Collins, P. G. S., Riley, R. D., Snell, K. I. E., CALSTER, B. V., Reitsma, J. B., & Moons, K. G. M. (2023) · BMJ · doi:10.1136/bmj-2022-071058 · Cited by 62 · see citing works
“59”
Self-citationA Review of HIV Pre-exposure Prophylaxis Streamlining Strategies
Siegler, A. J., Steehler, K., Sales, J. M., & Krakower, D. (2020) · Current HIV/AIDS Reports · doi:10.1007/s11904-020-00528-9 · Cited by 68 · see citing works
Performance of the ACC/AHA Pooled Cohort Cardiovascular Risk Equations in Clinical Practice
Medina‐Inojosa, J. R., Somers, V. K., Garcia, M. C., Thomas, R. J., Allison, T. G., Chaudry, R., Wood‐Wentz, C. M., Bailey, K. R., Mulvagh, S. L., & López-Jiménez, F. A. (2023) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2023.07.018 · Cited by 59 · see citing works
Ahmad, A., Lim, L., Morieri, M. L., Tam, C. H. T., Cheng, F., Chikowore, T., Dudenhöffer‐Pfeifer, M., Fitipaldi, H., Huang, C., Kanbour, S., et al. (2024) · Communications Medicine · doi:10.1038/s43856-023-00429-z · Cited by 60 · see citing works
“12, 13 provide the pooled c-statistics from external validation studies on those risk scores for which the analysis was possible: ADVANCE, CHS, CVD-EDIC, NDR, NZ DCS and UKPDS risk scores.”
AHA PREVENT Equations and Cardiovascular Disease Risk in Diverse Health Care Populations
Cho, S. M. J., Levin, M. G., Chen, R. K., Samarakoon, R., Judy, R., Hartmann, K., Postupaka, D., Viscosi, V., Farber‐Eger, E. H., Fahed, A. C., et al. (2025) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2025.04.066 · Cited by 52 · see citing works
“Because the PCE previously demonstrated varied transportability across different contextual settings, 6–8 understanding the demographic-specific predictive abilities of PREVENT may appropriately guide clinicians to effectively achieve primary prevention.”
InfluentialChaves, J. M. Z., Wentland, A. L., Desai, A., Banerjee, I., Kaur, G., Correa, R., Boutin, R. D., Maron, D. J., Rodríguez, F., Sandhu, A. T., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-47895-y · Cited by 45 · see citing works
Applications of AI in multi-modal imaging for cardiovascular disease
Milosevic, M., Jin, Q., Singh, A., & Amal, S. (2024) · Frontiers in Radiology · doi:10.3389/fradi.2023.1294068 · Cited by 53 · see citing works
Romijn, M., Dhiman, P., Finken, M. J. J., van Kaam, A. H., Katz, T. A., Rotteveel, J., Schuit, E., Collins, P. G. S., Onland, W., & Torchin, H. (2023) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2023.01.024 · Cited by 47 · see citing works
Agrawal, S., Klarqvist, M. D. R., Emdin, C. A., Patel, A. P., Paranjpe, M. D., Ellinor, P. T., Philippakis, A., Ng, K., Batra, P., & Khera, A. V. (2021) · Patterns · doi:10.1016/j.patter.2021.100364 · Cited by 50 · see citing works
“7 14 As the number of candidate predictors of CAD increases from tens to thousands, the traditional approach using standard Cox regression models is prone to several limitations.”
MethodLucaroni, F., Cicciarella Modica, D., Macino, M., Palombi, L., Abbondanzieri, A., Agosti, G., Biondi, G., Morciano, L., & Vinci, A. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-030234 · Cited by 42 · see citing works
Marcolino, M. S., Oliveira, J. A. D. Q., Cimini, C. C. R., Maia, J. X., Pinto, V. S. D. O. E. A., de Sá, T. Q. V., Gouvêa, K. A. A., Coelho, L., Ribeiro, L. B., Cardoso, C. S., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/18872 · Cited by 48 · see citing works
“We decided to use the Framingham score [32], as it was widely validated in different populations (European, Asian, Australian, and North American) and assessed important outcomes (coronary artery disease, stroke, peripheral artery disease, and heart failure) with no limitation in number of events (as in the case with…”
MethodPopulation-Based Recalibration of the Framingham Risk Score and Pooled Cohort Equations
Sud, M. K., Sivaswamy, A., Chu, A., Austin, P. C., Anderson, T. J., Naimark, D. M. J., Farkouh, M. E., Lee, D. S., Roifman, I., Thanassoulis, G., et al. (2022) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2022.07.026 · Cited by 41 · see citing works
Guo, Z., Tang, J., Haiying, H., Yuping, Z., Yu, Q., & Jingfen, J. (2022) · BMC Public Health · doi:10.1186/s12889-022-13995-z · Cited by 38 · see citing works
“Compared with the performance of Framingham prediction models in previous studies [45], we found similar results that both Framingham Wilson and Framingham D’Agostino overestimate cardiovascular disease risk.”
ResultCardiovascular risk in patients with severe mental illness in Italy
Salvi, V., Aguglia, A., Barone‐Adesi, F., Bianchi, D., Donfrancesco, C., Dragogna, F., Palmieri, L., Serafini, G., Amore, M., & Mencacci, C. (2020) · European Psychiatry · doi:10.1192/j.eurpsy.2020.94 · Cited by 35 · see citing works
“model overestimates the risk of developing CV disease, especially in high-risk individuals and European populations [9].”
Jiang, Y., Ma, R., Guo, H., Zhang, X., Wang, X., Wang, K., Hu, Y., Keerman, M., Yan, Y., Ma, J., et al. (2020) · BMC Public Health · doi:10.1186/s12889-020-09579-4 · Cited by 33 · see citing works
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Wekesah, F. M., Mutua, M. K., Boateng, D., Grobbee, D. E., Asiki, G., Kyobutungi, C. K., & Klipstein‐Grobusch, K. (2020) · IJC Heart & Vasculature · doi:10.1016/j.ijcha.2020.100521 · Cited by 26 · see citing works
Damen, J. A., Debray, T. P. A., Pajouheshnia, R., Reitsma, J. B., Scholten, R. J. P. M., Moons, K. G. M., & Hooft, L. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-026160 · Cited by 45 · see citing works
Self-citationTailoring Risk Prediction Models to Local Populations
Zinzuwadia, A. N., Mineeva, O., Li, C., Farukhi, Z. M., Giulianini, F., Cade, B. E., Chen, L., Karlson, E. W., Paynter, N. P., Mora, S., et al. (2024) · JAMA Cardiology · doi:10.1001/jamacardio.2024.2912 · Cited by 29 · see citing works
Safari, A., Adibi, A., Sin, D. D., Lee, T. Y., Ho, J. K., Sadatsafavi, M., & IMPACT Study Team (2022) · EClinicalMedicine · doi:10.1016/j.eclinm.2022.101574 · Cited by 34 · see citing works
Mathew, R. O., Khan, S. S., Tuttle, K. R., Ho, J. E., Abramov, D., Bangalore, S., Sidhu, M. S., Ndumele, C. E., Powell‐Wiley, T. M., Neeland, I. J., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03789-2 · Cited by 23 · see citing works
“A prior meta-analysis of operational characteristics of the Framingham heart model and PCEs demonstrated wide variability across cohorts evaluated, with calibration slopes varying from 0.3 to 1.4 and C-statistics from 0.6 in men to 0.77 in women 18 .”
InfluentialNg, R., Sutradhar, R., Kornas, K., Wodchis, W. P., Sarkar, J., Fransoo, R., & Rosella, L. C. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.4669 · Cited by 30 · see citing works
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“28 These risk equations are also applied in cost effectiveness modelling for healthcare coverage evaluations, such as by the Institute for Clinical and Economic Review (ICER).”
Stabellini, N., Cullen, J., Moore, J. X., Dent, S. F., Sutton, A. L., Shanahan, J., Montero, A. J., & Guha, A. (2023) · Cancers · doi:10.3390/cancers15184630 · Cited by 25 · see citing works
Prediction performance and fairness heterogeneity in cardiovascular risk models
Kartoun, U., Khurshid, S. S., Kwon, B. C., Patel, A. P., Batra, P., Philippakis, A., Khera, A. V., Ellinor, P. T., Lubitz, S. A., & Ng, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-16615-3 · Cited by 22 · see citing works
Tejera, C. H., Minnier, J., Fazio, S., Safford, M. M., Colantonio, L. D., Irvin, M. R., Howard, V. J., Zakai, N. A., & Pamir, N. (2021) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2021.100198 · Cited by 23 · see citing works
Svinin, G. Е., Kutsenko, V. A., Shalnova, S. A., Yarovaya, E. B., Имаева, А. Э., Balanova, Y. А., Владимировна, К. А., Аркадьевна, М. Г., & Drapkina, O. M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300974 · Cited by 22 · see citing works
Wallisch, C., Heinze, G., Rinner, C., Mundigler, G., Winkelmayer, W., & Dunkler, D. (2020) · Scientific Reports · doi:10.1038/s41598-020-64629-6 · Cited by 20
Risk stratification of metabolic disorder–associated kidney disease
Xu, X. P., Shao, X., & Hou, F. F. (2025) · Kidney International · doi:10.1016/j.kint.2025.01.041 · Cited by 16 · see citing works
Deawjaroen, K., Sillabutra, J., Poolsup, N., Stewart, D. C., & Suksomboon, N. (2021) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15104 · Cited by 22 · see citing works
“Currently, many prediction tools have been developed to aid patient screening and prioritization in specific areas, such as fall risk assessments,(29) suicide risk assessment tools(30) and the Framingham risk score.(31) In pharmaceutical practice, prediction tools to identify patients at risk of adverse medication-rel…”
BackgroundAssessment of QRISK3 as a predictor of cardiovascular disease events in type 2 diabetes mellitus
Mu, X., Wu, A., Hu, H., Zhou, H., & Yang, M. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1077632 · Cited by 17 · see citing works
Panagiotakos, D. B., Chrysohoou, C. A., Pitsavos, C., & Tsioufis, K. P. (2023) · Hellenic Journal of Cardiology · doi:10.1016/j.hjc.2023.10.001 · Cited by 17 · see citing works
Matyas, B., Benedek, I., Raț, N., Gerculy, R., & Benedek, T. (2026) · Medicina · doi:10.3390/medicina62040630 · Cited by 13 · see citing works
Yu, J., Yang, X., Deng, Y., Krefman, A. E., Pool, L. R., Zhao, L., Mi, X., Ning, H., Wilkins, J. T., Lloyd‐Jones, D. M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-51685-5 · Cited by 22 · see citing works
Dehghan, A., Rezaei, F., & Aune, D. (2023) · Scientific Reports · doi:10.1038/s41598-023-40820-3 · Cited by 12 · see citing works
“Because the levels of CVD predictors vary across populations, CVD risk prediction models that have been developed for specific countries cannot automatically be used in other countries 12, 13 .”
BackgroundWHO cardiovascular disease risk prediction model performance in 10 regions, China
Yang, S.-C., Ding, Y., Yu, C.-Q., Guo, Y., Pang, Y., Sun, D., Pei, P., Yang, L., Chen, Y.-P., Du, H., et al. (2023) · Bulletin of the World Health Organization · doi:10.2471/blt.22.288645 · Cited by 15 · see citing works
“Discrimination: Discrimination performance indicates the ability of the model to distinguish between people who did and did not develop the disease of interest, which is usually assessed by Harrell’s C index.(7)”
MethodInfluentialSex-Specific Survival Bias and Interaction Modeling in Coronary Artery Disease Risk Prediction
Surakka, I. L., Wolford, B. N., Ritchie, S. C., Hornsby, W., Sutton, N. R., Gabrielsen, M. E., Skogholt, A. H., Thomas, L. F., Inouye, M., Hveem, K., et al. (2022) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.121.003542 · Cited by 20 · see citing works
“8), depending on characteristics of the external validation dataset such as age and statin use(7-10).”
BackgroundLee, T.-L., Lin, F.-J., Yeh, C.-F., Hsiao, Y., Yang, K.-C., Hsuan, C.-F., & Wu, C.-C. (2025) · BMC Public Health · doi:10.1186/s12889-025-22944-5 · Cited by 14 · see citing works
“However, the FRS was developed 3 decades ago, and although the model has been updated over these decades the variables and calculations used can be complicated and the results are not always consistent with other models for predicting the risk of developing CVD [18, 19].”
Cheng, C.-H., Lee, B.-J., Nfor, O. N., Hsiao, C.-H., Huang, Y.-C., & Liaw, Y. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02603-2 · Cited by 14 · see citing works
“It has been previously suggested that the Framingham score encompasses a limited number of predictors and may overestimate CVD risk, potentially leading to overtreatment [4, 5].”
BackgroundSerés‐Noriega, T., Perea, V., & Amor, A. J. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13041097 · Cited by 15 · see citing works
Perry, B. I., Vandenberghe, F., Torres, N. G., Osimo, E. F., Piras, M., Vázquez-Bourgón, J., Upthegrove, R., Grosu, C., de la Foz, V. O., Jones, P. B., et al. (2022) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2022.100493 · Cited by 14 · see citing works
Schiborn, C., Kuhn, T. E., Mühlenbruch, K., Kuxhaus, O., Weikert, C., Fritsche, A., Kaaks, R., & Schulze, M. B. (2021) · Scientific Reports · doi:10.1038/s41598-021-99103-4 · Cited by 13 · see citing works
Kumar, S. S., Al-Kindi, S. G., Tashtish, N., Rajagopalan, V., Fu, P., Rajagopalan, S., & Madabhushi, A. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.976769 · Cited by 15 · see citing works
Lim, H., Lui, B., Tacey, M., Selan, C., Donnan, G., Burrell, L., Nandurkar, H., & Ho, P. (2021) · Journal of Thrombosis and Thrombolysis · doi:10.1007/s11239-021-02400-y · Cited by 12
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“Thus, it is recommended that prediction models be adjusted locally before their implementation [20, 24], and recalibration would be the first step of this model adjustment [25].”
BackgroundMethodXiao, E., Yu, R., Cai, X., Jiang, L., Li, J., Ma, C., Liu, Y., Liu, L., Su, G., & Wang, X. (2025) · Lipids in Health and Disease · doi:10.1186/s12944-025-02445-5 · Cited by 8 · see citing works
“Second, the PCE may over-estimate or underestimate CVD risk in specific populations, as it was originally developed using data from the general population [31, 36].”
Zhang, M.-Z., Gharleghi, R., Shen, C., & Beier, S. (2024) · Royal Society Open Science · doi:10.1098/rsos.241267 · Cited by 11 · see citing works
Future burden of myocardial infarction in Australia: impact on health outcomes between 2019 and 2038
Abebe, T. B., Morton, J. I., Ilomäki, J., & Ademi, Z. (2023) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcad062 · Cited by 11 · see citing works
“….’s study 10 mainly derived risk estimates from the 2011/12 National Health Survey 23 and pooled cohort equation, 24 which in many cases has shown to overestimate the risk of developing CVD. 25 Our projection of non-fatal MI was close to incident non-fatal acute coronary syndrome reported by AIHW.”
BackgroundHernández‐Vásquez, A., Vargas‐Fernández, R., & Chacón-Díaz, M. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19073838 · Cited by 11 · see citing works
“The FRS is a common and widely used tool in several studies around the world [19] and has six modifiable and non-modifiable coronary risk factors (age, sex, total cholesterol level, high-density lipoprotein level, smoking, and SBP) [20].”
MethodChun, M., Clarke, R., Zhu, T., Clifton, D. A., Bennett, D., Chen, Y., Guo, Y., Pei, P., Lv, J., Yu, C., et al. (2022) · Stroke and Vascular Neurology · doi:10.1136/svn-2021-001251 · Cited by 10 · see citing works
Xu, W., Huang, J., Yu, Q., Yu, H., Pu, Y., & Shi, Q. (2021) · Neurological Sciences · doi:10.1007/s10072-021-05219-w · Cited by 11 · see citing works
Pinto‐Sietsma, S., Velthuis, B. K., Nurmohamed, N. S., Vliegenthart, R., & Martens, F. M. A. C. (2024) · Netherlands Heart Journal · doi:10.1007/s12471-024-01897-1 · Cited by 11 · see citing works
Bowles, N. P., He, Y., Huang, Y.-H., Stecker, E. C., Seixas, A. A., & Thosar, S. S. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1364730 · Cited by 11 · see citing works
Agyekum, F., Akumiah, F. K., Nguah, S. B., Appiah, L. T., Ganatra, K., Adu‐Boakye, Y., Folson, A. A., Ayetey, H., & Owusu, I. K. (2024) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2024.100670 · Cited by 9 · see citing works
“These tools have been extensively tested in different populations and increasingly demonstrate the need for models tailored to local epidemiological data [14].”
BackgroundNomali, M., Khalili, D., Yaseri, M., Mansournia, M. A., Ayati, A., Navid, H., & Nedjat, S. (2023) · PLoS ONE · doi:10.1371/journal.pone.0292396 · Cited by 10 · see citing works
Mammographic breast density and cardiovascular disease risk in women
Trần, T. X. M., Chang, Y., Kim, S., Ryu, S., & Park, B. (2023) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2023.117392 · Cited by 12 · see citing works
Ahmadijoo, P., Eftekhari, M. H., Masoumi, S. J., Zahedani, M. R., & Mohammadi, F. (2023) · BMC Nutrition · doi:10.1186/s40795-023-00735-8 · Cited by 10 · see citing works
“Multivariate CVDs risk assessment tools are available to identify individuals at risk for effective preventive interventions including drug administration and lifestyle changes [4].”
BackgroundAssessment of plasma ceramides as predictor for subclinical atherosclerosis
Mishra, P. P., Mishra, B., Lyytikäinen, L., Hilvo, M., Juonala, M., Kähönen, M., Hutri-Kähönen, N., Fotiadis, D., Raitakari, O., Laaksonen, R., et al. (2021) · Atherosclerosis Plus · doi:10.1016/j.athplu.2021.09.005 · Cited by 10 · see citing works
Expected Value of Sample Information Calculations for Risk Prediction Model Validation
Sadatsafavi, M., Vickers, A. J., Lee, T., Gustafson, P., & Wynants, L. (2024) · Medical decision making · doi:10.1177/0272989x251314010 · Cited by 9 · see citing works
“Among such metrics, NB is a decision-theoretic one, as it enables direct assessment of the clinical utility of a model (i.e., if a model’s expected NB is higher than alternatives, it is expected to confer clinical utility).”
Osoro, I., Kumar, R., & Sharma, A. (2023) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-023-01170-2 · Cited by 12 · see citing works
“Various CVD prediction models have been developed worldwide with the Framingham risk score model being the most commonly used [7–9].”
MethodReplication of a predictive model for youth ADHD in an independent sample from a developing country.
Lorenzi, C., Leffa, D. T., Bressan, R., Belangero, S., Gadelha, A., Santoro, M., Salum, G., Rohde, L., & Caye, A. (2022) · Journal of Child Psychology and Psychiatry and Allied Disciplines · doi:10.1111/jcpp.13682 · Cited by 9 · see citing works
Lidgard, B., Zelnick, L. R., Go, A., O'Brien, K., Bansal, N., Appel, L., Chen, J., Cohen, D. L., Feldman, H., Lash, J., et al. (2022) · Journal of the American Heart Association : Cardiovascular and Cerebrovascular Disease · doi:10.1161/jaha.121.024913 · Cited by 9 · see citing works
Nawi, C. M. N. H. C., Omar, M., Keegan, T., Yu, Y., & Musa, K. (2022) · Medicina · doi:10.3390/medicina58050656 · Cited by 9
Kimbrel, N. A., Beckham, J. C., Calhoun, P. S., DeBeer, B. B., Keane, T. M., Lee, D. J., Marx, B. P., Meyer, E. C., Morissette, S. B., & Elbogen, E. B. (2021) · PLoS Medicine · doi:10.1371/journal.pmed.1003713 · Cited by 9 · see citing works
Momayyezi, M., Sefidkar, R., & Fallahzadeh, H. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e20396 · Cited by 8
Bufalino, V. J., Bleser, W. K., Singletary, E. A., Granger, B. B., O'Brien, E. C., Elkind, M. S. V., Lopez, M. H., Saunders, R. S., McClellan, M., & Brown, N. (2020) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.120.006780 · Cited by 7 · see citing works
Coronary artery calcification: More than meets the eye
Huang, X., D’Addabbo, J., & Nguyen, P. K. P. (2020) · Journal of Nuclear Cardiology · doi:10.1007/s12350-020-02058-8 · Cited by 7 · see citing works
Geraci, G., Sorce, A., Zanoli, L., Cuttone, G., Calabrese, V., Pallotti, F., Paternò, V., Ferrara, P., Domínguez, L. J., Polosa, R., et al. (2025) · Life · doi:10.3390/life15030401 · Cited by 7 · see citing works
Alemu, M., Alemu, S. M., Bagheri, N., Wangdi, K., & Chateau, D. G. (2025) · Open Heart · doi:10.1136/openhrt-2024-003147 · Cited by 6 · see citing works
AlKetbi, L. B., Nagelkerke, N. J. D., Humaid, A., AlAlawi, N., AlKetbi, R. M., Aleissaee, H., AlShamsi, N., Abdulbaqi, H., Fahmawee, T., AlHashaikeh, B., et al. (2024) · Journal of the American Heart Association · doi:10.1161/jaha.124.035930 · Cited by 11 · see citing works
Quadackers, D. M. C., Liemburg, E. J., Bos, F. M., Doornbos, B., Risselada, A., PHAMOUS investigators, Bartels‐Velthuis, A. A., Bruggeman, R., Castelein, S., Jörg, F., et al. (2023) · BMC Psychiatry · doi:10.1186/s12888-023-05022-1 · Cited by 8 · see citing works
“, with statins, and anti-hypertensive medication) could be partly responsible for this overprediction as well [71].”
BackgroundClarifying the Link Between the Blood Lactate Concentration and Cardiovascular Risk
Ahlgrim, C., Baumstark, M. W., & Röecker, K. (2022) · International Journal of Sports Medicine · doi:10.1055/a-1812-5840 · Cited by 6 · see citing works
“…risk calculation: whilst the Framingham risk score was recommended by the European Society of Cardiology’s Guideline at the time of data acquisition [32], it was recently shown that Framingham risk models might overestimate cardiovascular risk in non-US cohorts and subjects at higher risk [33].”
Schoemaker, D., Velilla-Jiménez, L. M., Zuluaga, Y., Baena, A. Y., Ospina, C., Bocanegra, Y., Álvarez, S., Ochoa‐Escudero, M., Guzmán‐Vélez, E., Martinez, J. E., et al. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210313 · Cited by 7 · see citing works
“The laboratory and non-laboratory-based FCRP scores both show good discrimination and calibration in predicting subsequent cardiovascular events [35, 36] and have been widely used in the literature as a global measure of cardiovascular risks [37, 38].”
Strategies to alter the trajectory of atherosclerotic cardiovascular disease
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Adaptable graph neural networks design to support generalizability for clinical event prediction
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“The specific cut-off point would engender the opportunity to predict the CVD risk more accurately and prevent over-or under-treatment [11].”
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“7, 8 Additionally, current clinical guidelines provide more ambivalent and complex treatment recommendations for those who fall in the borderline (5–7.5”
BackgroundInfluentialReference distributions of aortic calcification and association with Framingham risk score
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Outcomes in CME/CPD - Special Collection: How to make the “pyramid” a perpetuum mobile
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Self-citationGenetic Risk Scores in Stroke Research and Care
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Use of biomarkers of metals to improve prediction performance of cardiovascular disease mortality
Fansler, S. D., Bakulski, K. M., Park, S. K., Walker, E., & Wang, X. (2024) · Environmental Health · doi:10.1186/s12940-024-01137-4 · Cited by 5 · see citing works
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Cardiovascular risk indicators among depressed persons: A special case?
van Zutphen, E. V., Kok, A. A. L., Muller, M., Voshaar, R. O., Rhebergen, D., Huisman, M., & Beekman, A. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.02.092 · Cited by 5 · see citing works
- Authors
- Damen, J. A., Pajouheshnia, R., Heus, P., Moons, K. G. M., Reitsma, J. B., Scholten, R. J. P. M., Hooft, L., & Debray, T. P. A.
- Affiliations
- Utrecht University (NL)
- Corresponding author
- Damen, Johanna A.
- ORCID
- Venue
- BMC Medicine
- Published
- 2019-06-13
- Volume
- 17
- Issue
- 1
- Pages
- 109-109
- ISSN
- 1741-7015 (ISSN-L)
- PMC
- PMC6563379
- OpenAlex
- W2953059279
- Cited by
- 250
- References
- 111
- Citations by year
- 2026: 33 · 2025: 45 · 2024: 37 · 2023: 41 · 2022: 37 · 2021: 27 · 2020: 25 · 2019: 3 · 2018: 1
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- Cochrane Methods Innovation Funds Round 2 (MTH001F)
- Cochrane Trusted methods and Support for Cochrane Reviews of Prognostic studies (-)
- Netherlands Organization for Scientific Research (918.10.615, 91208004, 91617050)
- CREW NHS project (2013T083)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Keywords
- Framingham risk score, Pooled Cohort Equations, cardiovascular risk prediction, coronary heart disease risk, risk overestimation, model calibration, discrimination (statistics), sex differences, population heterogeneity
- MeSH terms
- Aged, Cardiovascular Diseases, Female, Humans, Male, Models, Theoretical, Predictive Value of Tests, Prognosis, Risk Factors, Cohort Studies, Risk Assessment
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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