The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
Open accessAbstract
Background— Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. Methods and Results— We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions (“general CVD” algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P <0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non–laboratory-based predictors. Conclusions— A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“The predicted 10-year CVD risk score was calculated using the BMI-based sex-specific risk equations (supplementary material online, Methods), which were developed from the Framingham Heart Study by D’Agostino et al.15”
From Tirzepatide compared with semaglutide and 10-year cardiovascular disease risk reduction in obesity: post-hoc analysis of the SURMOUNT-5 trial (2025) · cites this paper as [15]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The CAC score was measured manually by expert readers using the method by Agatston and Janowitz5,6 on dedicated workstations, as reported in the parent FHS17, PROMISE19, and ROMICAT-II20 studies.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [17]
Experimental — accuracy not yet measuredIn the citing paper
“Many risk assessment systems are available and have been comprehensively reviewed, including different Framingham models, 41 Systemic Coronary Risk Estimation (SCORE), 42 ASSIGN (CV risk estimation model from the Scottish Intercollegiate Guidelines Network), 43 Q-Risk, 44 Prospective Cardiovascular Munster Study (PROCAM), 45 Reynolds, 46,47 CUORE, 48 the Pooled Cohort equations 49 and Globorisk. 5”
From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [41]
In the citing paper
“According to a recent large-scale cross-sectional study conducted in Japan on visceral fat area and accumulated risk factors, the average number of obesity-related cardiovascular risk factors (dyslipidemia, high blood pressure, and high blood glucose) was more than 1.0 at 100 cm2 for visceral fat area in both men and women330).”
From Japan Atherosclerosis Society (JAS) guidelines for prevention of atherosclerotic cardiovascular diseases 2017 (2018) · cites this paper as [330]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
Grundy, S. M., Bilheimer, D. W., Chait, A., Clark, L. T., Denke, M. A., Havel, R. J., Hazzard, W. R., Hulley, S. B., Hunninghake, D. B., Kreisberg, R. A., et al. (1993) · JAMA · doi:10.1001/jama.269.23.3015 · Cited by 27,944
Regression Models and Life-Tables
Cox, D. R. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-4380-9_37 · Cited by 26,297
Expert Panel on Detection, E. (2001) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.285.19.2486 · Cited by 26,011
Yusuf, S., Hawken, S., Ôunpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P., Varigos, J., et al. (2004) · The Lancet · doi:10.1016/s0140-6736(04)17018-9 · Cited by 12,026
Prediction of coronary heart disease using risk factor categories
Wilson, P. W. F., D’Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., & Kannel, W. B. (1998) · Circulation · doi:10.1161/01.cir.97.18.1837 · Cited by 9,581
Self-citationPencina, M. J., D' Agostino, R. B., D' Agostino, R. B., & Vasan, R. S. (2008) · Statistics in Medicine · doi:10.1002/sim.2929 · Cited by 6,493
Self-citationLopez, A. D., Mathers, C. D., Ezzati, M., Jamison, D. T., & Murray, C. J. (2006) · The Lancet · doi:10.1016/s0140-6736(06)68770-9 · Cited by 5,630
Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project
Conroy, R. (2003) · European Heart Journal · doi:10.1016/s0195-668x(03)00114-3 · Cited by 5,290
Rosamond, W., Flegal, K., Friday, G., Furie, K., Go, A., Greenlund, K., Haase, N., Ho, M., Howard, V., Kissela, B., et al. (2007) · Circulation · doi:10.1161/circulationaha.106.179918 · Cited by 3,277
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–200 of 1,055
Brethauer, S. A., Aminian, A., Romero-Talamás, H., Batayyah, E., Mackey, J., Kennedy, L., Kashyap, S. R., Kirwan, J. P., Rogula, T., Kroh, M., et al. (2013) · Annals of Surgery · doi:10.1097/sla.0b013e3182a5034b · Cited by 552
Ambrosius, W. T., Sink, K. M., Foy, C. G., Berlowitz, D. R., Cheung, A. K., Cushman, W. C., Fine, L. J., Goff, D. C., Johnson, K., Killeen, A. A., et al. (2014) · Clinical Trials · doi:10.1177/1740774514537404 · Cited by 550
Deep learning in ophthalmology: The technical and clinical considerations
Ting, D. S. W., Peng, L. H., Varadarajan, A. V., Keane, P. A., Burlina, P., Chiang, M. F., Schmetterer, L. F., Pasquale, L. R., Bressler, N. M., Webster, D. R., et al. (2019) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2019.04.003 · Cited by 566
Interpreting Incremental Value of Markers Added to Risk Prediction Models
Pencina, M. J., D'Agostino, R. B., Pencina, K. M., Janssens, A. C. J. W., & Greenland, P. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kws207 · Cited by 506
Self-citationDeFilippis, A. P., Young, R. L., Carrubba, C. J., McEvoy, J. W., Budoff, M. J., Blumenthal, R. S., Kronmal, R. A., McClelland, R. L., Nasir, K., & Blaha, M. J. (2015) · Annals of Internal Medicine · doi:10.7326/m14-1281 · Cited by 534
A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring
Alpert, A., Pickman, Y., Leipold, M., Rosenberg-Hasson, Y., Ji, X., Gaujoux, R., Rabani, H., Starosvetsky, E., Kveler, K., Schaffert, S., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0381-y · Cited by 513
ACCF/AHA 2011 Expert Consensus Document on Hypertension in the Elderly
Aronow, W. S., Fleg, J. L., Pepine, C. J., Artinian, N. T., Bakris, G. L., Brown, A. S., Ferdinand, K. C., Forciea, M. A., Frishman, W. H., Jaigobin, C. S., et al. (2011) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2011.01.008 · Cited by 519
Impact of Lipids on Cardiovascular Health
Ference, B. A., Graham, I., Tokgozoglu, L., & Catapano, A. L. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.06.046 · Cited by 511
Circulating Irisin in Relation to Insulin Resistance and the Metabolic Syndrome
Park, K., Zaichenko, L., Brinkoetter, M. T., Thakkar, B., Sahin-Efe, A., Joung, K. E., Tsoukas, M. A., Geladari, E. V., Huh, J. Y., Dincer, F., et al. (2013) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-2373 · Cited by 513
Blonde, L., Umpierrez, G. E., Reddy, S. S., McGill, J. B., Berga, S. L., Bush, M., Chandrasekaran, S., DeFronzo, R. A., Einhorn, D., Galindo, R. J., et al. (2022) · Endocrine Practice · doi:10.1016/j.eprac.2022.08.002 · Cited by 507
Atherogenic Index of Plasma: Novel Predictive Biomarker for Cardiovascular Illnesses
Fernández-Macías, J. C., Ochoa-Martínez, A. C., Varela-Silva, J. A., & Pérez-Maldonado, I. N. (2019) · Archives of Medical Research · doi:10.1016/j.arcmed.2019.08.009 · Cited by 458
2016 European Guidelines on cardiovascular disease prevention in clinical practice
Piepoli, M. F., Hoes, A. W., Agewall, S., Albus, C., Brotons, C., Catapano, A. L., Cooney, M. T., Corrà, U., Cosyns, B., Deaton, C., et al. (2016) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2016.05.037 · Cited by 502
A Modern Approach to Dyslipidemia
Berberich, A. J., & Hegele, R. A. (2021) · Endocrine Reviews · doi:10.1210/endrev/bnab037 · Cited by 529
Baber, U., Mehran, R., Sartori, S., Schoos, M. M., Sillesen, H., Muntendam, P., Garcia, M. J., Gregson, J., Pocock, S., Falk, E., et al. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.01.017 · Cited by 461
Leung, A. A. C., Nerenberg, K. A., Daskalopoulou, S. S., McBrien, K., Zarnke, K. B., Dasgupta, K., Cloutier, L., Gelfer, M., Lamarre-Cliché, M., Milot, A., et al. (2016) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2016.02.066 · Cited by 473
The Swedish CArdioPulmonary BioImage Study: objectives and design
Bergström, G. M. L., Berglund, G., Blomberg, A., Brandberg, J., Engström, G., Engvall, J. E., Eriksson, M., Fairé, U. D., Flinck, A., HANSSON, M. G., et al. (2015) · Journal of Internal Medicine · doi:10.1111/joim.12384 · Cited by 458
COPD Guidelines: A Review of the 2018 GOLD Report
Mirza, S., Clay, R., Koslow, M., & Scanlon, P. D. (2018) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2018.05.026 · Cited by 456
Value and Limitations of Existing Scores for the Assessment of Cardiovascular Risk
Cooney, M. T., Dudina, A. L., & Graham, I. M. (2009) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2009.07.020 · Cited by 436
Updated Cardiovascular Prevention Guideline of the Brazilian Society of Cardiology - 2019
Précoma, D. B., de Oliveira, G. M. M., Simão, A. F. L., Dutra, Ó. P., Coelho-Filho, O. R., Izar, M. C. D. O., Póvoa, R., Giuliano, I. D. C. B., Filho, A. C. D. A., Machado, C. A., et al. (2019) · Arquivos Brasileiros de Cardiologia · doi:10.5935/abc.20190204 · Cited by 507
ST-segment elevation myocardial infarction
Vogel, B., Claessen, B. E. P. M., Arnold, S. V., Chan, D., Cohen, D. J., Giannitsis, E., Gibson, C. M., Goto, S., Katus, H. A., Kernéis, M., et al. (2019) · Nature Reviews Disease Primers · doi:10.1038/s41572-019-0090-3 · Cited by 465
2013 ESH/ESC Guidelines for the management of arterial hypertension
Mancia, G., Fagard, R. H., Narkiewicz, K., Redón, J., Zanchetti, A., Böhm, M., Christiaens, T. C., Cífková, R., Backer, G. D., Dominiczak, A. F., et al. (2013) · Blood Pressure · doi:10.3109/08037051.2013.812549 · Cited by 430
White matter hyperintensities and imaging patterns of brain ageing in the general population
Habes, M., Erus, G., Toledo, J. B., Zhang, T., Bryan, R. N., Launer, L. J., Rosseel, Y., Janowitz, D., Doshi, J. J., der Auwera, S. V., et al. (2016) · Brain · doi:10.1093/brain/aww008 · Cited by 441
Ascertainment of Individual Risk of Mortality for Patients with Idiopathic Pulmonary Fibrosis
du Bois, R. M., Weycker, D. A., Albera, C., Bradford, W. Z., Costabel, U., Kartashov, A. I., LANCASTER, L. H., Noble, P. W., Raghu, G., Sahn, S. A., et al. (2011) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201011-1790oc · Cited by 428
Pavlou, M., Ambler, G., Seaman, S. R., Iorio, M. D., & Omar, R. (2015) · Statistics in Medicine · doi:10.1002/sim.6782 · Cited by 441
Causes of death and comorbidities in hospitalized patients with COVID-19
Elezkurtaj, S., Greuel, S., Ihlow, J., Michaelis, E. G., Bischoff, P., Kunze, C. A., Sinn, B. V., Gerhold, M., Hauptmann, K., Ingold‐Heppner, B., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-82862-5 · Cited by 422
Time Course of LDL Cholesterol Exposure and Cardiovascular Disease Event Risk
Domanski, M. J., Tian, X., Wu, C. O., Reis, J. P., Dey, A. K., Gu, Y., Zhao, L., Bae, S., Liu, K., Hasan, A. A., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.07.059 · Cited by 411
TNFα in atherosclerosis, myocardial ischemia/reperfusion and heart failure
Kleinbongard, P., Heusch, G., & Schulz, R. (2010) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2010.05.002 · Cited by 415
Drosos, G. C., Vedder, D., Houben, E., Boekel, L., Atzeni, F., Badreh, S., Boumpas, D. T., Brodin, N., Bruce, I. N., González-Gay, M. Á., et al. (2022) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2021-221733 · Cited by 446
Hyperlipidemia in Early Adulthood Increases Long-Term Risk of Coronary Heart Disease
Navar‐Boggan, A. M., Peterson, E. D., D’Agostino, R. B., Neely, B., Sniderman, A. D., & Pencina, M. J. (2015) · Circulation · doi:10.1161/circulationaha.114.012477 · Cited by 412
Self-citationCardiovascular disease in patients with severe mental illness
Nielsen, R. E., Banner, J., & Jensen, S. E. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00463-7 · Cited by 389
Association of Smoking Cessation With Subsequent Risk of Cardiovascular Disease
Duncan, M. S., Freiberg, M. S., Greevy, R. A., Kundu, S., Vasan, R. S., & Tindle, H. A. (2019) · JAMA · doi:10.1001/jama.2019.10298 · Cited by 408
Self-citationFraser, A., Nelson, S. M., Macdonald-Wallis, C., Cherry, L., Butler, E., Sattar, N., & Lawlor, D. A. (2012) · Circulation · doi:10.1161/circulationaha.111.044784 · Cited by 401
Microvascular Function Predicts Cardiovascular Events in Primary Prevention
Anderson, T. J., Charbonneau, F., Title, L. M., Buithieu, J., Rose, M. S., Conradson, H. E., Hildebrand, K., Fung, M., Verma, S., & Lonn, E. (2011) · Circulation · doi:10.1161/circulationaha.110.953653 · Cited by 381
The number of years lived with obesity and the risk of all-cause and cause-specific mortality
Abdullah, A., Wolfe, R. S., Stoelwinder, J. U., de Courten, M., Stevenson, C., Walls, H. L., & Peeters, A. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr018 · Cited by 385
Bushnell, C., Kernan, W. N., Sharrief, A. Z., Chaturvedi, S., Cole, J. W., Cornwell, W. K., Cosby-Gaither, C., Doyle, S., Goldstein, L. B., Lennon, O. C., et al. (2024) · Stroke · doi:10.1161/str.0000000000000475 · Cited by 443
Migraine and cardiovascular disease
BIGAL, M. E., Kurth, T., Santanello, N. C., Buse, D. C., Golden, W., Robbins, M. S., & Lipton, R. B. (2010) · Neurology · doi:10.1212/wnl.0b013e3181d0cc8b · Cited by 370
Predicting the Risk for Dialysis or Death in IgA Nephropathy
Berthoux, F. C., Mohey, H., Laurent, B., Mariat, C., Afiani, A., & Thibaudin, L. (2011) · Journal of the American Society of Nephrology · doi:10.1681/asn.2010040355 · Cited by 361
HDL-targeted therapies: progress, failures and future
Kingwell, B. A., Chapman, M. J., Kontush, A., & Miller, N. E. (2014) · Nature Reviews Drug Discovery · doi:10.1038/nrd4279 · Cited by 354
Kantarjian, H. M., Hughes, T. P., Larson, R. A., Kim, D.-W., Issaragrisil, S., le Coutre, P., Etienne, G., Boquimpani, C., Pasquini, R., Clark, R. E., et al. (2021) · Leukemia · doi:10.1038/s41375-020-01111-2 · Cited by 351
Yano, Y., Reis, J. P., Colangelo, L. A., Shimbo, D., Viera, A. J., Allen, N. B., Gidding, S. S., Bress, A. P., Greenland, P., Muntner, P., et al. (2018) · JAMA · doi:10.1001/jama.2018.13551 · Cited by 365
Pencina, M. J., D’Agostino, R. B., & Demler, O. (2011) · Statistics in Medicine · doi:10.1002/sim.4348 · Cited by 348
Self-citationFischer, K., Kettunen, J., Würtz, P., Haller, T., Havulinna, A. S., Kangas, A. J., Soininen, P., Esko, T., Tammesoo, M.-L., Mägi, R., et al. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001606 · Cited by 344
Predicting Cardiovascular Events in Familial Hypercholesterolemia
de Isla, L. P., Rodrigo Alonso, K., Mata, N., Fernández, C., Muñiz-Grijalvo, O., Díaz-Díaz, J. L., Saltijeral, A., Fuentes, F. J., de Andrés, R., Zambón, D., et al. (2017) · Circulation · doi:10.1161/circulationaha.116.024541 · Cited by 344
Attributable Risk Estimate of Severe Psoriasis on Major Cardiovascular Events
Mehta, N. N., Yu, Y., Pinnelas, R., Krishnamoorthy, P. M., Shin, D. B., Troxel, A. B., & Gelfand, J. M. (2011) · The American Journal of Medicine · doi:10.1016/j.amjmed.2011.03.028 · Cited by 343
Cardiovascular disease and spinal cord injury: Results from a national population health survey
Cragg, J. J., Noonan, V. K., Krassioukov, A., & Borisoff, J. (2013) · Neurology · doi:10.1212/wnl.0b013e3182a1aa68 · Cited by 342
High-density lipoproteins: A consensus statement from the National Lipid Association
Toth, P. P., Barter, P. J., Rosenson, R. S., Boden, W. E., Chapman, M. J., Cuchel, M., D'Agostino, R. B., Davidson, M. H., Davidson, W. S., Heinecke, J. W., et al. (2013) · Journal of Clinical Lipidology · doi:10.1016/j.jacl.2013.08.001 · Cited by 336
Self-citationSources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring
Fine, J., Branan, K. L., Rodriguez, A. J., Boonya-ananta, T., Ajmal, Ramella-Roman, J. C., McShane, M. J., & Coté, G. L. (2021) · Biosensors · doi:10.3390/bios11040126 · Cited by 348
“more closely resembles a chronological age index, as they correlated PPG features to chronological age and not CVD risk [85].”
BackgroundSex Hormones and Sex Chromosomes Cause Sex Differences in the Development of Cardiovascular Diseases
Arnold, A. P., Cassis, L. A., Eghbali, M., Reue, K., & Sandberg, K. L. (2017) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.116.307301 · Cited by 339
Ray, K. K., Haq, I., Bilitou, A., Manu, M. C., Burden, A., Aguíar, C., Arca, M., Connolly, D. L., Eriksson, M., Ferrières, J., et al. (2023) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2023.100624 · Cited by 367
Figueroa, A. L., Abdelbaky, A., Truong, Q. A., Corsini, E. M., MacNabb, M. H., Lavender, Z. R., Lawler, M. A., Grinspoon, S. K., Brady, T. J., Nasir, K., et al. (2013) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2013.08.006 · Cited by 336
Statistical methods for assessment of added usefulness of new biomarkers
Pencina, M. J., D’Agostino, R. B., & Vasan, R. S. (2010) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm.2010.340 · Cited by 335
Self-citationDhingra, R., & Vasan, R. S. (2011) · Medical Clinics of North America · doi:10.1016/j.mcna.2011.11.003 · Cited by 331
Self-citationWalli-Attaei, M., Joseph, P. G., Rosengren, A., Chow, C. K., Rangarajan, S., Lear, S. A., AlHabib, K. F., Davletov, K., Dans, A., Lanas, F. T., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30543-2 · Cited by 349
Cardiovascular Disease Risk Assessment: Insights from Framingham
D’Agostino, R. B., Pencina, M. J., Massaro, J. M., & Coady, S. (2013) · Global Heart · doi:10.1016/j.gheart.2013.01.001 · Cited by 350
Self-citationJones, D. W., Ferdinand, K. C., Taler, S. J., Johnson, H. M., Shimbo, D., Abdalla, M., Altieri, M. M., Bansal, N., Bello, N. A., Bress, A. P., et al. (2025) · Hypertension · doi:10.1161/hyp.0000000000000249 · Cited by 424
Cardiovascular comorbidity in rheumatic diseases
Nurmohamed, M. T., Heslinga, M., & Kitas, G. D. (2015) · Nature Reviews Rheumatology · doi:10.1038/nrrheum.2015.112 · Cited by 338
Valtorta, N. K., Kanaan, M., Gilbody, S., & Hanratty, B. (2018) · European Journal of Preventive Cardiology · doi:10.1177/2047487318792696 · Cited by 323
GAPSS: the Global Anti-Phospholipid Syndrome Score
Sciascia, S., Sanna, G., Murru, V., Roccatello, D., Khamashta, M. A., & Bertolaccini, M. L. (2013) · Rheumatology · doi:10.1093/rheumatology/kes388 · Cited by 318
Leung, A. A. C., Daskalopoulou, S. S., Dasgupta, K., McBrien, K., Butalia, S., Zarnke, K. B., Nerenberg, K. A., Harris, K. C., Nakhla, M. M., Cloutier, L., et al. (2017) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2017.03.005 · Cited by 334
Antihypertensive effects of the flavonoid quercetin
Pérez‐Vizcaíno, F., Duarte, J., Jiménez, R., Santos‐Buelga, C., & Osuna, A. (2009) · Pharmacological Reports · doi:10.1016/s1734-1140(09)70008-8 · Cited by 317
Misuse of DeLong test to compare AUCs for nested models
Demler, O., Pencina, M. J., & D’Agostino, R. B. (2012) · Statistics in Medicine · doi:10.1002/sim.5328 · Cited by 313
Self-citationComparisons of established risk prediction models for cardiovascular disease: systematic review
Siontis, G. C. M., Tzoulaki, I., Siontis, K. C., & Ioannidis, J. P. A. (2012) · BMJ · doi:10.1136/bmj.e3318 · Cited by 300
Systemic Inflammation and Cardiovascular Comorbidity in Psoriasis Patients: Causes and Consequences
Boehncke, W. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.00579 · Cited by 314
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Raghunath, S., Ulloa Cerna, A. E., Jing, L., vanMaanen, D. P., Stough, J., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Stumpe, M. C., Hafez, A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0870-z · Cited by 314
A Community-Based Intervention for Managing Hypertension in Rural South Asia
Jafar, T. H., Gandhi, M., de Silva, H. A., Jehan, I., Naheed, A., Finkelstein, E. A., Turner, E. L., Morisky, D. E., Kasturiratne, A., Khan, A. H., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa1911965 · Cited by 323
Wong, N. D., Budoff, M. J., Ferdinand, K. C., Graham, I. M., Michos, E. D., Reddy, T. K., Shapiro, M. D., & Toth, P. P. (2022) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2022.100335 · Cited by 327
C-Reactive Protein and Reclassification of Cardiovascular Risk in the Framingham Heart Study
Wilson, P. W. F., Pencina, M., Jacques, P., Selhub, J., D'Agostino, R., & O'Donnell, C. J. (2008) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.108.831198 · Cited by 299
Self-citationPolak, J. F., Szklo, M., Kronmal, R. A., Burke, G. L., Shea, S., Zavodni, A. E. H., & O’Leary, D. H. (2013) · Journal of the American Heart Association · doi:10.1161/jaha.113.000087 · Cited by 302
Lloyd-Jones, D. M., Braun, L. T., Ndumele, C. E., Smith, S. C., Sperling, L. S., Virani, S. S., & Blumenthal, R. S. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.11.005 · Cited by 302
Assessing calibration of prognostic risk scores
Crowson, C. S., Atkinson, E. J., & Therneau, T. M. (2013) · Statistical Methods in Medical Research · doi:10.1177/0962280213497434 · Cited by 295
Cooper, S. J., Reynolds, G. P., order):, W. E. C.-A. (. A., Barnes, T., England, E., Haddad, P., Heald, A., Holt, R., Lingford-Hughes, A., Osborn, D., et al. (2016) · Journal of Psychopharmacology · doi:10.1177/0269881116645254 · Cited by 292
Influence of Age and Health Behaviors on Stroke Risk: Lessons from Longitudinal Studies
Kelly‐Hayes, M. (2010) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2010.02915.x · Cited by 311
Magder, L. S., & Petri, M. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kws130 · Cited by 290
Tests of calibration and goodness‐of‐fit in the survival setting
Demler, O., Paynter, N. P., & Cook, N. R. (2015) · Statistics in Medicine · doi:10.1002/sim.6428 · Cited by 287
Association of Healthy Lifestyle With Years Lived Without Major Chronic Diseases
Nyberg, S. T., Singh‐Manoux, A., Pentti, J., Madsen, I. E. H., Sabia, S., Alfredsson, L. S., Bjørner, J. B., Borritz, M., Burr, H., Goldberg, M., et al. (2020) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2020.0618 · Cited by 308
Dorková, Z., Petrášová, D., Molčányiová, A., Popovnakova, M., & Tkáčová, R. (2008) · CHEST Journal · doi:10.1378/chest.08-0556 · Cited by 286
Private predictive analysis on encrypted medical data
Bos, J. W., Lauter, K., & Naehrig, M. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.04.003 · Cited by 283
Individual Prediction of Heart Failure Among Childhood Cancer Survivors
Chow, E. J., Chen, Y., Kremer, L. C. M., Breslow, N. E., Hudson, M. M., Armstrong, G. T., Border, W. L., Feijen, E. A. M., Green, D. M., Meacham, L. R., et al. (2014) · Journal of Clinical Oncology · doi:10.1200/jco.2014.56.1373 · Cited by 281
McGorrian, C., Yusuf, S., Islam, S., Jung, H., Rangarajan, S., Avezum, Á., Prabhakaran, D., Almahmeed, W. A., Rumboldt, Z., Budaj, A., et al. (2010) · European Heart Journal · doi:10.1093/eurheartj/ehq448 · Cited by 279
Nomograms for Risk of Hepatocellular Carcinoma in Patients With Chronic Hepatitis B Virus Infection
Yang, H., Sherman, M., Su, J., Chen, P., Liaw, Y., Iloeje, U. H., & Chen, C. (2010) · Journal of Clinical Oncology · doi:10.1200/jco.2009.27.4456 · Cited by 281
Innovation and challenges of artificial intelligence technology in personalized healthcare
Li, Y.-H., Li, Y., Wei, M.-Y., & Li, G.-Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-70073-7 · Cited by 342
Arts, E., Popa, C. D., den Broeder, A. A., Semb, A. G., Toms, T. E., Kitas, G. D., van Riel, P. L., & Fransen, J. (2014) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2013-204024 · Cited by 279
Cardiac Radiation Dose, Cardiac Disease, and Mortality in Patients With Lung Cancer
Atkins, K. M., Rawal, B., Chaunzwa, T. L., Lamba, N., Bitterman, D. S., Williams, C. L., Kozono, D., Baldini, E. H., Chen, A. B., Nguyen, P. L., et al. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.03.500 · Cited by 281
Greve, S. V., Blicher, M. K., Kruger, R., Sehestedt, T. B., Gram-Kampmann, E.-M., Rasmussen, S. L., Vishram, J. K. K., Boutouyrie, P., Laurent, S., & Olsen, M. H. (2016) · Journal of Hypertension · doi:10.1097/hjh.0000000000000935 · Cited by 276
Timmers, P. R., Mounier, N., Lall, K., Fischer, K., Ning, Z., Feng, X., Bretherick, A. D., Clark, D. W., eQTLGen Consortium, Agbessi, M., et al. (2019) · eLife · doi:10.7554/elife.39856 · Cited by 259
Schwalm, J.-D., McCready, T., López-Jaramillo, P. A., Yusoff, K., Attaran, A., Lamelas, P. M., Camacho, P. A., Majid, F. A., Bangdiwala, S. I., Thabane, L., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31949-x · Cited by 276
The metabolic syndrome – What is it and how should it be managed?
Nilsson, P. M., Tuomilehto, J. O., & Rydén, L. (2019) · European Journal of Preventive Cardiology · doi:10.1177/2047487319886404 · Cited by 293
Apolipoprotein B and Cardiovascular Disease: Biomarker and Potential Therapeutic Target
Behbodikhah, J., Ahmed, S., Elyasi, A., Kasselman, L. J., Leon, J. D., Glass, A. D., & Reiss, A. B. (2021) · Metabolites · doi:10.3390/metabo11100690 · Cited by 299
Colen, C. G., Ramey, D. M., Cooksey, E. C., & Williams, D. R. (2017) · Social Science & Medicine · doi:10.1016/j.socscimed.2017.04.051 · Cited by 267
Alshehry, Z. H., Mundra, P. A., Barlow, C. K., Mellett, N. A., Wong, G., McConville, M. J., Simes, J. R., Tonkin, A., SULLIVAN, D. R., Barnes, E. H., et al. (2016) · Circulation · doi:10.1161/circulationaha.116.023233 · Cited by 270
Crowson, C. S., Matteson, E. L., Roger, V. L., Therneau, T. M., & Gabriel, S. E. (2012) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2012.03.044 · Cited by 268
Cook, N. R., Paynter, N. P., Eaton, C. B., Manson, J. E., Martin, L. W., Robinson, J. G., Rossouw, J. E., Wassertheil-Smoller, S. W., & Ridker, P. M. (2012) · Circulation · doi:10.1161/circulationaha.111.075929 · Cited by 266
Nguyen, T. T., Kosciolek, T., Maldonado, Y., Daly, R. E., Martin, A. S., McDonald, D., Knight, R., & Jeste, D. V. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2018.09.014 · Cited by 260
Effects of Blood Pressure Reduction in Mild Hypertension
Sundström, J., Arima, H., Jackson, R., Turnbull, F., Rahimi, K., Chalmers, J. P., Woodward, M., & Neal, B. C. (2014) · Annals of Internal Medicine · doi:10.7326/m14-0773 · Cited by 261
Karbach, S. H., Croxford, A. L., Oelze, M. W., Schüler, R., Minwegen, D., Wegner, J., Koukes, L., Yogev, N., Nikolaev, A., Reißig, S., et al. (2014) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.114.304108 · Cited by 260
KONES, R. J. (2011) · Drug Design Development and Therapy · doi:10.2147/dddt.s14934 · Cited by 266
Clair, C., Rigotti, N. A., Porneala, B., Fox, C. S., D’Agostino, R. B., Pencina, M. J., & Meigs, J. B. (2013) · JAMA · doi:10.1001/jama.2013.1644 · Cited by 255
Self-citationChu, P., Gotink, R. A., Yeh, G. Y., Goldie, S. J., & Hunink, M. G. M. (2014) · European Journal of Preventive Cardiology · doi:10.1177/2047487314562741 · Cited by 256
Chronic Obstructive Pulmonary Disease and Cardiac Diseases. An Urgent Need for Integrated Care
Roversi, S., Fabbri, L. M., Sin, D. D., Hawkins, N. M., & Agustí, A. G. N. (2016) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201604-0690so · Cited by 254
Metadata
- Authors
- D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B.
- Affiliations
- Boston University (US)
- Venue
- Circulation
- Published
- 2008-02-12
- Volume
- 117
- Issue
- 6
- Pages
- 743-753
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- OpenAlex
- W2148092884
- Cited by
- 7,491
- References
- 52
- Citations by year
- 2026: 491 · 2025: 567 · 2024: 552 · 2023: 511 · 2022: 529 · 2021: 592 · 2020: 562 · 2019: 454 · 2018: 475 · 2017: 452 · 2016: 385 · 2015: 389 · 2014: 391 · 2013: 323 · 2012: 294
- FWCI
- 169.19
- Type
- journal article
- Language
- en
- Update notices
- correction (2008-07-22)
- Open access
- bronze
- doi.org
- Locations
- Circulation (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (n01hc25195-41-0-1)
- National Heart, Lung, and Blood Institute (2K24 HL 04334, N01‐HC25195, K24HL004334)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
- Keywords
- cardiovascular risk prediction, atherosclerotic cardiovascular disease, Framingham risk score, coronary artery disease, stroke, peripheral arterial disease, heart failure, traditional risk factors, cholesterol, blood pressure, smoking, diabetes mellitus, primary care
- MeSH terms
- Adult, Aged, Algorithms, Cardiovascular Diseases, Female, Humans, Longitudinal Studies, Male, Middle Aged, Primary Health Care, Risk Factors, Sex Factors, Multivariate Analysis, Proportional Hazards Models, Risk Assessment
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
