Prediction models for cardiovascular disease risk in the general population: systematic review
Open accessAbstract
OBJECTIVE: To provide an overview of prediction models for risk of cardiovascular disease (CVD) in the general population. DESIGN: Systematic review. DATA SOURCES: Medline and Embase until June 2013. ELIGIBILITY CRITERIA FOR STUDY SELECTION: Studies describing the development or external validation of a multivariable model for predicting CVD risk in the general population. RESULTS: 9965 references were screened, of which 212 articles were included in the review, describing the development of 363 prediction models and 473 external validations. Most models were developed in Europe (n=167, 46%), predicted risk of fatal or non-fatal coronary heart disease (n=118, 33%) over a 10 year period (n=209, 58%). The most common predictors were smoking (n=325, 90%) and age (n=321, 88%), and most models were sex specific (n=250, 69%). Substantial heterogeneity in predictor and outcome definitions was observed between models, and important clinical and methodological information were often missing. The prediction horizon was not specified for 49 models (13%), and for 92 (25%) crucial information was missing to enable the model to be used for individual risk prediction. Only 132 developed models (36%) were externally validated and only 70 (19%) by independent investigators. Model performance was heterogeneous and measures such as discrimination and calibration were reported for only 65% and 58% of the external validations, respectively. CONCLUSIONS: There is an excess of models predicting incident CVD in the general population. The usefulness of most of the models remains unclear owing to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies. Rather than developing yet another similar CVD risk prediction model, in this era of large datasets, future research should focus on externally validating and comparing head-to-head promising CVD risk models that already exist, on tailoring or even combining these models to local settings, and investigating whether these models can be extended by addition of new predictors.
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
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?
A review of prediction models for cardiovascular disease identified 363 models and concluded that the usefulness of most models remains unclear owing to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies.
“a review of prediction models for cardiovascular disease identified 363 models19; the authors concluded that “the usefulness of most of the models remains unclear owing to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies.””
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [19]
Published prediction modelling studies often have severe methodological limitations that undermine their usefulness.
“Empirical evidence suggests that published studies often have severe methodological limitations, which undermine their usefulness.”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [19]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Yaşlı Kadınlarda Üreme Sağlığı
Kokanalı, D., & Engin Üstün, Y. (n.d.) · DergiPark (Istanbul University) · Cited by 170,090 · see citing works
Detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III)
Grundy, S. M., Becker, D. M., Clark, L. T., Cooper, R., Denke, M. A., Howard, J. L., Hunninghake, D. B., Illingworth, D. R., Luepker, R. V., McBride, P. E., et al. (2002) · Civil War Book Review · Cited by 12,198 · see citing works
A simulation study of the number of events per variable in logistic regression analysis
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00236-3 · Cited by 9,655 · see citing works
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 · see citing works
General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008) · Circulation · doi:10.1161/circulationaha.107.699579 · Cited by 7,491 · see citing works
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 · see citing works
Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures
Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., & Kattan, M. W. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181c30fb2 · Cited by 5,170 · see citing works
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147 · see citing works
Self-citation2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk
Goff, D. C., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’Agostino, R. B., Gibbons, R., Greenland, P., Lackland, D. T., Levy, D., O’Donnell, C. J., et al. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.11.005 · Cited by 4,945 · see citing works
Goff, D. C., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’Agostino, R. B., Gibbons, R., Greenland, P., Lackland, D. T., Levy, D., O’Donnell, C. J., et al. (2014) · Circulation · doi:10.1161/01.cir.0000437741.48606.98 · Cited by 3,697 · see citing works
Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · British Journal of Cancer · doi:10.1038/bjc.2014.639 · Cited by 3,312 · see citing works
Self-citationCollins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0697 · Cited by 3,267 · see citing works
Self-citationAntlanger, M., Aschauer, S., Kopecky, C., Hecking, M., Kovarik, J. J., Werzowa, J. M., Mascherbauer, J., Genser, B., Säemann, M. D., & Bonderman, D. (2017) · Kidney & Blood Pressure Research · doi:10.1159/000473868 · Cited by 2,808 · see citing works
Eckel, R. H., Jakicic, J. M., Ard, J. D., de Jesus, J. M., Miller, N. H., Hubbard, V. S., Lee, I.-M., Lichtenstein, A. H., Loria, C. M., Millen, B. E., et al. (2014) · Circulation · doi:10.1161/01.cir.0000437740.48606.d1 · Cited by 2,261 · see citing works
Cardiovascular disease risk profiles
Anderson, K. M., Odell, P. M., Wilson, P. W. F., & Kannel, W. B. (1991) · American Heart Journal · doi:10.1016/0002-8703(91)90861-b · Cited by 2,163 · see citing works
Peduzzi, P., Concato, J., Feinstein, A. R., & Holford, T. R. (1995) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(95)00048-8 · Cited by 2,154 · see citing works
Moons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018 · see citing works
Self-citationTowards better clinical prediction models: seven steps for development and an ABCD for validation
Steyerberg, E. W., & Vergouwe, Y. (2014) · European Heart Journal · doi:10.1093/eurheartj/ehu207 · Cited by 1,928 · see citing works
An updated coronary risk profile. A statement for health professionals.
Anderson, K. M., Wilson, P. W., Odell, P. M., & Kannel, W. B. (1991) · Circulation · doi:10.1161/01.cir.83.1.356 · Cited by 1,834 · see citing works
Probability of stroke: a risk profile from the Framingham Study.
Wolf, P. A., D'Agostino, R. B., Belanger, A. J., & Kannel, W. B. (1991) · Stroke · doi:10.1161/01.str.22.3.312 · Cited by 1,782 · see citing works
Assmann, G., Cullen, P., & Schulte, H. (2002) · Circulation · doi:10.1161/hc0302.102575 · Cited by 1,541 · see citing works
Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research
Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., Schroter, S., Riley, R. D., Hemingway, H., Altman, D. G., & for the PROGRESS Group (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001381 · Cited by 1,508 · see citing works
Self-citationPredicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2
Hippisley-Cox, J., Coupland, C., Vinogradova, Y., Robson, J., Minhas, R., Sheikh, A., & Brindle, P. (2008) · BMJ · doi:10.1136/bmj.39609.449676.25 · Cited by 1,459 · see citing works
Prognosis and prognostic research: validating a prognostic model
Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009) · BMJ · doi:10.1136/bmj.b605 · Cited by 1,436 · see citing works
Self-citationPrognosis and prognostic research: what, why, and how?
Moons, K. G. M., Royston, P., Vergouwe, Y., Grobbee, D. E., & Altman, D. G. (2009) · BMJ · doi:10.1136/bmj.b375 · Cited by 1,337 · see citing works
Self-citationReducing waste from incomplete or unusable reports of biomedical research
Glasziou, P., Altman, D. G., Bossuyt, P., Boutron, I., Clarke, M., Julious, S., Michie, S., Moher, D., & Wager, E. (2014) · The Lancet · doi:10.1016/s0140-6736(13)62228-x · Cited by 1,319 · see citing works
Risk prediction models: II. External validation, model updating, and impact assessment
Moons, K. G. M., Kengne, A. P., Grobbee, D. E., Royston, P., Vergouwe, Y., Altman, D. G., & Woodward, M. (2012) · Heart · doi:10.1136/heartjnl-2011-301247 · Cited by 1,195 · see citing works
Self-citationPrognosis and prognostic research: Developing a prognostic model
Royston, P., Moons, K. G. M., Altman, D. G., & Vergouwe, Y. (2009) · BMJ · doi:10.1136/bmj.b604 · Cited by 1,172 · see citing works
Self-citationHippisley-Cox, J., Coupland, C., Vinogradova, Y., Robson, J., May, M., & Brindle, P. (2007) · British medical journal · doi:10.1136/bmj.39261.471806.55 · Cited by 1,070 · see citing works
Prognosis and prognostic research: application and impact of prognostic models in clinical practice
Moons, K. G. M., Altman, D. G., Vergouwe, Y., & Royston, P. (2009) · BMJ · doi:10.1136/bmj.b606 · Cited by 934 · see citing works
Self-citationExternal validation of a Cox prognostic model: principles and methods
Royston, P. J., & Altman, D. G. (2013) · BMC Medical Research Methodology · doi:10.1186/1471-2288-13-33 · Cited by 911 · see citing works
Moons, K. G. M., Kengne, A. P., Woodward, M., Royston, P., Vergouwe, Y., Altman, D. G., & Grobbee, D. E. (2012) · Heart · doi:10.1136/heartjnl-2011-301246 · Cited by 889 · see citing works
Self-citationWoodward, M., Brindle, P., & Tunstall-Pedoe, H. (2007) · Heart · doi:10.1136/hrt.2006.108167 · Cited by 667 · see citing works
Reporting and Methods in Clinical Prediction Research: A Systematic Review
Bouwmeester, W., Zuithoff, N. P. A., Mallett, S., Geerlings, M. I., Vergouwe, Y., Steyerberg, E. W., Altman, D. G., & Moons, K. G. M. (2012) · PLoS Medicine · doi:10.1371/journal.pmed.1001221 · Cited by 600 · cited 1× · see citing works
“15 28 29-31 The full list of extracted items is available on request.”
Self-citationDebray, T. P. A., Vergouwe, Y., Koffijberg, H., Nieboer, D., Steyerberg, E. W., & Moons, K. G. M. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2014.06.018 · Cited by 597 · see citing works
Self-citationSteyerberg, E. W., Borsboom, G. J. J. M., van Houwelingen, H. C., Eijkemans, M. J. C., & Habbema, J. D. F. (2004) · Statistics in Medicine · doi:10.1002/sim.1844 · Cited by 552 · see citing works
Collins, G. S., Mallett, S., Omar, O., & Yu, L.-M. (2011) · BMC Medicine · doi:10.1186/1741-7015-9-103 · Cited by 484 · see citing works
Self-citationHippisley-Cox, J., Coupland, C., Robson, J., & Brindle, P. (2010) · British medical journal · doi:10.1136/bmj.c6624 · Cited by 366 · see citing works
Abbasi, A., Peelen, L. M., Corpeleijn, E., van der Schouw, Y. T., Stolk, R. P., Spijkerman, A. M. W., van der A, D. L., Moons, K. G. M., Navis, G., Bakker, S. J. L., et al. (2012) · BMJ · doi:10.1136/bmj.e5900 · Cited by 321 · see citing works
Self-citationCoronary heart disease risk prediction in the Atherosclerosis Risk in Communities (ARIC) study
Chambless, L. E., Folsom, A. R., Sharrett, A. R., Sorlie, P. D., Couper, D. J., Szklo, M., & Nieto, F. J. (2003) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(03)00055-6 · Cited by 305 · see citing works
Comparisons 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 · see citing works
Self-citationHajifathalian, K., Ueda, P., Lu, Y., Woodward, M., Ahmadvand, A., Aguilar-Salinas, C. A., Azizi, F., Cifkova, R., Di Cesare, M., Eriksen, L., et al. (2015) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00081-9 · Cited by 287 · see citing works
Probability of Middle-Aged Men Developing Coronary Heart Disease in Five Years
Keys, A. B., Aravanis, C., BLACKBURN, H. W., et al. (1972) · Circulation · doi:10.1161/01.cir.45.4.815 · Cited by 259 · see citing works
Estimation of 10-Year Risk of Fatal and Nonfatal Ischemic Cardiovascular Diseases in Chinese Adults
Wu, Y., Liu, X., Li, X., Li, Y., Zhao, L., Chen, Z., Li, Y., Rao, X., Zhou, B., Detrano, R., et al. (2006) · Circulation · doi:10.1161/circulationaha.105.607499 · Cited by 247 · see citing works
Assessing the incremental value of diagnostic and prognostic markers: a review and illustration
Steyerberg, E. W., Pencina, M. J., Lingsma, H. F., Kattan, M. W., Vickers, A. J., & Van Calster, B. (2012) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2011.02562.x · Cited by 219 · see citing works
van Dieren, S., Beulens, J. W. J., Kengne, A. P., Peelen, L. M., Rutten, G. E. H. M., Woodward, M., van der Schouw, Y. T., & Moons, K. G. M. (2011) · Heart · doi:10.1136/heartjnl-2011-300734 · Cited by 217 · see citing works
Self-citationDebray, T. P. A., Moons, K. G. M., Ahmed, I., Koffijberg, H., & Riley, R. D. (2013) · Statistics in Medicine · doi:10.1002/sim.5732 · Cited by 215 · see citing works
Self-citationGijsberts, C. M., Groenewegen, K. A., Hoefer, I. E., Eijkemans, M. J. C., Asselbergs, F. W., Anderson, T. J., Britton, A. R., Dekker, J. M., Engström, G., Evans, G. W., et al. (2015) · PLOS ONE · doi:10.1371/journal.pone.0132321 · Cited by 197 · see citing works
Cardiovascular risk prediction tools for populations in Asia
Barzi, F., Patel, A., Gu, D., Sritara, P., Lam, T., Rodgers, A., & Woodward, M. (2007) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2005.044842 · Cited by 192 · see citing works
Collins, G. S., Omar, O., Shanyinde, M., & Yu, L.-M. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2012.06.020 · Cited by 192 · see citing works
Self-citationStratifying the patient at risk from coronary disease: New insights from the framingham heart study
Levy, D. A., Wilson, P. W. F., Anderson, K. M., & Castelli, W. P. (1990) · American Heart Journal · doi:10.1016/s0002-8703(05)80050-x · Cited by 157 · see citing works
Leaverton, P. E., Sorlie, P. D., Kleinman, J. C., Dannenberg, A. L., Ingster, L. M., Kannel, W. B., & Cornoni‐Huntley, J. C. (1987) · Journal of Chronic Diseases · doi:10.1016/0021-9681(87)90129-9 · Cited by 146 · see citing works
Ettema, R. G. A., Peelen, L. M., Schuurmans, M. J., Nierich, A. P., Kalkman, C. J., & Moons, K. G. M. (2010) · Circulation · doi:10.1161/circulationaha.109.926808 · Cited by 130 · see citing works
Self-citationDebray, T. P. A., Riley, R. D., Rovers, M. M., Reitsma, J. B., Moons, K. G. M., & Cochrane IPD Meta-analysis Methods group (2015) · PLoS Medicine · doi:10.1371/journal.pmed.1001886 · Cited by 130 · see citing works
Self-citationRoyston, P. J., Parmar, M., & Sylvester, R. J. (2004) · Statistics in Medicine · doi:10.1002/sim.1691 · Cited by 129 · see citing works
Friedland, D. R., Cederberg, C. A., & Tarima, S. S. (2009) · The Laryngoscope · doi:10.1002/lary.20130 · Cited by 112 · see citing works
Comparing risk prediction models
Collins, G. S., & Moons, K. G. M. (2012) · BMJ · doi:10.1136/bmj.e3186 · Cited by 110 · see citing works
Self-citationClinical Prediction Models for Cardiovascular Disease
Wessler, B. S., Lana Lai, Y. H., Kramer, W., Cangelosi, M. J., Raman, G., Lutz, J. S., & Kent, D. M. (2015) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.115.001693 · Cited by 106 · see citing works
A Prescription for Improving Drug Formulary Decision Making
Schiff, G. D., Galanter, W., Duhig, J., Koronkowski, M. J., Lodolce, A. E., Pontikes, P., Busker, J., Touchette, D. R., Walton, S. M., & Lambert, B. L. (2012) · PLoS Medicine · doi:10.1371/journal.pmed.1001220 · Cited by 87 · see citing works
Usher‐Smith, J. A., Šilarova, B., Schuit, E., Moons, K. G. M., & Griffin, S. J. (2015) · BMJ Open · doi:10.1136/bmjopen-2015-008717 · Cited by 78 · see citing works
Self-citationSnell, K. I. E., Hua, H., Debray, T. P. A., Ensor, J., Look, M. P., Moons, K. G. M., & Riley, R. D. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.05.009 · Cited by 75 · see citing works
Self-citationMeta‐analysis and aggregation of multiple published prediction models
Debray, T. P. A., Koffijberg, H., Nieboer, D., Vergouwe, Y., Steyerberg, E. W., & Moons, K. G. M. (2014) · Statistics in Medicine · doi:10.1002/sim.6080 · Cited by 73 · see citing works
Self-citationDebray, T. P. A., Koffijberg, H., Vergouwe, Y., Moons, K., & Steyerberg, E. (2012) · Statistics in Medicine · doi:10.1002/sim.5412 · Cited by 66 · see citing works
Self-citationDynamic prediction by landmarking in competing risks
Nicolaie, M. A., van Houwelingen, H. C., de Witte, T. M., et al. (2012) · Statistics in Medicine · doi:10.1002/sim.5665 · Cited by 64 · see citing works
A Naive Bayes machine learning approach to risk prediction using censored, time‐to‐event data
Wolfson, J. A., Bandyopadhyay, S., Elidrisi, M., Vazquez‐Benitez, G., Vock, D. M., Musgrove, D. R., Adomavičius, G., Johnson, P. E., & O’Connor, P. J. (2015) · Statistics in Medicine · doi:10.1002/sim.6526 · Cited by 58 · see citing works
Klaveren, D. V., Vergouwe, Y., Farooq, V., Serruys, P. W. J. C., & Steyerberg, E. W. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.02.012 · Cited by 47 · see citing works
Beswick, A. D., Brindle, P., Fahey, T. P., & Ebrahim, S. B. J. (2008) · Europe PMC (PubMed Central) · Cited by 46 · see citing works
A new strategy for meta‐analysis of continuous covariates in observational studies
Sauerbrei, W., & Royston, P. J. (2011) · Statistics in Medicine · doi:10.1002/sim.4333 · Cited by 41 · see citing works
Machine learning derived risk prediction of anorexia nervosa
Guo, Y., Wei, Z., Keating, B. J., & Hakonarson, H. H. (2015) · BMC Medical Genomics · doi:10.1186/s12920-016-0165-x · Cited by 30 · see citing works
Chien, K., Hsu, H., Su, T., Chang, W., Chen, P., Sung, F., Lin, H., Chen, M.-F., & Lee, Y. (2012) · International Journal of Cardiology · doi:10.1016/j.ijcard.2012.03.017 · Cited by 26 · see citing works
Teramukai, S., Okuda, Y., Miyazaki, S., Kawamori, R., Shirayama, M., & Teramoto, T. (2015) · Hypertension Research · doi:10.1038/hr.2015.120 · Cited by 24 · see citing works
The CROWN Initiative: journal editors invite researchers to develop core outcomes in women's health
Khan, K. S. (2014) · Contraception · doi:10.1016/j.contraception.2014.07.002 · Cited by 19 · see citing works
The CROWN Initiative: journal editors invite researchers to develop core outcomes in women’s health
Khan, K. S., & Romero, R. J. (2014) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2014.09.015 · Cited by 18 · see citing works
Harrell, F., Jr. (1996) · doi:10.1002/(sici)1097-0258(19960229)15:4
Flagged first, then by citations
Research works printed but not linked: 2
- [11]Lipid modification: cardiovascular risk assessment and the modification of blood lipids for the primary and secondary prevention of cardiovascular disease. NICE guidelines [CG181], National Institute for Health and Clinical Excellence 2014. https://www.nice.org.uk/guidance/cg181. 25340243
- [13]Matheny M, McPheeters ML, Glasser A, et al. Systematic Review of Cardiovascular Disease Risk Assessment Tools Rockville MD, 2011. http://www.ncbi.nlm.nih.gov/books/NBK56166/. 21796824
Cited by
Sorted by citations
Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-078378 · Cited by 4,718 · see citing works
Self-citationPROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984 · see citing works
Self-citationMoons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., et al. (2025) · BMJ · doi:10.1136/bmj-2024-082505 · Cited by 1,069 · see citing works
Self-citationEvaluation of clinical prediction models (part 1): from development to external validation
Collins, G. S., Dhiman, P., Ma, J., Schlussel, M. M., Archer, L., Van Calster, B., Harrell, F. E., Martin, G. P., Moons, K. G. M., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-074819 · Cited by 798 · see citing works
Self-citationReporting of artificial intelligence prediction models
Collins, G. S., & Moons, K. G. M. (2019) · The Lancet · doi:10.1016/s0140-6736(19)30037-6 · Cited by 701 · see citing works
Self-citationBias in artificial intelligence algorithms and recommendations for mitigation
Nazer, L. H., Zatarah, R., Waldrip, S., Ke, J. X. C., Moukheiber, M., Khanna, A. K., Hicklen, R. S., Moukheiber, L., Moukheiber, D., Ma, H., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000278 · Cited by 621 · see citing works
Machine learning in cardiovascular medicine: are we there yet?
Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018) · Heart · doi:10.1136/heartjnl-2017-311198 · Cited by 555 · see citing works
Developing clinical prediction models: a step-by-step guide
Efthimiou, O., Seo, M., Chalkou, K., Debray, T., Egger, M., & Salanti, G. (2024) · BMJ · doi:10.1136/bmj-2023-078276 · Cited by 544 · see citing works
Self-citationAndaur Navarro, C. L., Damen, J. A. A., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., Moons, K. G. M., et al. (2021) · BMJ · doi:10.1136/bmj.n2281 · Cited by 415 · see citing works
Self-citation(2024) · BMJ · doi:10.1136/bmj.q902 · Cited by 416 · see citing works
“7 8 36 The ability to critically evaluate the quality of prediction models and understand their ability to serve well in a particular setting or for a particular use case is therefore of even greater critical importance.”
Early detection of type 2 diabetes mellitus using machine learning-based prediction models
Kopitar, L., Kocbek, P., Cilar, L., Sheikh, A., & Stiglic, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-68771-z · Cited by 369 · see citing works
Sit less and move more for cardiovascular health: emerging insights and opportunities
Dunstan, D. W., Dogra, S., Carter, S. E., & Owen, N. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00547-y · Cited by 333 · see citing works
A 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
“For the same disease, outcome or the target population, often numerous, sometimes hundreds, prediction models have been published.(5) This practice is clearly undesirable for health-care professionals, guideline developers and patients, as it obfuscates which model to use in which context.”
BackgroundSelf-citationCombining modifiable risk factors and risk of dementia: a systematic review and meta-analysis
Peters, R., Booth, A., Rockwood, K., Peters, J., D’Este, C., & Anstey, K. J. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-022846 · Cited by 255 · see citing works
Time to reality check the promises of machine learning-powered precision medicine.
Wilkinson, J., Arnold, K. F., Murray, E. J., van Smeden, M., Carr, K., Sippy, R., de Kamps, M., Beam, A., Konigorski, S., Lippert, C., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30200-4 · Cited by 233 · see citing works
Binuya, M. A. E., Engelhardt, E. G., Schats, W., Schmidt, M. K., & Steyerberg, E. W. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01801-8 · Cited by 246 · see citing works
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. (2019) · BMC Medicine · doi:10.1186/s12916-019-1340-7 · Cited by 250 · see citing works
Self-citationBellou, V., Belbasis, L., Konstantinidis, A. K., Tzoulaki, I., & Evangelou, E. (2019) · BMJ · doi:10.1136/bmj.l5358 · Cited by 197 · see citing works
Self-citationLin, J. S., Evans, C. V., Johnson, E., Redmond, N., Coppola, E. L., & Smith, N. (2018) · JAMA · doi:10.1001/jama.2018.4242 · Cited by 216 · see citing works
Meehan, A. J., Lewis, S. J., Fazel, S., Fusar-Poli, P., Steyerberg, E. W., Stahl, D., & Danese, A. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01528-4 · Cited by 219 · see citing works
Lynam, A. L., Dennis, J. M., Owen, K. R., Oram, R. A., Jones, A. G., Shields, B. M., & Ferrat, L. A. (2020) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-00075-2 · Cited by 142 · see citing works
Dhiman, P., Ma, J., Andaur Navarro, C. L., Speich, B., Bullock, G., Damen, J. A. A., Hooft, L., Kirtley, S., Riley, R. D., Van Calster, B., et al. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01577-x · Cited by 140 · see citing works
Weng, S. F., Vaz, L., Qureshi, N., & Kai, J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0214365 · Cited by 118 · see citing works
Cai, Y., Cai, Y.-Q., Tang, L.-Y., Wang, Y.-H., Gong, M., Jing, T.-C., Li, H.-J., Li-Ling, J., Hu, W., Yin, Z., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03273-7 · Cited by 122 · see citing works
Andaur Navarro, C. L., Damen, J. A. A., van Smeden, M., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., et al. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2022.11.015 · Cited by 121 · see citing works
Self-citationNeuromarkers for Mental Disorders: Harnessing Population Neuroscience
Jollans, L., & Whelan, R. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00242 · Cited by 56 · see citing works
Lucaroni, 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
Postpartum depression: a developed and validated model predicting individual risk in new mothers
Munk-Olsen, T., Liu, X., Madsen, K. B., Kjeldsen, M.-M. Z., Petersen, L. V., Bergink, V., Skalkidou, A., Vigod, S. N., Frokjaer, V. G., Pedersen, C. B., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02190-8 · Cited by 36 · see citing works
Lim, C. G. Y., Chong, C. C. Y., Wong, Y. H. M., Yao, J., Ma, S., Chambers, J. C., Yeo, K. K., Tai, E. S., Tromp, J., van Dam, R. M., et al. (2026) · The Lancet Regional Health - Western Pacific · doi:10.1016/j.lanwpc.2025.101794 · Cited by 3 · see citing works
- Authors
- Damen, J. A. A. G., Hooft, L., Schuit, E., Debray, T. P. A., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C. M., Chiocchia, V., Roberts, C., Schlüssel, M. M., Gerry, S., Black, J. A., Heus, P., van der Schouw, Y. T., Peelen, L. M., & Moons, K. G. M.
- Affiliations
- University Medical Center Utrecht (NL)
- Imperial College London (GB)
- University Hospital of Bern (CH)
- University of Oxford (GB)
- University of Cambridge (GB)
- Corresponding author
- Damen, Johanna A A G
- ORCID
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2016-05-16
- Volume
- 353
- Pages
- i2416
- ISSN
- 1756-1833 (ISSN-L)
- PMC
- PMC4868251
- OpenAlex
- W2399385044
- Cited by
- 935
- References
- 77
- Citations by year
- 2026: 80 · 2025: 108 · 2024: 121 · 2023: 96 · 2022: 126 · 2021: 108 · 2020: 96 · 2019: 68 · 2018: 72 · 2017: 49 · 2016: 11
- FWCI
- 9.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- BMJ (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- Cancer Research UK (16895)
- European Commission (279233)
- ZonMw (91208004, 918.10.615)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (918.10.615)
- Medical Research Council (MC_UU_12015/4, G1100513)
- 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
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- cardiovascular risk prediction, risk prediction model, coronary artery disease, external validation, discrimination and calibration, smoking, age, model heterogeneity, general population
- MeSH terms
- Cardiovascular Diseases, Female, Humans, Male, Models, Theoretical, Predictive Value of Tests, Risk Factors, 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
