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Dr Laure Wynants (L.)

Key publications
Wynants, L., Van Calster, B., Collins, G. S., Riley, R. D., Heinze, G., Schuit, E., Bonten, M. M. J., Dahly, D. L., Damen, J. A. A., Debray, T. P. A., de Jong, V. M. T., De Vos, M., Dhiman, P., Haller, M. C., Harhay, M. O., Henckaerts, L., Heus, P., Kammer, M., Kreuzberger, N., ... van Smeden, M. (2020). Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ, 369, Article m1328. https://doi.org/10.1136/bmj.m1328
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Recent publications
  • Sadatsafavi, M., Vickers, A. J., Lee, T. Y., Gustafson, P., & Wynants, L. (2025). Expected Value of Sample Information Calculations for Risk Prediction Model Validation. Medical Decision Making, 45(3), 232-244. https://doi.org/10.1177/0272989X251314010
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  • Vickers, A. J., Van Calster, B., Wynants, L., & Steyerberg, E. W. (2025). Correction: Decision curve analysis: confidence intervals and hypothesis testing for net benefit (vol 7, 11, 2023). Diagnostic and prognostic research, 9(1), Article 8. https://doi.org/10.1186/s41512-025-00188-6
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  • Moons, 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., Denniston, A. K., Ghassemi, M., Heinze, G., Kengne, A. P., Maier-Hein, L., Liu, X., Logullo, P., McCradden, M. D., Liu, N., ... van Smeden, M. (2025). PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ, 388, Article e082505. https://doi.org/10.1136/bmj-2024-082505
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  • Burvenich, R., Bos, D. A., Lowie, L., Peeters, K., Toelen, J., Wynants, L., & Verbakel, J. Y. (2025). Effectiveness of safety netting approaches for acutely ill children: a network meta-analysis. British Journal of General Practice, 75(751), e90-e97. https://doi.org/10.3399/BJGP.2024.0141
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  • Cox, E. G. M., Meijs, D. A. M., Wynants, L., Sels, J.-W. E. M., Koeze, J., Keus, F., Bos-van Dongen, B., van der Horst, I. C. C., & van Bussel, B. C. T. (2025). "The definition of predictor and outcome variables in mortality prediction models: a scoping review and quality of reporting study". Journal of Clinical Epidemiology, 178, Article 111605. https://doi.org/10.1016/j.jclinepi.2024.111605
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  • Kaul, T., Damen, J. A., Wynants, L., Van Calster, B., van Smeden, M., Hooft, L., & Moons, K. G. (2025). Assessing the quality of prediction models in health care using the Prediction model Risk Of Bias ASsessment Tool (PROBAST): an evaluation of its use and practical application. Journal of Clinical Epidemiology, 181, Article 111732. Advance online publication. https://doi.org/10.1016/j.jclinepi.2025.111732
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  • Wynants, L., Broers, N., Platteel, T., Venekamp, R., Barten, D., Leers, M., Verheij, T., Stassen, P., Cals, J., & de Bont, E. (2024). Development and validation of a risk prediction model for hospital admission in COVID-19 patients presenting to primary care. European Journal of General Practice, 30(1), Article 2339488. https://doi.org/10.1080/13814788.2024.2339488
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  • Damen, J. A. A., Arshi, B., van Smeden, M., Bertagnolio, S., Diaz, J. V., Silva, R., Thwin, S. S., Wynants, L., & Moons, K. G. M. (2024). Validation of prognostic models predicting mortality or ICU admission in patients with COVID-19 in low- and middle-income countries: a global individual participant data meta-analysis. Diagnostic and prognostic research, 8(1), Article 17. https://doi.org/10.1186/s41512-024-00181-5
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  • Meijs, D. A. M., Wynants, L., van Kuijk, S. M. J., Scheeren, C. I. E., Hana, A., Mehagnoul-Schipper, J., Stessel, B., Vander Laenen, M., Cox, E. G. M., Sels, J. W. E. M., Smits, L. J. M., Bickenbach, J., Mesotten, D., van der Horst, I. C. C., Marx, G., van Bussel, B. C. T., CoDaP Investigators, Heijnen, N., Mulder, M., ... Ghossein, C. (2024). Boosting the accuracy of existing models by updating and extending: using a multicenter COVID-19 ICU cohort as a proxy. Scientific Reports, 14(1), Article 26344. https://doi.org/10.1038/s41598-024-70333-6
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  • Albu, E., Van Calster, B., & Wynants, L. (2024). Letter to the editor: The use of fast approximate random forests using subsampling (rfsrc.fast) does not support the conclusion on sample sizes needed for random survival forests. Statistics in Medicine, 43(24), 4778-4780. https://doi.org/10.1002/sim.10204
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Other publications

Wynants, L, Riley, RD, Timmerman, D, Van Calster, B. Random-effects meta-analysis of the clinical utility of tests and prediction models. Statistics in Medicine. 2018; 37: 2034– 2052. https://doi.org/10.1002/sim.7653

Wynants, L, Collins, GS, Van Calster, B. Key steps and common pitfalls in developing and validating risk models. BJOG 2017; 124: 423– 432.  https://doi.org/10.1111/1471-0528.14170