Fracture risk prediction in postmenopausal women with traditional and machine learning models in a nationwide, prospective cohort study in Switzerland with validation in the UK Biobank.

Autor:	Lehmann O; Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland., Mineeva O; Department of Computer Science, ETH Zürich, Zürich, Switzerland., Veshchezerova D; Department of Computer Science, ETH Zürich, Zürich, Switzerland., Häuselmann H; Zentrum für Rheuma- und Knochenerkrankungen, Klinik Im Park, Hirslanden, Zürich, Switzerland., Guyer L; Faculty of Medicine, University of Bern, Bern, Switzerland., Reichenbach S; Institute for Social and Preventive Medicine, University of Bern, Bern, Switzerland.; Department of Rheumatology and Immunology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland., Lehmann T; OsteoRheuma Bern, Bahnhofplatz 1, Bern, Switzerland., Demler O; Department of Computer Science, ETH Zürich, Zürich, Switzerland.; Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States., Everts-Graber J; Department of Rheumatology and Immunology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.; OsteoRheuma Bern, Bahnhofplatz 1, Bern, Switzerland.; Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland.
Jazyk:	angličtina
Zdroj:	Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research [J Bone Miner Res] 2024 Aug 21; Vol. 39 (8), pp. 1103-1112.
DOI:	10.1093/jbmr/zjae089
Abstrakt:	Fracture prediction is essential in managing patients with osteoporosis and is an integral component of many fracture prevention guidelines. We aimed to identify the most relevant clinical fracture risk factors in contemporary populations by training and validating short- and long-term fracture risk prediction models in 2 cohorts. We used traditional and machine learning survival models to predict risks of vertebral, hip, and any fractures on the basis of clinical risk factors, T-scores, and treatment history among participants in a nationwide Swiss Osteoporosis Registry (N = 5944 postmenopausal women, median follow-up of 4.1 yr between January 2015 and October 2022; a total of 1190 fractures during follow-up). The independent validation cohort comprised 5474 postmenopausal women from the UK Biobank with 290 incident fractures during follow-up. Uno's C-index and the time-dependent area under the receiver operating characteristics curve were calculated to evaluate the performance of different machine learning models (Random survival forest and eXtreme Gradient Boosting). In the independent validation set, the C-index was 0.74 [0.58, 0.86] for vertebral fractures, 0.83 [0.7, 0.94] for hip fractures, and 0.63 [0.58, 0.69] for any fractures at year 2, and these values further increased for longer estimations of up to 7 yr. In comparison, the 10-yr fracture probability calculated with FRAX Switzerland was 0.60 [0.55, 0.64] for major osteoporotic fractures and 0.62 [0.49, 0.74] for hip fractures. The most important variables identified with Shapley additive explanations values were age, T-scores, and prior fractures, while number of falls was an important predictor of hip fractures. Performances of both traditional and machine learning models showed similar C-indices. We conclude that fracture risk can be improved by including the lumbar spine T-score, trabecular bone score, numbers of falls and recent fractures, and treatment information has a significant impact on fracture prediction. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society for Bone and Mineral Research. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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