Development of machine-learning algorithms for 90-day and one-year mortality prediction in the elderly with femoral neck fractures based on the HEALTH and FAITH trials

Aug 5, 2026

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Development of machine-learning prediction models was undertaken for 90-day and one-year mortality in elderly patients with femoral neck fractures, using data from the HEALTH and FAITH trials. The study analyzed 2,388 participants and incorporated patient and injury characteristics into six internally validated algorithms, assessing discrimination, calibration, and Brier score. Penalized logistic regression showed the strongest overall performance in both time horizons in the hold-out set. Final models require external validation to confirm generalizability and prospective evaluation for integration into shared decision-making.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Aims

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University of Groningen Development of machine-learning algorithms for 90-day and one-year mortality prediction in the elderly with femoral neck fractures based on the HEALTH and FAITH trials Dijkstra, H. ; Oosterhoff, J. H. F. ; van de Kuit, A. ; Ijpma, F. F.A. ; Schwab, J. H. ; Poolman, R. W. ; Sprague, S. ; Bzovsky, S. ; Bhandari, M. ; Swiontkowski, M. Published in: Bone and Joint Open DOI: 10.1302/2633-1462.43.BJO-2022-0162.R1 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2023 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Dijkstra, H. , Oosterhoff, J. H. F. , van de Kuit, A. , Ijpma, F. F. A. , Schwab, J. H. , Poolman, R. W. , Sprague, S. , Bzovsky, S. , Bhandari, M. , Swiontkowski, M. , Schemitsch, E. H. , Doornberg, J. N. , & Hendrickx, L. A. M.(2023) . Development of machine-learning algorithms for 90-day and one-year mortality prediction in the elderly with femoral neck fractures based on the HEALTH and FAITH trials. Bone and Joint Open, 4(3), 168-181. [https://doi.org/10.1302/2633-1462.43.BJO-2022-0162.R1](https://doi.org/10.1302/2633-1462.43.BJO-2022-0162.R1) Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) . The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.) Download date: 01-01-2026 BJO 􀂄 HIP Development of machine-learning algorithms for 90-day and one-year mortality prediction in the elderly with femoral neck fractures based on the HEALTH and FAITH trials H. Dijkstra, J. H. F. Oosterhoff, A. van deKuit, F. F. A. IJpma, J. H. Schwab, R. W. Poolman, S. Sprague, S. Bzovsky, M. Bhandari, M. Swiontkowski, E. H. Schemitsch, J. N. Doornberg, L. A. M. Hendrickx, On behalf of the Machine Learning Consortium, the HEALTH Investigators, and the FAITH Investigators From University Medical Centre Groningen, Groningen, the Netherlands Correspondence should be sent to Hidde Dijkstra; email: [h.b.dijkstra@umcg.nl](h.b.dijkstra@umcg.nl) doi: 10.1302/2633-1462.43.BJO- 2022-0162.R1 Bone Jt Open 2023;4-3:168–181. Aims To develop prediction models using machine-learning (ML) algorithms for 90-day and oneyear mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials. Methods This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and oneyear mortality proportions of 3.0%(71/2,388) and 6.4%(153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and

FAQs

What was the goal of the study?

To develop machine-learning prediction models for 90-day and one-year mortality in patients aged 50 years or older with femoral neck fractures, using the HEALTH and FAITH trials.

How many patients were included, and what outcomes were modeled?

The study included 2,388 patients and modeled mortality at 90 days and at one year.

Which algorithm performed best and how was performance evaluated?

Penalized logistic regression performed best, evaluated using discrimination (c-statistic), calibration, and the Brier score.

Aug 5, 2026

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