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 de Kuit, 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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Aims To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year 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 Hemi-arthroplasty (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 one-year 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 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, in-ternally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration). Results The developed algorithms distinguished between patients at high and low risk for 90-day and one-year mortality. The penalized logistic regression algorithm had the best performance metrics for both 90-day (c-statistic 0.80, calibration slope 0.95, calibration intercept-0.06, and Brier score 0.039) and one-year (c-statistic 0.76, calibration slope 0.86, calibration intercept-0.20, and Brier score 0.074) mortality prediction in the hold-out set. Conclusion Using high-quality data, the ML-based prediction models accurately predicted 90-day and one-year mortality in patients aged 50 years or older with a FNF. The final models must be externally validated to assess generalizability to other populations, and prospectively evaluated in the process of shared decision-making.

Original languageEnglish
Pages (from-to)168-181
Number of pages14
JournalBone and Joint Open
Volume4
Issue number3
DOIs
Publication statusPublished - Mar-2023

Keywords

  • Artificial intelligence
  • Hip fracture
  • Machine learning
  • Prediction models
  • Shared decision-making

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