Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients: the Dutch Hip Fracture Audit algorithms in 74,396 cases.

ABSTRACT:

Background and purpose

 Treatment-related shared decision-making (SDM) in older adults with hip fractures is complex due to the need to balance patient-specific factors such as life goals, frailty, and surgical risks. It includes considerations such as prognosis and decisions concerning whether to operate or not on frail, life-limited patients. We aimed to develop machine learning (ML)-driven prediction models for short- and long-term mortality in a large cohort of patients with hip fractures.

Methods

 In this national registry-based retrospective cohort study, patients aged ≥ 70 years registered in the nationwide Dutch Hip Fracture Audit from 2018-2023 were included. Predictive variables were selected based on the literature and/or clinical relevance. 6 ML algorithms, inclu

SUBMITTER: Dijkstra H 

PROVIDER: S-EPMC12231630 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature