Machine Learning–Based Identification and Ranking of Mortality Predictors After Hip Fracture Surgery: An Analysis of the Australian and New Zealand Hip Fracture Registry

BACKGROUND: Hip fractures are a major global health issue with high mortality and morbidity, especially in older adults. One-year mortality post-surgery ranges from 22% to 36%, with many patients never regaining baseline mobility. While several predictors of mortality have been identified, their relative contribution to mortality risk within a unified survival prediction framework remains unclear. This study used machine learning to rank key perioperative predictors of mortality following hip fracture surgery. METHODS: Over 11,000 patients from the Australian and New Zealand Hip Fracture Registry were analyzed. Twenty demographic, clinical, and perioperative variables were assessed using a Random Survival Forest (RSF) model. Model performance was evaluated using the concordance index and Brier scores. Permutation-based feature importance ranked predictors according to their contribution to predictive performance for mortality risk. RESULTS: During the follow-up period (median 630 days), 31% of patients died. The RSF model performed well (test C index: 0.7305). The four most important predictors of mortality were American Society of Anesthesiologists (ASA) grade (importance score: 0.051), pre-existing dementia (0.036), age (0.019), and preadmission walking ability (0.016). Other factors like male sex (0.009) and acute hospital stay (0.006) had weaker associations with mortality prediction in this model. CONCLUSIONS: Machine learning identified ASA grade, dementia, age, and mobility as the top predictors of mortality after hip fracture surgery. RSF modeling offered strong performance and better interpretability than traditional methods. These findings support individualized stratification to inform perioperative discussions and goals-of-care planning in this high-risk population.