A novel machine-learning algorithm for predicting mortality risk after hip fracture surgery

INTRODUCTION: Although several risk stratification models have been developed to predict hip fracture mortality, efforts are still being placed in this area. Our aim is to (1) construct a risk prediction model for long-term mortality after hip fracture utilizing the RSF method and (2) to evaluate the changing effects over time of individual pre- and post-treatment variables on predicting mortality. METHODS: 1330 hip fracture surgical patients were included. Forty-five admission and in-hospital variables were analyzed as potential predictors of all-cause mortality. A random survival forest (RSF) algorithm was applied in predictors identification. Cox regression models were then constructed. Sensitivity analyses and internal validation were performed to assess the performance of each model. C statistics were calculated and model calibrations were further assessed. RESULTS: Our machine-learning RSF algorithm achieved a c statistic of 0.83 for 30-day prediction and 0.75 for 1-year mortality. Additionally, a COX model was also constructed by using the variables selected by RSF, c statistics were shown as 0.75 and 0.72 when applying in 2-year and 4-year mortality prediction. The presence of post-operative complications remained as the strongest risk factor for both short- and long-term mortality. Variables including fracture location, high serum creatinine, age, hypertension, anemia, ASA, hypoproteinemia, abnormal BUN, and RDW became more important as the length of follow-up increased. CONCLUSION: The RSF machine-learning algorithm represents a novel approach to identify important risk factors and a risk stratification models for patients undergoing hip fracture surgery is built through this approach to identify those at high risk of long-term mortality.