Artificial intelligence for predicting 30-day mortality after surgery for femoral shaft fractures: A retrospective study

Abstract

Background and Aims: Surgical repair of femoral shaft fractures continues to have notable perioperative morbidity and mortality. The purpose of this study is to assess whether artificial intelligence (AI)-driven models can be utilised to predict 30-day mortality after surgery for femoral shaft fractures and to identify patient risk factors for mortality using AI. Methods: This retrospective study utilised data from the National Surgical Quality Improvement Program between 2015 and 2020. Five AI-driven models were developed and tested using patient clinical information to predict mortality within 30 days of surgery. Additionally, the most important variables for the best-performing model were identified. Results: A total of 1720 patients were identified, and the 30-day mortality rate after femoral shaft fracture surgery was 3.4% (n = 58). XGBoost demonstrated the best predictive performance, with an area under the curve (AUC) of 0.83, a calibration intercept of -0.03, a calibration slope of 1.17, and a Brier score of 0.02. The most important variables for prediction were age, preoperative white blood cell count, creatinine, haematocrit, platelets, blood urea nitrogen, and body mass index. Conclusion: This study is the first to internally validate an AI-driven model for predicting mortality within 30 days of surgery in an isolated population of femoral shaft fracture patients, demonstrating good performance. Further research is needed to develop an excellent-performing, AI-driven model that is externally validated prior to clinical translation to support anaesthesiologists and orthopaedic surgeons in perioperative risk stratification and patient education.

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