Gupta Puneet, Shen Hong-Jui, Patel Kunj, Guo Rui, Heinz Eric R, Manyam Rameshbabu
Department of Anesthesiology and Critical Care Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.
Indian J Anaesth. 2025 Jun;69(6):606-614. doi: 10.4103/ija.ija_1060_24. Epub 2025 May 14.
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.
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.
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.
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.
股骨干骨折的手术修复在围手术期仍有显著的发病率和死亡率。本研究的目的是评估人工智能（AI）驱动的模型是否可用于预测股骨干骨折手术后30天的死亡率，并使用AI识别死亡的患者风险因素。
这项回顾性研究利用了2015年至2020年国家外科质量改进计划的数据。使用患者临床信息开发并测试了五个AI驱动的模型，以预测手术30天内的死亡率。此外，还确定了表现最佳模型的最重要变量。
共识别出1720例患者，股骨干骨折手术后30天死亡率为3.4%（n = 58）。XGBoost表现出最佳预测性能，曲线下面积（AUC）为0.83，校准截距为-0.03，校准斜率为1.17，Brier评分为0.02。预测的最重要变量是年龄、术前白细胞计数、肌酐、血细胞比容、血小板、血尿素氮和体重指数。
本研究首次在孤立的股骨干骨折患者群体中对AI驱动的模型进行内部验证，以预测手术后30天内的死亡率，表现良好。需要进一步研究开发一个性能优异、经外部验证的AI驱动模型，以便在临床转化前支持麻醉医生和骨科医生进行围手术期风险分层和患者教育。
