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

Authors

Puneet Gupta, Department of Anesthesiology and Critical Care Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
Hong-Jui Shen, Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.
Kunj Patel, Department of Anesthesiology and Critical Care Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
Rui Guo, Department of Anesthesiology and Critical Care Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
Eric R. Heinz, Department of Anesthesiology and Critical Care Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
Rameshbabu Manyam, Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.

Document Type

Journal Article

Publication Date

6-1-2025

Journal

Indian journal of anaesthesia

Volume

69

Issue

6

DOI

10.4103/ija.ija_1060_24

Keywords

Artificial intelligence; XGBoost; femoral shaft; fractures; mortality

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.

APA Citation

Gupta, Puneet; Shen, Hong-Jui; Patel, Kunj; Guo, Rui; Heinz, Eric R.; and Manyam, Rameshbabu, "Artificial intelligence for predicting 30-day mortality after surgery for femoral shaft fractures: A retrospective study" (2025). GW Authored Works. Paper 7489.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/7489

Department

School of Medicine and Health Sciences Resident Works