Artificial Neural Networks Predict 30-Day Mortality After Hip Fracture: Insights From Machine Learning. Issue 22 (15th November 2021)

• Record Type: Journal Article
• Authors: DeBaun, Malcolm R.; Chavez, Gustavo; Fithian, Andrew; Oladeji, Kingsley; Van Rysselberghe, Noelle; Goodnough, L. Henry; Bishop, Julius A.; Gardner, Michael J.
• Is Part Of: Journal of the American Academy of Orthopaedic Surgeons. Volume 29:Issue 22(2021)
• Publication Date: 2021-11-15
• Subjects: Orthopedics -- Periodicals; Orthopedic surgery -- Periodicals; Joint Diseases -- Periodicals; Orthopedics -- Periodicals; Orthopedic surgery; Orthopedics; Periodicals; 616.7005
• DOI: 10.5435/JAAOS-D-20-00429
• Languages: English
• ISSNs: 1067-151X

Abstract

Abstract : Objectives: Accurately stratifying patients in the preoperative period according to mortality risk informs treatment considerations and guides adjustments to bundled reimbursements. We developed and compared three machine learning models to determine which best predicts 30-day mortality after hip fracture. Methods: The 2016 to 2017 National Surgical Quality Improvement Program for hip fracture (AO/OTA 31-A-B-C) procedure-targeted data were analyzed. Three models—artificial neural network, naive Bayes, and logistic regression—were trained and tested using independent variables selected via backward variable selection. The data were split into 80% training and 20% test sets. Predictive accuracy between models was evaluated using area under the curve receiver operating characteristics. Odds ratios were determined using multivariate logistic regression with P < 0.05 for significance. Results: The study cohort included 19, 835 patients (69.3% women). The 30-day mortality rate was 5.3%. In total, 47 independent patient variables were identified to train the testing models. Area under the curve receiver operating characteristics for 30-day mortality was highest for artificial neural network (0.92), followed by the logistic regression (0.87) and naive Bayes models (0.83). Discussion: Machine learning is an emerging approach to develop accurate risk calculators that account for the weighted interactions between variables. In this study, we developed and tested a neural network model that was highly accurate for predicting 30-day mortality after hip fracture. This was superior to the naive Bayes and logistic regression models. The role of machine learning models to predict orthopaedic outcomes merits further development and prospective validation but shows strong promise for positively impacting patient care.