Development and internal validation of a clinical prediction model using machine learning algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above

• Published in: European journal of trauma and emergency surgery (Munich : 2007) Vol. 48; no. 6; pp. 4669 - 4682
• Main Authors: Oosterhoff, Jacobien Hillina Froukje, Savelberg, Angelique Berit Marte Corlijn, Karhade, Aditya Vishwas, Gravesteijn, Benjamin Yaël, Doornberg, Job Nicolaas, Schwab, Joseph Hasbrouck, Heng, Marilyn
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022; Springer Nature B.V
• Subjects: Algorithms; Calibration; Critical Care Medicine; Emergency medical care; Emergency Medicine; Fractures; Hip joint; Intensive; Machine learning; Medicine; Medicine & Public Health; Mortality; Original; Original Article; Orthopedics; Patients; Precision medicine; Shared decision making; Sports Medicine; Surgery; Surgical Orthopedics; Traumatic Surgery; Femoral neck fracture; Hip fracture; Precision medicine; Mortality; Prediction model; Machine learning; Geriatric trauma
• ISSN: 1863-9933; 1863-9941; 1863-9941
• DOI: 10.1007/s00068-022-01981-4

Purpose Preoperative prediction of mortality in femoral neck fracture patients aged 65 years or above may be valuable in the treatment decision-making. A preoperative clinical prediction model can aid surgeons and patients in the shared decision-making process, and optimize care for elderly femoral neck fracture patients. This study aimed to develop and internally validate a clinical prediction model using machine learning (ML) algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above. Methods A retrospective cohort study at two trauma level I centers and three (non-level I) community hospitals was conducted to identify patients undergoing surgical fixation for a femoral neck fracture. Five different ML algorithms were developed and internally validated and assessed by discrimination, calibration, Brier score and decision curve analysis. Results In total, 2478 patients were included with 90 day and 2 year mortality rates of 9.1% ( n  = 225) and 23.5% ( n  = 582) respectively. The models included patient characteristics, comorbidities and laboratory values. The stochastic gradient boosting algorithm had the best performance for 90 day mortality prediction, with good discrimination (c-statistic = 0.74), calibration (intercept = − 0.05, slope = 1.11) and Brier score (0.078). The elastic-net penalized logistic regression algorithm had the best performance for 2 year mortality prediction, with good discrimination (c-statistic = 0.70), calibration (intercept = − 0.03, slope = 0.89) and Brier score (0.16). The models were incorporated into a freely available web-based application, including individual patient explanations for interpretation of the model to understand the reasoning how the model made a certain prediction: https://sorg-apps.shinyapps.io/hipfracturemortality/ Conclusions The clinical prediction models show promise in estimating mortality prediction in elderly femoral neck fracture patients. External and prospective validation of the models may improve surgeon ability when faced with the treatment decision-making. Level of evidence Prognostic Level II.