All-Cause Mortality Risk in Elderly Patients with Femoral Neck and Intertrochanteric Fractures: A Predictive Model Based on Machine Learning

Aoying Min,1 Yan Liu,2 Mingming Fu,3 Zhiyong Hou,2,4 Zhiqian Wang1 1Department of Geriatric Orthopedics, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China; 2Department of Orthopaedic Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China; 3The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China; 4NHC Key Laboratory of Intelligent Orthopeadic Equipment, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of ChinaCorrespondence: Zhiqian Wang, Department of Geriatric Orthopedics, Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050051, People’s Republic of China, Email [email protected] Zhiyong Hou, Department of Orthopaedic Surgery, Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050051, People’s Republic of China, Email [email protected]: The aim of this study was to identify the influencing factors for all-cause mortality in elderly patients with intertrochanteric and femoral neck fractures and to construct predictive models.Methods: This study retrospectively collected elderly patients with intertrochanteric fractures and femoral neck fractures who underwent hip fractures surgery in the Third Hospital of Hebei Medical University from January 2020 to December 2022. Cox proportional hazards regression is used to explore the association between fractures type and mortality. Boruta algorithm was used to screen the risk factors related to death. Multivariate logistic regression was used to determine the independent risk factors, and a nomogram prediction model was established. The ROC curve, calibration curve and DCA decision curve were drawn by R language, and the prediction model was established by machine learning algorithm.Results: Among the 1373 patients. There were 6 variables that remained in the model for intertrochanteric fractures: age (HR 1.048, 95% CI 1.014– 1.083, p = 0.006), AMI (HR 4.631, 95% CI 2.190– 9.795, P < 0.001), COPD (HR 3.818, 95% CI 1.516– 9.614, P = 0.004), CHF (HR 2.743, 95% CI 1.510– 4.981, P = 0.001), NOAF (HR 1.748, 95% CI 1.033– 2.956, P = 0.037), FBG (HR 1.116, 95% CI 1.026– 1.215, P = 0.011). There were 3 variables that remained in the model for femoral neck fractures: age (HR 1.145, 95% CI 1.097– 1.196, P < 0.001), HbA1c (HR 1.264, 95% CI 1.088– 1.468, P = 0.002), BNP (HR 1.001, 95% CI 1.000– 1.002, P = 0.019). The experimental results showed that the model has good identification ability, calibration effect and clinical application value.Conclusion: Intertrochanteric fractures is an independent risk factor for all-cause mortality in elderly patients with hip fractures. By constructing a prognostic model based on machine learning, the risk factors of mortality in patients with intertrochanteric fractures and femoral neck fractures can be effectively identified, and personalized treatment strategies can be developed.Keywords: mortality, intertrochanteric fractures, femoral neck fractures, boruta algorithm, machine learning, prediction model