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

Q: What was the aim of the study regarding hip fractures in elderly patients?

To identify factors influencing all-cause mortality in elderly patients with intertrochanteric and femoral neck fractures and to construct predictive models.

Q: Which methods were used to screen and select mortality risk factors?

Cox proportional hazards regression assessed associations with mortality, Boruta algorithm screened risk factors, and multivariate logistic regression determined independent predictors.

Q: What variables were retained in the final predictive model for intertrochanteric fractures?

Six variables remained: age, AMI, COPD, CHF, NOAF, and FBG.

Q: What variables were retained in the final predictive model for femoral neck fractures?

Three variables remained: age, HbA1c, and BNP.

Aug 5, 2026

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A predictive, machine-learning–based analysis was developed to identify factors influencing all-cause mortality in elderly patients undergoing surgery for femoral neck and intertrochanteric fractures. A retrospective cohort of hip fracture patients treated from January 2020 to December 2022 was analyzed using Cox proportional hazards regression for fracture-type associations, Boruta for feature screening, and multivariate logistic regression to determine independent risk factors. For intertrochanteric fractures, six variables remained in the final model; for femoral neck fractures, three variables remained. The resulting nomogram and ML framework showed good identification, calibration, and clinical utility, supporting individualized prognostic risk assessment.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Study rationale and background

Materials and Methods

Study design and population

Statistical methods and model building

Results

Intertrochanteric fracture predictors

Femoral neck fracture predictors

Model performance and interpretation

Conclusion

Prognostic implications

Clinical Interventions in Aging downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. Clinical Interventions in Aging Open Access Full Text Article ORIGINAL RESEARCH All-Cause Mortality Risk in Elderly Patients with Femoral Neck and Intertrochanteric Fractures: A Predictive Model Based on Machine Learning Aoying Min 1 , Yan Liu2 , Mingming Fu 3 , Zhiyong Hou2 ,4 , Zhiqian Wang 1 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 China Correspondence: Zhiqian Wang, Department of Geriatric Orthopedics, Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050051, People’s Republic of China, Email [37800709@hebmu.edu.cn](37800709@hebmu.edu.cn); Zhiyong Hou, Department of Orthopaedic Surgery, Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050051, People’s Republic of China, Email [drzyhou@gmail.com](drzyhou@gmail.com) | Introduction: 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, anda 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 | | --- | | Introduction
As the global elderly population accelerates, the number of elderly patients with hip fractures is also increasing.1 Hip fractures are among the fractures with the highest mortality risk.2–5
Most studies have treated hip fractures as a single, uniform condition, but it includes two major anatomic types: intertrochanteric fractures and femoral neck fractures. The former is an extracapsular frac

FAQs

What was the aim of the study regarding hip fractures in elderly patients?

To identify factors influencing all-cause mortality in elderly patients with intertrochanteric and femoral neck fractures and to construct predictive models.

Which methods were used to screen and select mortality risk factors?

Cox proportional hazards regression assessed associations with mortality, Boruta algorithm screened risk factors, and multivariate logistic regression determined independent predictors.

What variables were retained in the final predictive model for intertrochanteric fractures?

Six variables remained: age, AMI, COPD, CHF, NOAF, and FBG.

What variables were retained in the final predictive model for femoral neck fractures?

Three variables remained: age, HbA1c, and BNP.

Aug 5, 2026

Upload by Riley