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Prognostic Features for Overall Survival in Male Diabetic Patients Undergoing Hemodialysis Using Elastic Net
Mehrdad Sharifi1, Razieh Sadat Mousavi-Roknabadi2,3,4, Vahid Ebrahimi2
1Emergency Medicine Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran Emergency Medicine Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Archives of Iranian Medicine
|February 26, 2025
Summary
Machine learning accurately predicted mortality in male diabetic hemodialysis patients. Lower BMI, specific vascular access, and longer dialysis sessions were linked to reduced death risk, improving survival predictions.
Area of Science:
- Nephrology
- Medical Informatics
- Biostatistics
Background:
- Diabetic patients on hemodialysis (HD) face elevated mortality risks, particularly males.
- Predictive models are crucial for managing outcomes in this high-risk population.
Purpose of the Study:
- To develop and optimize a machine learning model for predicting mortality risk in male diabetic hemodialysis (MDHD) patients.
- To identify key factors influencing survival in MDHD patients.
Main Methods:
- A multicenter retrospective study analyzed data from 308 adult MDHD patients (2011-2019).
- Elastic net penalized Cox proportional hazards (EN-Cox) regression was employed for predictive modeling.
- Backward elimination refined the model to essential covariates for enhanced generalizability.
Main Results:
- The EN-Cox model identified 6 significant predictors of mortality from 35 initial candidates.
- Factors associated with increased mortality included BMI <25 kg/m², central venous catheter (CVC) access, lower systolic blood pressure, anemia (Hb <12.5 g/dL), longer dialysis sessions (≥4 hours), and lower HDL-C.
- The model achieved significant predictive accuracy for overall survival (OS) rates.
Conclusions:
- Key factors like anemia, hypotension, CVC use, prolonged dialysis, low BMI, and low HDL-C are associated with reduced mortality in MDHD patients.
- The developed machine learning model offers a valuable tool for predicting survival in this vulnerable group.
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