To Identify the Predictors of Mortality in Renal Patients Undergoing Dialysis

Abstract

Chronic Kidney Disease (CKD) patients undergoing dialysis experience high mortality risk due to complex clinical factors and multiple comorbidities. Precise identification of mortality predictors is vital for early risk stratification and improving patient management. This study aimed to identify key predictors of mortality among renal patients undergoing dialysis using a combination of statistical and machine learning techniques on a dataset comprising 224 observations and 33 clinical features. Associations between mortality and clinical variables were assessed using chi-square tests and independent samples t-tests. Feature selection methods—LASSO regression, Random Forest, and Gradient Boosting—were employed to identify important predictors. Machine learning models were developed to evaluate predictive performance. LASSO regression emphasized sparsity, selecting critical features including total dialysis sessions, heart, and lung disease. Random Forest highlighted age, diabetes, and cardiovascular comorbidities, capturing nonlinear relationships. Gradient Boosting identified additional hemodynamic variables such as pre- and post-dialysis blood pressures. The combined feature set aggregated predictors from all methods, enhancing robustness. The Random Forest model achieved the highest discriminative performance (AUC = 0.851), with LASSO demonstrating higher sensitivity for deceased patients. Cardiovascular and metabolic comorbidities, dialysis parameters, and age are pivotal predictors of mortality in CKD patients on dialysis. Integrating multiple analytical methods strengthens predictive accuracy, facilitating better-informed clinical decision-making and targeted interventions. Multivariable Cox regression revealed that age was a significant predictor of mortality, with each additional year increasing the hazard by approximately 3% (HR = 1.028; 95% CI: 1.006–1.050; p = 0.0122). Conversely, a higher number of dialysis sessions was associated with a reduced mortality risk, decreasing the hazard by 3.8% per session (HR = 0.962; 95% CI: 0.952–0.973; p < 0.001). Lung involvement more than doubled the risk of death (HR = 2.226; 95% CI: 1.088–4.557; p = 0.0285), while the presence of anaemia and diabetes independently increased mortality risk by nearly threefold (HR = 2.846 and 2.848, respectively; p < 0.01). These results highlight the importance of managing comorbid conditions to improve survival outcomes.