Multiple machine learning models for predicting major adverse cardiovascular events in dialysis with clinical and echocardiographic parameters: a retrospective cohort study

Abstract

Abstract Background Patients undergoing dialysis are at an elevated risk of cardiovascular events. This study aimed to develop machine learning (ML) prediction models to identify risk factors for major adverse cardiovascular events (MACE) in dialysis patients. Materials and Methods This retrospective study included 203 patients undergoing dialysis with a median age of 45.0 years and 64.0% male. The participants were divided into training and test sets in a 7:3 ratio. LASSO regression selected characteristic variables from patients’general information, laboratory tests, and echocardiographic parameters (including global longitudinal strain [GLS]). Eight ML models were constructed,and SHAP analysis evaluated feature importance. Results The incidence of MACE (including myocardial infarction, unstable angina, heart failure, and cardiovascular death) in dialysis patients was 38.92%. The average follow-up period was 18 months. LASSO regression identified eight feature variables. Among the ML models, AdaBoost demonstrated superior performance, with an AUC of 0.883 (95% CI: 0.830–0.937), accuracy of 0.804, sensitivity of 0.864 and specificity of 0.762 in the training set, and an AUC of 0.809 (95% CI: 0.706–0.912), accuracy of 0.750, sensitivity of 0.90 and specificity of 0.675 in the test set. The SHAP analysis identified N-terminal pro-brain natriuretic peptide (NT-proBNP) level, estimated glomerular filtration rate (eGFR), GLS and age as the four most important features for predicting MACE in patients undergoing dialysis (mean absolute SHAP values: 0.199, 0.176, 0.096 and 0.091, respectively). Conclusion Elevated NT-proBNP, advanced age, reduced eGFR and impaired GLS were independently associated with an increased risk of MACE in patients undergoing dialysis.