Development of prediction models for cardiovascular disease mortality risk in maintenance hemodialysis patients based on nomogram and CART algorithm

作者：He X, Zhang X, Mao N, Zhang Y, Ma X · 发表于：Clinical nephrology · 年份：2026 · DOI：10.5414/CN111819 · 研究领域：Nomograms、Renal Dialysis、Cardiovascular Diseases、Humans、Female、Male、Retrospective Studies、Middle Aged、Risk Assessment、Prediction Algorithms、Aged、Risk Factors

BACKGROUND: Patients on maintenance hemodialysis (MHD) face a dramatically elevated risk of cardiovascular death, which is 10 - 20 times higher than in the general population. To address this high risk, we developed and validated a prediction model to accurately estimate cardiovascular disease (CVD) mortality and guide preemptive clinical management. MATERIALS AND METHODS: This study retrospectively collected data from MHD patients at the First Affiliated Hospital of Chengdu Medical College from 2016 to 2021 (Approval No. CYFYEC-C-005), including demographic characteristics, medical history, biochemical indicators, and echocardiogram indices. Variables were screened using univariate logistic regression and stepwise regression to construct a nomogram model. The dataset was randomly divided (6 : 4) into training and validation sets, and a classification and regression tree (CART) decision tree model was also constructed. Both models' discrimination, calibration, and clinical utility were evaluated. RESULTS: The nomogram identified systolic blood pressure, uric acid, total cholesterol, diabetes, myoglobin, serum albumin, and procalcitonin as predictors, with an AUC of 0.947 (95% CI: 0.903 - 0.991) and good clinical applicability. The CART model identified serum albumin, procalcitonin, and myoglobin as predictors, categorizing the population into four groups. AUC values were 0.933 (95% CI: 0.851 - 1.000) in the training set and 0.774 (95% CI: 0.612 - 0.936) in the validation se...