machine learning classification of cardiovascular disease among middle-aged and elderly chronic kidney disease patients

Source article: Cardiovascular Classification in Middle-Aged and Elderly CKD Patients: A Machine Learning Approach in China and the United States

Background Cardiovascular disease (CVD) drives mortality in chronic kidney disease (CKD) patients. Conventional risk tools underperform in CKD, and few machine learning (ML) models undergo cross-national validation. We developed interpretable ML models for classifying CVD amongst Chinese and US middle-aged and elderly CKD patients. Methods We conducted retrospective cross-sectional analyses using two nationally representative cohorts: NHANES (2011-2016, US) and CHARLS (Wave 1 and 3, China). A total of 1057 CKD p…

By the Truvace deskSeptember 30, 20262 min read1 sourcePeer-reviewedTRV-2026-1220· cite & verify

Cardiovascular Classification in Middle-Aged and Elderly CKD Patients: A Machine Learning Approach in China and the United States — "Plugged into dialysis" by newslighter is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.

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In brief

Researchers developed interpretable machine learning models to classify cardiovascular disease among middle-aged and elderly CKD patients using US NHANES (n=1057) and China CHARLS (n=1109) cohorts. Seven algorithms were trained and tested internally, with XGBoost achieving test AUC 0.753 in NHANES and logistic regression achieving 0.769 in CHARLS, followed by bidirectional cross-population external validation and SHAP analysis.

The findings matter because CVD drives mortality in CKD yet conventional risk tools underperform, and the observed drop in performance across countries plus differing top features (age in US, hypertension in China) suggests a single unified model may misclassify risk. Uncertainty remains about prospective clinical utility, as the work was retrospective cross-sectional classification rather than longitudinal prediction or implementation.

Main points

Retrospective cross-sectional analysis included 1057 CKD patients from NHANES 2011-2016 and 1109 from CHARLS Wave 1 and 3.
Seven ML algorithms were trained and optimized with internal test sets and bidirectional cross-population external validation.
SHAP analysis showed Age ranked as the top contributing feature in the US cohort, whereas hypertension was the primary contributing features in the Chinese cohort.

The gain

Interpretable ML models classified prevalent CVD among middle-aged and elderly CKD patients with test AUCs of 0.753 in the US NHANES cohort using XGBoost and 0.769 in the China CHARLS cohort using logistic regression.

The problem

The same CVD classification models lost discriminative performance when deployed across US and Chinese CKD populations, indicating limited cross-population transportability.

The rundown

The study used two nationally representative cohorts, NHANES 2011-2016 with 1057 CKD patients and CHARLS Wave 1 and 3 with 1109 CKD patients, to train seven ML algorithms with optimization and SHAP interpretability.

Internal testing identified XGBoost as best in NHANES and logistic regression as optimal in CHARLS, but bidirectional external validation showed performance decline across populations, with distinct SHAP profiles highlighting age in the US and hypertension in China.

What this doesn’t fix

Models showed reduced discriminative performance when deployed across countries, indicating limited cross-population transportability and supporting need for population-calibrated models.