A Machine Learning-Based Model to Estimate the Risk of Pulmonary Hypertension in Chronic Kidney Disease Patients.

ABSTRACT: Pulmonary hypertension (PH) is a common complication in patients with chronic kidney disease (CKD) and is associated with high mortality. Early detection and proper management may improve outcomes in high-risk patients. This study aimed to develop a simple and effective model for screening PH risk in this population. We retrospectively screened 1082 CKD patients. Feature selection was performed using the least absolute shrinkage and selection operator, univariate and multivariate logistic regression (LR). Nomograms were developed for PH risk assessment. The discriminative ability was estimated by the area under the receiver operating characteristic curve (AUROC), and the accuracy was assessed with a Brier score. Models were validated externally by calculating their performance on a validat

SUBMITTER: Gu W 

PROVIDER: S-EPMC12424067 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature