Machine learning models to predict post-dialysis blood pressure in children and young adults on maintenance hemodialysis.

ABSTRACT: Hypertension is associated with significant cardiovascular morbidity. Blood pressure (BP) control on maintenance hemodialysis (HD) is strongly impacted by volume status. The objective of this study was to assess whether machine learning (ML) is effective in predicting post-HD BP in children and young adults on HD. We collected data on BP, IDWG, pulse, and weights for patients on maintenance HD (> 3 months). Input features included DW, pre-post weight difference, IDWG and pre-HD BP. Seven models were trained and tuned using open-source libraries. Model performance was evaluated using time-series cross-validation on a rolling basis (3-12 month training, 1-day testing). Various regression scores were compared between models. Data for 35 patients (14,375 HD sessions) were analyzed. Extreme gra

SUBMITTER: Bou-Matar R 

PROVIDER: S-EPMC10625550 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature