Visit-to-visit blood pressure variability and clinical outcomes in peritoneal dialysis - based on machine learning algorithms.

Abstract

This study aims to investigate the association between visit-to-visit blood pressure variability (VVV) in early stage of continuous ambulatory peritoneal dialysis (CAPD) and long-term clinical outcomes, utilizing machine learning algorithms. Patients who initiated CAPD therapy between January 1, 2006, and December 31, 2009 were enrolled. VVV parameters were collected during the first six months of CAPD therapy. Patient follow-up extended to December 31, 2021, for up to 15.8 years. The primary outcome was the occurrence of a three-point major adverse cardiovascular event (MACE). Four machine learning algorithms and competing risk regression analysis were applied to construct predictive models. A total of 666 participants were included in the analysis with a mean age of 47.9 years. One of the six VVV parameters, standard deviation of diastolic blood pressure (SDDBP), was finally enrolled into the MACE predicting model and mortality predicting model. In the MACE predicting model, higher SDDBP was associated with 99% higher MACE risk. The association between SDDBP and MACE risk was attenuated by better residual renal function (p for interaction <0.001). In the mortality predicting model, higher SDDBP was associated with 46% higher mortality risk. This cohort study discerned that high SDDBP in early stage of CAPD indicated increased long-term MACE and mortality risks.