Intradialytic blood pressure pattern recognition based on density peak clustering.

Abstract

End-stage renal disease (ESRD) is the final stage of chronic kidney disease (CKD) and requires hemodialysis (HD) for survival. Intradialytic blood pressure (IBP) measurements are necessary to ensure patient safety during HD treatments and have critical clinical and prognostic significance. Studies on IBP measurements, especially IBP patterns, are limited. All related studies have been based on a priori knowledge and artificially classified IBP patterns. Therefore, the results were influenced by subjective concepts. In this study, we proposed a new approach to identify IBP patterns to classify ESRD patients. We used the dynamic time warping (DTW) algorithm to measure the similarity between two series of IBP data. Five blood pressure (BP) patterns were identified by applying the density peak clustering algorithm (DPCA) to the IBP data. To illustrate the association between BP patterns and prognosis, we constructed three random survival forest (RSF) models with different covariates. Model accuracy was improved 3.7-6.3% by the inclusion of BP patterns. The results suggest that BP patterns have critical clinical and prognostic significance regarding the risk of cerebrovascular events. We can also apply this clustering approach to other time series data from electronic health records (EHRs). This work is generalizable to analyses of dense EHR data.