Lin C.-J.a,b,c· Chen Y.-Y.a· Wu P.-C.a· Pan C.-F.a· Shih H.-M.a,d· Wu C.-J.a,b,e

Author affiliations

aDivision of Nephrology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan
bDepartment of Medicine, Mackay Medical College, New Taipei, Taiwan
cMackay Junior College of Medicine, Nursing and Management, Taipei, Taiwan
dGraduate Institute of Physiology, College of Medicine, National Taiwan University, Taipei, Taiwan
eDepartment of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

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Article / Publication Details

First-Page Preview

Received: March 29, 2022
Accepted: October 05, 2022
Published online: March 08, 2023

Number of Print Pages: 9
Number of Figures: 3
Number of Tables: 3

ISSN: 0253-5068 (Print)
eISSN: 1421-9735 (Online)

For additional information: https://www.karger.com/BPU

Abstract

Introduction: Cardiovascular (CV) events are the major cause of morbidity and mortality associated with blood pressure (BP) in hemodialysis (HD) patients. BP varies significantly during HD treatment, and the dramatic variation in BP is a well-recognized risk factor for increased mortality. The development of an intelligent system capable of predicting BP profiles for real-time monitoring is important. Our aim was to build a web-based system to predict changes in systolic BP (SBP) during HD. Methods: In this study, dialysis equipment connected to the Vital Info Portal gateway collected HD parameters that were linked to demographic data stored in the hospital information system. There were 3 types of patients: training, test, and new. A multiple linear regression model was built using the training group with SBP change as the dependent variable and dialysis parameters as the independent variables. We tested the model’s performance on test and new patient groups using coverage rates with different thresholds. The model’s performance was visualized using a web-based interactive system. Results: A total of 542,424 BP records were used for model building. The accuracy was greater than 80% in the prediction error range of 15%, and 20 mm Hg of true SBP in the test and new patient groups for the model of SBP changes suggested the good performance of our prediction model. In the analysis of absolute SBP values (5, 10, 15, 20, and 25 mm Hg), the accuracy of the SBP prediction increased as the threshold value increased. Discussion: This databae supported our prediction model in reducing the frequency of intradialytic SBP variability, which may help in clinical decision-making when a new patient receives HD treatment. Further investigations are needed to determine whether the introduction of the intelligent SBP prediction system decreases the incidence of CV events in HD patients.

© 2023 S. Karger AG, Basel

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First-Page Preview

Received: March 29, 2022
Accepted: October 05, 2022
Published online: March 08, 2023

Number of Print Pages: 9
Number of Figures: 3
Number of Tables: 3

ISSN: 0253-5068 (Print)
eISSN: 1421-9735 (Online)

For additional information: https://www.karger.com/BPU

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