Prediction of Intradialytic Blood Pressure Variation Based on Big Data
Cheng-Jui Lin1,2,3, Ying-Ying Chen1, Pei-Chen Wu1
1Division of Nephrology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan.
Insights
This study developed an intelligent system to predict blood pressure (BP) changes during hemodialysis (HD). The system achieved over 80% accuracy, aiding clinical decisions and potentially reducing cardiovascular events in HD patients.
Area of Science:
- Nephrology
- Cardiology
- Medical Informatics
Background:
- Cardiovascular events are a major cause of mortality in hemodialysis (HD) patients.
- Significant blood pressure (BP) variations during HD are linked to increased mortality risk.
- Real-time BP monitoring and prediction are crucial for managing HD patients.
Purpose of the Study:
- To develop a web-based intelligent system for predicting systolic blood pressure (SBP) changes during HD.
- To provide a tool for real-time monitoring and clinical decision-making in HD treatment.
Main Methods:
- Collected HD parameters and linked them with demographic data.
- Developed a multiple linear regression model using training data to predict SBP changes.
- Validated the model's performance on test and new patient groups using a web-based interactive system.
Main Results:
- Utilized over 542,000 BP records for model development.
- Achieved over 80% accuracy in predicting SBP changes within a 15-20 mm Hg error range.
- Demonstrated increasing prediction accuracy with higher SBP value thresholds.
Conclusions:
- The developed prediction model shows good performance and supports reducing intradialytic SBP variability.
- The intelligent SBP prediction system can aid clinical decision-making for new HD patients.
- Further research is needed to confirm if this system reduces cardiovascular event incidence in HD patients.
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.
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