Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis.

Abstract

Objective: Blood pressure fluctuations during dialysis, including intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN), are common complications among patients undergoing maintenance hemodialysis. Early prediction of IDH and IDHTN can help reduce the occurrence of these fluctuations. With the development of artificial intelligence, machine learning and deep learning models have become increasingly sophisticated in the field of hemodialysis. Utilizing machine learning to predict blood pressure fluctuations during dialysis has become a viable predictive method.

Methods: Our study included data from 67,524 hemodialysis sessions conducted at Ningbo No.2 Hospital and Xiangshan First People's Hospital from August 1, 2019, to September 30, 2023. 47,053 sessions were used for model training and testing, while 20,471 sessions were used for external validation. We collected 45 features, including general information, vital signs, blood routine, blood biochemistry, and other relevant data. Data not meeting the inclusion criteria were excluded, and feature engineering was performed. The definitions of IDH and IDHTN were clarified, and 10 machine learning algorithms were used to build the models. For model development, the dialysis data were randomly split into a training set (80%) and a testing set (20%). To evaluate model performance, six metrics were used: accuracy, precision, recall, F1 score, ROC-AUC, and PR-AUC. Shapley Additive Explanation (SHAP) method was employed to identify eight key features, which were used to develop a clinical application utilizing the Streamlit framework.

Results: Statistical analysis showed that IDH occurred in 56.63% of hemodialysis sessions, while the incidence of IDHTN was 23.53%. Multiple machine learning models (e.g., CatBoost, RF) were developed to predict IDH and IDHTN events. XGBoost performed the best, achieving ROC-AUC scores of 0.89 for both IDH and IDHTN in internal validation, with PR-AUC scores of 0.95 and 0.78, and high accuracy, precision, recall, and F1 scores. The SHAP method identified pre-dialysis systolic blood pressure, BMI, and pre-dialysis mean arterial pressure as the top three important features. It has been translated into a convenient application for use in clinical settings.

Conclusion: Using machine learning models to predict IDH and IDHTN during hemodialysis is feasible and provides clinically reliable predictive performance. This can help timely implement interventions during hemodialysis to prevent problems, reduce blood pressure fluctuations during dialysis, and improve patient outcomes.