关键词:

慢性肾脏病, 血液透析, 机器学习, 透析内低血压, 预测模型

Abstract:

Objective　To develop a predictive model for intradialytic hypotension (IDH) using machine learning techniques. Methods　A retrospective analysis was conducted on the demographic data and dialysis records of the patients who underwent hemodialysis at Fuding City Hospital between October 2020 and August 2022. The variables included age, gender, pre-dialysis blood pressure, and pre-dialysis weight. Three distinct machine learning algorithms, light Gradient Boosting Machine (LGBM), support vector machine (SVM), and TabNet, were employed to construct two predictive models, designated as IDH-1 and IDH-2. The IDH-1 model integrates real-time pre-dialysis data with historical dialysis data averages to predict IDH risk instantaneously. Conversely, the IDH-2 model incorporates comprehensive current dialysis data along with historical averages to forecast IDH risk during the subsequent dialysis session. The areas under the curves (AUC), accurate rates, and F1 scores by the three algorithms were compared. Results　A total of 77 808 hemodialysis treatment records of 434 patients were used as the initial data set. After rigorous data screening, the final data set of the IDH-1 model contained 416 patients and 71 427 hemodialysis records, and the IDH-2 model contained 416 patients and 71 011 hemodialysis records. TabNet outperformed both LGBM and SVM. The AUC of the TabNet algorithm in the IDH-1 model was 0.84, with a 95% confidence interval (CI) ranging from 0.810 to 0.860. In the IDH-2 model, the AUC of the TabNet algorithm was 0.83, with a 95%CI ranging from 0.805 to 0.850. Historical frequency of IDH episodes, as well as pre-dialysis and intra-dialysis systolic blood pressures, were identified as critical predictive factors for IDH. Conclusions　This study underscores the significant potential of employing machine learning methodologies, in conjunction with demographic data and dialysis parameters, to predict IDH in hemodialysis patients.

Key words:

Chronic kidney disease, Among  , them,TabNet  , has  , the  , best  , Performance  , Hemodialysis, Machine learning, Intradialytic hypotension,  , Predictive model