Deep Learning for Intradialytic Hypotension Prediction in Hemodialysis Patients
Jin‐Bor Chen, Kuo‐Chuan Wu, Sin‐Hua Moi, Li‐Yeh Chuang, Cheng‐Hong Yang
Abstract
Intradialytic hypotension is a common problem during hemodialysis treatment. Despite several clinical variables have been authenticated for associations during dialysis session, the interaction effects between variables has not yet been presented. Our study aimed to investigate clinical factors associated with intradialytic hypotension by deep learning. A total of 279 participants with 780 hemodialysis sessions on an outpatient in a hospital-facilitated hemodialysis center were enrolled in March 2018. Associations between clinical factors and intradialytic hypotension were determined using linear regression method and deep neural network. A full-adjusted model indicated that intradialytic hypotension is positively associated with body mass index (Beta = 0.17, p = 0.028), hypertension comorbidity (Beta = 0.17, p = 0.008), and ultrafiltration amount (Beta = 0.31, p <; 0.001), and is inversely associated with the ultrafiltration rate in a hemodialysis session (Beta = -0.30, p = 0.001). The 4-factor locus obtained by the deep neural network reached the maximum performance metrics evaluation (accuracy = 64.97± 0.94; true positive rate = 87.97 ± 2.73; positive predictive value = 66.74 ± 0.98; Matthews correlation coefficient = 0.19 ± 0.03). The prediction model obtained by the deep learning scheme could be a potential tool for the management of intradialytic hypotension.