%0 Journal Article
%A Lee, Hojun
%A Yun, Donghwan
%A Yoo, Jayeon
%A Yoo, Kiyoon
%A Kim, Yong Chul
%A Kim, Dong Ki
%A Oh, Kook-Hwan
%A Joo, Kwon Wook
%A Kim, Yon Su
%A Kwak, Nojun
%A Han, Seung Seok
%T Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension
%D 2021
%R 10.2215/CJN.09280620
%J Clinical Journal of the American Society of Nephrology
%P 396-406
%V 16
%N 3
%X Background and objectives Intradialytic hypotension has high clinical significance. However, predicting it using conventional statistical models may be difficult because several factors have interactive and complex effects on the risk. Herein, we applied a deep learning model (recurrent neural network) to predict the risk of intradialytic hypotension using a timestamp-bearing dataset.Design, setting, participants, & measurements We obtained 261,647 hemodialysis sessions with 1,600,531 independent timestamps (i.e., time-varying vital signs) and randomly divided them into training (70%), validation (5%), calibration (5%), and testing (20%) sets. Intradialytic hypotension was defined when nadir systolic BP was <90 mm Hg (termed intradialytic hypotension 1) or when a decrease in systolic BP ≥20 mm Hg and/or a decrease in mean arterial pressure ≥10 mm Hg on the basis of the initial BPs (termed intradialytic hypotension 2) or prediction time BPs (termed intradialytic hypotension 3) occurred within 1 hour. The area under the receiver operating characteristic curves, the area under the precision-recall curves, and F1 scores obtained using the recurrent neural network model were compared with those obtained using multilayer perceptron, Light Gradient Boosting Machine, and logistic regression models.Results The recurrent neural network model for predicting intradialytic hypotension 1 achieved an area under the receiver operating characteristic curve of 0.94 (95% confidence intervals, 0.94 to 0.94), which was higher than those obtained using the other models (P<0.001). The recurrent neural network model for predicting intradialytic hypotension 2 and intradialytic hypotension 3 achieved area under the receiver operating characteristic curves of 0.87 (interquartile range, 0.87–0.87) and 0.79 (interquartile range, 0.79–0.79), respectively, which were also higher than those obtained using the other models (P≤0.001). The area under the precision-recall curve and F1 score were higher using the recurrent neural network model than they were using the other models. The recurrent neural network models for intradialytic hypotension were highly calibrated.Conclusions Our deep learning model can be used to predict the real-time risk of intradialytic hypotension.
%U https://cjasn.asnjournals.org/content/clinjasn/16/3/396.full.pdf