Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension

Abstract

Background and objectivesIntradialytic 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, & measurementsWe 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.ResultsThe 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.ConclusionsOur deep learning model can be used to predict the real-time risk of intradialytic hypotension.

Deep Learning Model for Predicting Intradialytic Hypotension Without Privacy Infringement: A Retrospective Two-Center Study

“…Moreover, real-time data at 1-min intervals were used to detect IDH events in this study. In some studies, time-varying variables were used to improve the efficacy of the prediction model, but real-time data continuously generated from the dialysis machine were not used, as in our study ( 8 ). Similarly, a previous study showed the feasibility of using data continuously measured by the hemodialysis machine to predict outcomes ( 28 ).…”

System of integrating biosignals during hemodialysis: the CONTINUAL (Continuous mOnitoriNg viTal sIgN dUring hemodiALysis) registry

“…The large quantity of biosignals necessitates advanced or novel analytics that range from collection to interpretation [ 24 ]. Machine learning, including deep learning, is a rapidly developing branch of artificial intelligence that has shown promise for use in clinics [ 13 , 25 ]. A major limitation in utilizing biosignals for artificial intelligence-based clinical purposes is the lack of data storage [ 26 ].…”

“…Monitoring biosignals increases the awareness of their clinical importance because they can serve as indicators for unpredictable events during routine or urgent practice. Hemodialysis per se changes the biosignals of patients with or sometimes without symptoms, and thus monitoring changes in biosignals may allow for tracing or predicting hemodynamic complications during hemodialysis [ 11 – 13 ]. Some studies have traced intradialytic biosignals such as BP and ECG, and the risk of hemodynamic complications and relevant outcomes could be identified in detail by monitoring these signals [ 8 , 14 – 16 ].…”

Dialysis adequacy predictions using a machine learning method

“…Various studies have also used DL to investigate hemodialysis. Akl et al 14 suggested decades ago that the neural network can achieve artificial-intelligent dialysis control, and studies on intradialytic hypotension predictions [15][16][17][18] , the optimal dry weight setting 19 , and anemia control 20 for hemodialysis have been presented. DL in research has also expanded to other kidney diseases to predict acute kidney injury outcomes 21,22 and hyperkalemia 23 .…”