Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension

Abstract

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.

Keywords: artificial intelligence; deep learning; hemodialysis; hypotension; intradialytic hypotension; machine learning.

Copyright © 2021 by the American Society of Nephrology.

Figures

Graphical abstract

Figure 1.

The flow chart of study data retrieval and splitting.

Figure 2.

Development of recurrent neural network model. (A) Illustrative example of sessions with stable vital signs (left) and intradialytic hypotension (IDH) (right). The risk of IDH within 1 hour at a certain time point (red circle) was calculated. When IDH was defined as a decrease in systolic BP ≥20 mm Hg and/or a decrease in mean arterial pressure ≥10 mm Hg, the reference BPs were determined at initial (IDH-2) or prediction (IDH-3) time point. Black and gray arrows indicate routine and additional monitoring of BPs, respectively. (B) Architecture of the proposed recurrent neural network model. Briefly, time-varying and time-invariant features were embedded in the cells with multilayer perceptron. The deepening effect was obtained by inserting fully connected layers between cells, and the learning was stabilized using the layer normalization. IDH-1, intradialytic hypotension defined as nadir systolic BP <90 mm Hg; IDH-2, intradialytic hypotension defined as decrease in systolic BP ≥20 mm Hg and/or decrease in mean arterial pressure ≥10 mm Hg on the basis of BP at initial time point; IDH-3, intradialytic hypotension defined as decrease in systolic BP ≥20 mm Hg and/or decrease in mean arterial pressure ≥10 mm Hg on the basis of BP at prediction time point. SBP, systolic BP; DBP, diastolic BP; HR, heart rate; BT, body temperature; BFR, blood flow rate; UF, ultrafiltration; DM, diabetes mellitus; WBC, white blood cell count; BatchNorm, batch normalization; LayerNorm, layer normalization; GRU, gated recurrent unit; FC, fully connected; ReLU, rectified linear unit.

Figure 3.

Receiver operating characteristic (left) and precision-recall (right) curves for prediction of intradialytic hypotension (IDH). (A) Prediction of IDH-1. (B) Prediction of IDH-2. (C) Prediction of IDH-3. RNN, recurrent neural network; MLP, multilayer perceptron; LightGBM, Light Gradient Boosting Machine; LR, logistic regression; AUC, area under the curve.

Figure 4.

Feature rankings for the model predicting intradialytic hypotension (IDH). (A) IDH-1; (B) IDH-2; (C) IDH-3.