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

Hyung Woo Kim; Seok-Jae Heo; Minseok Kim; Jakyung Lee; Keun Hyung Park; Gongmyung Lee; Song In Baeg; Young Eun Kwon; Hye Min Choi; Dong-Jin Oh; Chung-Mo Nam; Beom Seok Kim

doi:10.3389/fmed.2022.878858

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

Front Med (Lausanne). 2022 Jul 7:9:878858. doi: 10.3389/fmed.2022.878858. eCollection 2022.

Authors

Affiliations

¹ Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea.
² Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, South Korea.
³ Department of Internal Medicine, Myongji Hospital, Hanyang University College of Medicine, Goyang, South Korea.
⁴ Division of Biostatistics, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea.

Abstract

Objective: Previously developed Intradialytic hypotension (IDH) prediction models utilize clinical variables with potential privacy protection issues. We developed an IDH prediction model using minimal variables, without the risk of privacy infringement.

Methods: Unidentifiable data from 63,640 hemodialysis sessions (26,746 of 79 patients for internal validation, 36,894 of 255 patients for external validation) from two Korean hospital hemodialysis databases were finally analyzed, using three IDH definitions: (1) systolic blood pressure (SBP) nadir <90 mmHg (Nadir90); (2) SBP decrease ≥20 mmHg from baseline (Fall20); and (3) SBP decrease ≥20 mmHg and/or mean arterial pressure decrease ≥10 mmHg (Fall20/MAP10). The developed models use 30 min information to predict an IDH event in the following 10 min window. Area under the receiver operating characteristic curves (AUROCs) and precision-recall curves were used to compare machine learning and deep learning models by logistic regression, XGBoost, and convolutional neural networks.

Results: Among 344,714 segments, 9,154 (2.7%), 134,988 (39.2%), and 149,674 (43.4%) IDH events occurred according to three different IDH definitions (Nadir90, Fall20, and Fall20/MAP10, respectively). Compared with models including logistic regression, random forest, and XGBoost, the deep learning model achieved the best performance in predicting IDH (AUROCs: Nadir90, 0.905; Fall20, 0.864; Fall20/MAP10, 0.863) only using measurements from hemodialysis machine during dialysis session.

Conclusions: The deep learning model performed well only using monitoring measurement of hemodialysis machine in predicting IDH without any personal information that could risk privacy infringement.

Keywords: deep learning; hemodialysis; intradialytic hypotension; machine learning; privacy protection.