Abstract: SA-PO367
Deep Learning Model for Predicting Intradialytic Hypotension Without Privacy Infringement: A Retrospective Two-Center Study
Session Information
- Hemodialysis and Frequent Dialysis: CV and Risk Prediction
November 05, 2022 | Location: Exhibit Hall, Orange County Convention Center‚ West Building
Abstract Time: 10:00 AM - 12:00 PM
Category: Dialysis
- 701 Dialysis: Hemodialysis and Frequent Dialysis
Authors
- Kim, Hyung Woo, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea (the Republic of)
- Heo, Seok-Jae, Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea (the Republic of)
- Kim, Minseok, Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea (the Republic of)
- Lee, Jakyung, Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea (the Republic of)
- Park, Keun Hyung, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea (the Republic of)
- Lee, Gongmyung, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea (the Republic of)
- Baeg, Song in, Department of Internal Medicine, Hanyang University College of Medicine, Myongji Hospital, Goyang, Korea (the Republic of)
- Kwon, Young Eun, Department of Internal Medicine, Hanyang University College of Medicine, Myongji Hospital, Goyang, Korea (the Republic of)
- Choi, Hye Min, Department of Internal Medicine, Hanyang University College of Medicine, Myongji Hospital, Goyang, Korea (the Republic of)
- Oh, Dong-jin, Department of Internal Medicine, Hanyang University College of Medicine, Myongji Hospital, Goyang, Korea (the Republic of)
- Nam, Chung-Mo, Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea (the Republic of)
- Kim, Beom Seok, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea (the Republic of)
Background
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). To predict the IDH event after 10 minutes, segments for the previous 40 minutes from 10 minutes before each time points at which blood pressure was created. Areas under receiver operating characteristic (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.
Conclusion
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.