Enhancing Intradialytic Hypotension Prediction: A Deep Learning Approach Prioritizing Data Privacy

Intradialytic hypotension (IDH) poses a significant risk to hemodialysis patients. Traditional prediction models often rely on extensive patient data, raising concerns about privacy. This study presents a novel approach leveraging deep learning to predict IDH using minimal, anonymized data from hemodialysis machines, effectively mitigating privacy risks.

The research utilized data from two Korean hospital hemodialysis databases, encompassing 63,640 hemodialysis sessions from a total of 334 patients. This data was meticulously anonymized to protect patient privacy. The study employed three distinct 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 models were trained to predict IDH events within a 10-minute window using 30 minutes of preceding hemodialysis machine data.

A comparative analysis was conducted, evaluating the performance of a deep learning model (Convolutional Neural Networks) against traditional machine learning algorithms such as logistic regression and XGBoost. The deep learning model consistently outperformed its counterparts across all three IDH definitions. Specifically, the area under the receiver operating characteristic curve (AUROC) values achieved were: 0.905 for Nadir90, 0.864 for Fall20, and 0.863 for Fall20/MAP10. These results demonstrate the superior predictive capabilities of the deep learning approach in identifying impending IDH events.

This study’s significant contribution lies in its development of a robust and accurate IDH prediction model that prioritizes data privacy. By relying solely on readily available hemodialysis machine data, the model effectively eliminates the need for potentially sensitive patient information. The high AUROC values underscore the model’s clinical potential for improving patient safety and reducing the incidence of IDH during hemodialysis treatments. Further research could explore the generalizability of this model across diverse patient populations and hemodialysis settings.

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