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Prediction of Intradialytic Hypotension Using Pre-dialysis Features—a Deep Learning–based Artificial Intelligence Model by Hanbi Lee; Sung Joon Moon; Sung Woo Kim; Ji Won Min; Hoon Suk Park; Hye Eun Yoon; Young Soo Kim; Hyung Wook Kim; Chul Woo Yang; Sungjin Chung; Eun Sil Koh; Byung Ha Chung is a Medicine article available to read on EtoBox.

What is Prediction of Intradialytic Hypotension Using Pre-dialysis Features—a Deep Learning–based Artificial Intelligence Model about?

Abstract Background Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD) associated with increased risks of cardiovascular morbidity and mortality. However, its accurate prediction remains a clinical challenge. The aim of this study was to develop a deep learning–based artificial intelligence (AI) model to predict IDH using pre-dialysis features. Methods Data from 2,007 patients with 943,220 HD sessions at seven university hospitals were used. The performance of deep learning model was compared with 3 machine learning models (logistic regression, random forest, and XGBoost). Results IDH occurred in 5.39% of all studied HD sessions. A lower pre-dialysis blood pressure (BP), and a higher ultrafiltration (UF) target rate and interdialytic weight gain in IDH sessions compared to non-IDH sessions, and the occurrence of IDH in previous sessions was more frequent among IDH sessions compared to non-IDH sessions. Matthews correlation coefficient (MCC) and macro-averaged F1 score were used to evaluate both positive and negative prediction performances. Both values were similar in logistic regression, random forest, XGBoost, and deep learning models, developed with d

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Author

Hanbi Lee; Sung Joon Moon; Sung Woo Kim; Ji Won Min; Hoon Suk Park; Hye Eun Yoon; Young Soo Kim; Hyung Wook Kim; Chul Woo Yang; Sungjin Chung; Eun Sil Koh; Byung Ha Chung

Publisher

Oxford University Press (OUP)

Published

2023

Language

EN

Field

Medicine (Health Sciences)