Prediction of intradialytic hypotension using pre-dialysis features-a deep learning-based artificial intelligence
Hanbi Lee1,2, Sung Joon Moon3, Sung Woo Kim3
1Transplantation Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
A new artificial intelligence (AI) model accurately predicts intradialytic hypotension (IDH), a serious complication during hemodialysis (HD). This AI tool offers a reliable method for improving HD treatment safety and patient outcomes.
Area of Science:
- Nephrology and Artificial Intelligence
- Clinical Decision Support Systems
Background:
- Intradialytic hypotension (IDH) is a significant complication of hemodialysis (HD), linked to increased cardiovascular risks.
- Accurate prediction of IDH remains a challenge in clinical practice.
Purpose of the Study:
- To develop a deep learning-based artificial intelligence (AI) model for predicting IDH using pre-dialysis features.
- To evaluate the AI model's predictive performance against traditional machine learning approaches.
Main Methods:
- Utilized data from 2007 patients across 943,220 HD sessions from seven university hospitals.
- Compared a deep learning model with logistic regression, random forest, and XGBoost models.
- Assessed model performance using Matthews correlation coefficient and macro-averaged F1 score.
Main Results:
- IDH occurred in 5.39% of HD sessions; lower pre-dialysis blood pressure and higher ultrafiltration targets were associated with IDH.
- Deep learning model performance improved significantly when incorporating data from the previous three sessions, outperforming other models.
- Key predictors included mean systolic blood pressure from the previous session, ultrafiltration target rate, and prior IDH occurrence.
Conclusions:
- The developed AI model demonstrates accurate prediction of intradialytic hypotension.
- This AI model shows promise as a reliable tool for enhancing hemodialysis treatment.
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