Key result
An interpretable deep learning model predicted intraoperative hypotension 10 minutes in advance with an AUC of 0.9145 in internal validation and 0.9035 in external validation.
Why the study?
Existing artificial intelligence-based hypotension prediction indices are limited because they may not provide a convincing interpretation of the association between predictors and hypotension.
Does an interpretable deep learning model accurately predict intraoperative hypotension 10 minutes in advance in anesthetized patients?
Does an interpretable deep learning model accurately predict intraoperative hypotension 10 minutes in advance in anesthetized patients?
An interpretable deep learning model can accurately predict intraoperative hypotension 10 minutes in advance using short arterial blood pressure records while providing physiological interpretability.
Monitoring arterial blood pressure (ABP) in anesthetized patients is crucial for preventing hypotension, which can lead to adverse clinical outcomes. Thus, several efforts have been made to develop an artificial intelligence-based hypotension prediction index. Nevertheless, the use of these indices is limited because they may not provide a convincing interpretation of the association between predictors and hypotension. Herein, we developed an interpretable deep learning model that forecasts hypotension occurrences 10 min before a given 90 s ABP record. Internal and external validations of model performance reported the area under the receiver operating characteristic curve (AUC) as 0.9145 and 0.9035, respectively. Furthermore, the hypotension prediction mechanism can be physiologically interpreted by using predictors representing ABP trends that are automatically generated in the proposed model. Ultimately, we demonstrate high-applicability of a deep learning model that has a high accuracy performance and provides an interpretation of the association between ABP trends and hypotension in clinical practices.
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Hwang et al. (2022) studied Intraoperative hypotension in anesthetized patients. Interpretable deep learning model for hypotension prediction was evaluated on Prediction of hypotension occurrences 10 min before a given 90 s ABP record (AUC 0.9145 (internal) and 0.9035 (external)). An interpretable deep learning model predicted intraoperative hypotension 10 minutes in advance with an AUC of 0.9145 in internal validation and 0.9035 in external validation.
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