Key result
A deep learning model using permutation and dropout methods predicted intraoperative hypotension with an AUROC of 0.862 (95% CI 0.859-0.861), outperforming models without these methods.
Why the study?
Intraoperative hypotension increases postoperative complications, but high-dimensional prediction models risk overfitting, and strategies to prevent this have had little discussion.
Does a deep learning model with permutation and dropout methods improve the prediction of intraoperative hypotension in surgical patients?
Does a deep learning model with permutation and dropout methods improve the prediction of intraoperative hypotension in surgical patients?
A deep learning model incorporating permutation and dropout methods improves the accuracy of predicting intraoperative hypotension by mitigating overfitting.
Supports DL model refinement for perioperative monitoring; leaves open prospective validation before clinical adoption.
BACKGROUND Intraoperative hypotension (IOH) is associated with an increased risk of postoperative complications. Therefore, in recent years, various models for IOH prediction based on high-dimensional signal data have been developed. Given that the association between the high-dimensionality of data and the overfitting problem, it is very important to establish a strategy to prevent the overfitting problem. However, there has been little discussion of the strategy. OBJECTIVE This work aimed to develop an overfitting-resistant deep learning model that uses preoperative patient data along with intraoperative bio-signal information to predict the IOH about 5 minutes prior to its occurrence. METHODS Mean arterial blood pressure (2 sec interval) and electronic medical records of 990 patients from open-source database, VitalDB were integrated for this study. The IOH was defined as an MBP < 65 mmHg for >1 min. Our proposed deep learning model accommodates the dropout method for preventing overfitting and the permutation method for reducing the dependence of the American Society of Anesthesiologists (ASA) status on IOH; we permuted the ASA status in the process of model training. The primary outcome was evaluated in terms of the area under the receiver operating characteristic curve (AUROC). RESULTS The model with the permutation method showed better performance (AUROC, 95% confidence interval [CI]: 0.842, 0.838-0.845) than that of model without the permutation method (AUROC, 95% CI: 0.830, 0.825-0.835). Furthermore, the model with both the permutation and dropout methods exhibited the best performance (AUROC, 95% CI: 0.862, 0.859-0.861). CONCLUSIONS Our work demonstrated the effectiveness of the permutation method in preventing the overfitting problem. Ultimately, the introduction of the permutation of the ASA status and dropout methods into a deep learning model can prevent the overfitting problem and improve the accuracy of IOH prediction. CLINICALTRIAL
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Jeong et al. (2022) studied Intraoperative hypotension (n=990). Deep learning model with permutation and dropout methods vs. Model without permutation method was evaluated on Area under the receiver operating characteristic curve (AUROC) for predicting intraoperative hypotension (MBP < 65 mmHg for >1 min) (AUROC 0.862, 95% CI 0.859-0.861). A deep learning model using permutation and dropout methods predicted intraoperative hypotension with an AUROC of 0.862 (95% CI 0.859-0.861), outperforming models without these methods.
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