Deep learning models for the prediction of acute postoperative pain in PACU for video-assisted thoracoscopic surgery.

IF 3.9 3区医学 Q1 HEALTH CARE SCIENCES & SERVICES

BMC Medical Research Methodology Pub Date : 2024-10-07 DOI:10.1186/s12874-024-02357-5

Cao Zhang, Jiangqin He, Xingyuan Liang, Qinye Shi, Lijia Peng, Shuai Wang, Jiannan He, Jianhong Xu

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引用次数: 0

Abstract

Background: Postoperative pain is a prevalent symptom experienced by patients undergoing surgical procedures. This study aims to develop deep learning algorithms for predicting acute postoperative pain using both essential patient details and real-time vital sign data during surgery.

Methods: Through a retrospective observational approach, we utilized Graph Attention Networks (GAT) and graph Transformer Networks (GTN) deep learning algorithms to construct the DoseFormer model while incorporating an attention mechanism. This model employed patient information and intraoperative vital signs obtained during Video-assisted thoracoscopic surgery (VATS) surgery to anticipate postoperative pain. By categorizing the static and dynamic data, the DoseFormer model performed binary classification to predict the likelihood of postoperative acute pain.

Results: A total of 1758 patients were initially included, with 1552 patients after data cleaning. These patients were then divided into training set (n = 931) and testing set (n = 621). In the testing set, the DoseFormer model exhibited significantly higher AUROC (0.98) compared to classical machine learning algorithms. Furthermore, the DoseFormer model displayed a significantly higher F1 value (0.85) in comparison to other classical machine learning algorithms. Notably, the attending anesthesiologists' F1 values (attending: 0.49, fellow: 0.43, Resident: 0.16) were significantly lower than those of the DoseFormer model in predicting acute postoperative pain.

Conclusions: Deep learning model can predict postoperative acute pain events based on patients' basic information and intraoperative vital signs.

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用于预测视频辅助胸腔镜手术 PACU 急性术后疼痛的深度学习模型。

背景：术后疼痛是接受外科手术的患者普遍经历的症状。本研究旨在利用手术过程中患者的基本信息和实时生命体征数据，开发预测急性术后疼痛的深度学习算法：通过回顾性观察方法，我们利用图注意力网络（GAT）和图变换器网络（GTN）深度学习算法构建了 DoseFormer 模型，同时纳入了注意力机制。该模型利用在视频辅助胸腔镜手术（VATS）过程中获得的患者信息和术中生命体征来预测术后疼痛。通过对静态和动态数据进行分类，DoseFormer 模型进行了二元分类，以预测术后急性疼痛的可能性：最初共纳入了 1758 名患者，经过数据清理后为 1552 名患者。这些患者被分为训练集（931 人）和测试集（621 人）。在测试集中，DoseFormer 模型的 AUROC（0.98）明显高于传统的机器学习算法。此外，与其他经典机器学习算法相比，DoseFormer 模型的 F1 值（0.85）明显更高。值得注意的是，在预测术后急性疼痛方面，主治麻醉师的 F1 值（主治：0.49；研究员：0.43；住院医师：0.16）明显低于 DoseFormer 模型：结论：深度学习模型可根据患者的基本信息和术中生命体征预测术后急性疼痛事件。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Medical Research Methodology 医学-卫生保健

CiteScore

6.50

自引率

2.50%

发文量

298

审稿时长

3-8 weeks

期刊介绍： BMC Medical Research Methodology is an open access journal publishing original peer-reviewed research articles in methodological approaches to healthcare research. Articles on the methodology of epidemiological research, clinical trials and meta-analysis/systematic review are particularly encouraged, as are empirical studies of the associations between choice of methodology and study outcomes. BMC Medical Research Methodology does not aim to publish articles describing scientific methods or techniques: these should be directed to the BMC journal covering the relevant biomedical subject area.