Predicting postoperative nausea and vomiting using machine learning: a model development and validation study.

IF 2.3 3区 医学 Q2 ANESTHESIOLOGY
Maxim Glebov, Teddy Lazebnik, Maksim Katsin, Boris Orkin, Haim Berkenstadt, Svetlana Bunimovich-Mendrazitsky
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引用次数: 0

Abstract

Background: Postoperative nausea and vomiting (PONV) is a frequently observed complication in patients undergoing surgery under general anesthesia. Moreover, it is a frequent cause of distress and dissatisfaction in the early postoperative period. Currently, the classical scores used for predicting PONV have not yielded satisfactory results. Therefore, prognostic models for the prediction of early and delayed PONV were developed in this study to achieve satisfactory predictive performance.

Methods: The retrospective data of inpatient adult patients admitted to the post-anesthesia care unit after undergoing surgical procedures under general anesthesia at the Sheba Medical Center, Israel, between September 1, 2018, and September 1, 2023, were used in this study. An ensemble model of machine-learning algorithms trained on the data of 35,003 patients was developed. The k-fold cross-validation method was used followed by splitting the data to train and test sets that optimally preserve the sociodemographic features of the patients.

Results: Among the 35,003 patients, early and delayed PONV were observed in 1,340 (3.82%) and 6,582 (18.80%) patients, respectively. The proposed PONV prediction models correctly predicted early and delayed PONV in 83.6% and 74.8% of cases, respectively, outperforming the second-best PONV prediction score (Koivuranta score) by 13.0% and 10.4%, respectively. Feature importance analysis revealed that the performance of the proposed prediction tools aligned with previous clinical knowledge, indicating their utility.

Conclusions: The machine learning-based models developed in this study enabled improved PONV prediction, thereby facilitating personalized care and improved patient outcomes.

利用机器学习预测术后恶心和呕吐:模型开发与验证研究。
背景:术后恶心和呕吐(PONV)是全麻手术患者常见的并发症。此外,这也是术后早期患者焦虑和不满的常见原因。目前,用于预测PONV的经典分数并没有取得令人满意的结果。因此,本研究开发了预测早期和延迟PONV的预后模型,以达到令人满意的预测效果。方法:回顾性分析2018年9月1日至2023年9月1日在以色列示巴医疗中心(Sheba Medical Center)接受全身麻醉手术后入住麻醉后护理病房的成年住院患者的资料。开发了一个基于35,003名患者数据训练的机器学习算法集成模型。使用k-fold交叉验证方法,然后将数据分割为训练和测试集,以最佳地保留患者的社会人口统计学特征。结果:35003例患者中,出现早期和迟发性PONV的分别为1340例(3.82%)和6582例(18.80%)。所提出的PONV预测模型对早期和延迟PONV的预测准确率分别为83.6%和74.8%,比第二好的PONV预测评分(Koivuranta评分)分别高出13.0%和10.4%。特征重要性分析显示,所提出的预测工具的性能与以前的临床知识一致,表明它们的实用性。结论:本研究中开发的基于机器学习的模型能够改进PONV预测,从而促进个性化护理并改善患者预后。
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 Anesthesiology
BMC Anesthesiology ANESTHESIOLOGY-
CiteScore
3.50
自引率
4.50%
发文量
349
审稿时长
>12 weeks
期刊介绍: BMC Anesthesiology is an open access, peer-reviewed journal that considers articles on all aspects of anesthesiology, critical care, perioperative care and pain management, including clinical and experimental research into anesthetic mechanisms, administration and efficacy, technology and monitoring, and associated economic issues.
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