Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis.

IF 9.1 1区 医学 Q1 ANESTHESIOLOGY British journal of anaesthesia Pub Date : 2024-09-24 DOI:10.1016/j.bja.2024.08.007
Divya Mehta,Xiomara T Gonzalez,Grace Huang,Joanna Abraham
{"title":"Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis.","authors":"Divya Mehta,Xiomara T Gonzalez,Grace Huang,Joanna Abraham","doi":"10.1016/j.bja.2024.08.007","DOIUrl":null,"url":null,"abstract":"BACKGROUND\r\nWe lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on perioperative outcomes.\r\n\r\nMETHODS\r\nOvid MEDLINE, CINAHL, Embase, Scopus, PubMed, and ClinicalTrials.gov were searched to identify randomised controlled trials (RCTs) evaluating the effectiveness of ML-driven interventions in surgical inpatient populations. The review was registered with PROSPERO (CRD42023433163) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Meta-analysis was conducted for outcomes with two or more studies using a random-effects model, and vote counting was conducted for other outcomes.\r\n\r\nRESULTS\r\nAmong 13 included RCTs, three types of ML-driven interventions were evaluated: Hypotension Prediction Index (HPI) (n=5), Nociception Level Index (NoL) (n=7), and a scheduling system (n=1). Compared with the standard care, HPI led to a significant decrease in absolute hypotension (n=421, P=0.003, I2=75%) and relative hypotension (n=208, P<0.0001, I2=0%); NoL led to significantly lower mean pain scores in the post-anaesthesia care unit (PACU) (n=191, P=0.004, I2=19%). NoL showed no significant impact on intraoperative opioid consumption (n=339, P=0.31, I2=92%) or PACU opioid consumption (n=339, P=0.11, I2=0%). No significant difference in hospital length of stay (n=361, P=0.81, I2=0%) and PACU stay (n=267, P=0.44, I2=0) was found between HPI and NoL.\r\n\r\nCONCLUSIONS\r\nHPI decreased the duration of intraoperative hypotension, and NoL decreased postoperative pain scores, but no significant impact on other clinical outcomes was found. We highlight the need to address both methodological and clinical practice gaps to ensure the successful future implementation of ML-driven interventions.\r\n\r\nSYSTEMATIC REVIEW PROTOCOL\r\nCRD42023433163 (PROSPERO).","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":null,"pages":null},"PeriodicalIF":9.1000,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"British journal of anaesthesia","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.bja.2024.08.007","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ANESTHESIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

BACKGROUND We lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on perioperative outcomes. METHODS Ovid MEDLINE, CINAHL, Embase, Scopus, PubMed, and ClinicalTrials.gov were searched to identify randomised controlled trials (RCTs) evaluating the effectiveness of ML-driven interventions in surgical inpatient populations. The review was registered with PROSPERO (CRD42023433163) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Meta-analysis was conducted for outcomes with two or more studies using a random-effects model, and vote counting was conducted for other outcomes. RESULTS Among 13 included RCTs, three types of ML-driven interventions were evaluated: Hypotension Prediction Index (HPI) (n=5), Nociception Level Index (NoL) (n=7), and a scheduling system (n=1). Compared with the standard care, HPI led to a significant decrease in absolute hypotension (n=421, P=0.003, I2=75%) and relative hypotension (n=208, P<0.0001, I2=0%); NoL led to significantly lower mean pain scores in the post-anaesthesia care unit (PACU) (n=191, P=0.004, I2=19%). NoL showed no significant impact on intraoperative opioid consumption (n=339, P=0.31, I2=92%) or PACU opioid consumption (n=339, P=0.11, I2=0%). No significant difference in hospital length of stay (n=361, P=0.81, I2=0%) and PACU stay (n=267, P=0.44, I2=0) was found between HPI and NoL. CONCLUSIONS HPI decreased the duration of intraoperative hypotension, and NoL decreased postoperative pain scores, but no significant impact on other clinical outcomes was found. We highlight the need to address both methodological and clinical practice gaps to ensure the successful future implementation of ML-driven interventions. SYSTEMATIC REVIEW PROTOCOL CRD42023433163 (PROSPERO).
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
围手术期护理中的机器学习增强干预:系统回顾和荟萃分析。
背景我们缺乏有关围手术期机器学习(ML)驱动的干预措施累积效果的证据。因此,我们进行了一项系统性综述,以评估有关 ML 驱动的干预措施对围术期结果的影响的证据。我们检索了 MEDLINE、CINAHL、Embase、Scopus、PubMed 和 ClinicalTrials.gov,以确定评估 ML 驱动的干预措施在外科住院患者中的有效性的随机对照试验 (RCT)。该综述已在 PROSPERO(CRD42023433163)上注册,并按照《系统综述和元分析首选报告项目》(PRISMA)指南进行。采用随机效应模型对有两项或更多研究的结果进行了 Meta 分析,并对其他结果进行了计票。结果在 13 项纳入的 RCT 中,评估了三种 ML 驱动的干预措施:低血压预测指数(HPI)(5 例)、痛觉水平指数(NoL)(7 例)和调度系统(1 例)。与标准护理相比,HPI 显著降低了绝对低血压(421 人,P=0.003,I2=75%)和相对低血压(208 人,P<0.0001,I2=0%);NoL 显著降低了麻醉后护理病房(PACU)的平均疼痛评分(191 人,P=0.004,I2=19%)。NoL对术中阿片类药物消耗量(n=339,P=0.31,I2=92%)或PACU阿片类药物消耗量(n=339,P=0.11,I2=0%)无明显影响。HPI和NoL的住院时间(n=361,P=0.81,I2=0%)和PACU住院时间(n=267,P=0.44,I2=0)无明显差异。我们强调需要解决方法学和临床实践两方面的差距,以确保未来成功实施 ML 驱动的干预措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
13.50
自引率
7.10%
发文量
488
审稿时长
27 days
期刊介绍: The British Journal of Anaesthesia (BJA) is a prestigious publication that covers a wide range of topics in anaesthesia, critical care medicine, pain medicine, and perioperative medicine. It aims to disseminate high-impact original research, spanning fundamental, translational, and clinical sciences, as well as clinical practice, technology, education, and training. Additionally, the journal features review articles, notable case reports, correspondence, and special articles that appeal to a broader audience. The BJA is proudly associated with The Royal College of Anaesthetists, The College of Anaesthesiologists of Ireland, and The Hong Kong College of Anaesthesiologists. This partnership provides members of these esteemed institutions with access to not only the BJA but also its sister publication, BJA Education. It is essential to note that both journals maintain their editorial independence. Overall, the BJA offers a diverse and comprehensive platform for anaesthetists, critical care physicians, pain specialists, and perioperative medicine practitioners to contribute and stay updated with the latest advancements in their respective fields.
期刊最新文献
A resuscitation tool for major obstetric haemorrhage: a nomogram that expresses quantitative blood loss relative to effective circulating blood volume. Balanced electrolyte solution with 1% glucose as intraoperative maintenance fluid in infants: a prospective study of glucose, electrolyte, and acid-base homeostasis. It is time to take a broader equity lens to highlight health inequalities in people with pain. The role of wearable technology in home-based prehabilitation: a scoping review. Corrigendum to "Effect of prolonged sedation with dexmedetomidine, midazolam, propofol, and sevoflurane on sleep homeostasis in rats" [Br J Anaesth 132 (2024) 1248-1259].
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1