Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences (IUMS), Tehran, 14665-354, Iran.
Department of Surgery, Surgery Research Center, School of Medicine, Rasool-E Akram Hospital, Iran University of Medical Sciences, Tehran, Iran.
J Transl Med. 2024 Aug 5;22(1):725. doi: 10.1186/s12967-024-05481-4.
Intraoperative Hypotension (IOH) poses a substantial risk during surgical procedures. The integration of Artificial Intelligence (AI) in predicting IOH holds promise for enhancing detection capabilities, providing an opportunity to improve patient outcomes. This systematic review and meta analysis explores the intersection of AI and IOH prediction, addressing the crucial need for effective monitoring in surgical settings.
A search of Pubmed, Scopus, Web of Science, and Embase was conducted. Screening involved two-phase assessments by independent reviewers, ensuring adherence to predefined PICOS criteria. Included studies focused on AI models predicting IOH in any type of surgery. Due to the high number of studies evaluating the hypotension prediction index (HPI), we conducted two sets of meta-analyses: one involving the HPI studies and one including non-HPI studies. In the HPI studies the following outcomes were analyzed: cumulative duration of IOH per patient, time weighted average of mean arterial pressure < 65 (TWA-MAP < 65), area under the threshold of mean arterial pressure (AUT-MAP), and area under the receiver operating characteristics curve (AUROC). In the non-HPI studies, we examined the pooled AUROC of all AI models other than HPI.
43 studies were included in this review. Studies showed significant reduction in IOH duration, TWA-MAP < 65 mmHg, and AUT-MAP < 65 mmHg in groups where HPI was used. AUROC for HPI algorithms demonstrated strong predictive performance (AUROC = 0.89, 95CI). Non-HPI models had a pooled AUROC of 0.79 (95CI: 0.74, 0.83).
HPI demonstrated excellent ability to predict hypotensive episodes and hence reduce the duration of hypotension. Other AI models, particularly those based on deep learning methods, also indicated a great ability to predict IOH, while their capacity to reduce IOH-related indices such as duration remains unclear.
术中低血压（IOH）在手术过程中构成了实质性的风险。人工智能（AI）在预测 IOH 方面的集成有望提高检测能力，为改善患者结局提供机会。本系统评价和荟萃分析探讨了 AI 和 IOH 预测的交集，解决了手术环境中有效监测的关键需求。
对 Pubmed、Scopus、Web of Science 和 Embase 进行了检索。筛选涉及由独立评审员进行的两阶段评估，确保符合预定义的 PICOS 标准。纳入的研究集中在预测任何类型手术中 IOH 的 AI 模型。由于评估低血压预测指数（HPI）的研究数量众多，我们进行了两套荟萃分析：一套涉及 HPI 研究，另一套包括非 HPI 研究。在 HPI 研究中，分析了以下结果：每位患者的 IOH 持续时间、平均动脉压<65mmHg 的时间加权平均值（TWA-MAP<65mmHg）、平均动脉压阈值下面积（AUT-MAP）和接收者操作特征曲线下面积（AUROC）。在非 HPI 研究中，我们检查了除 HPI 以外的所有 AI 模型的汇总 AUROC。
本综述纳入了 43 项研究。研究表明，在使用 HPI 的组中，IOH 持续时间、TWA-MAP<65mmHg 和 AUT-MAP<65mmHg 显著减少。HPI 算法的 AUROC 显示出较强的预测性能（AUROC=0.89，95CI）。非 HPI 模型的汇总 AUROC 为 0.79（95CI：0.74，0.83）。
HPI 显示出预测低血压发作的出色能力，从而减少低血压的持续时间。其他 AI 模型，特别是基于深度学习方法的模型，也表明具有很好的预测 IOH 的能力，而其降低 IOH 相关指标（如持续时间）的能力尚不清楚。
