背景:术中低血压(IOH)在外科手术过程中具有相当大的风险。人工智能(AI)在预测IOH中的集成有望增强检测能力,提供改善患者预后的机会。这篇系统综述和荟萃分析探讨了AI和IOH预测的交叉点,解决手术环境中有效监测的关键需求。
方法:搜索Pubmed,Scopus,WebofScience,进行了Embase。筛查涉及独立审核员的两阶段评估,确保遵守预定义的PICOS标准。纳入的研究集中在AI模型预测任何类型的手术中的IOH。由于评估低血压预测指数(HPI)的研究数量众多,我们进行了两组荟萃分析:一组涉及HPI研究,一组包括非HPI研究.在HPI研究中,分析了以下结果:每位患者的IOH累积持续时间,平均动脉压的时间加权平均值<65(TWA-MAP<65),平均动脉压阈值下的面积(AUT-MAP),和接受者工作特征曲线下面积(AUROC)。在非HPI研究中,我们检查了除HPI以外的所有AI模型的合并AUROC。
结果:43项研究纳入本综述。研究表明,IOH持续时间显着减少,TWA-MAP<65mmHg,在使用HPI的组中,AUT-MAP<65mmHg。HPI算法的AUROC表现出强的预测性能(AUROC=0.89,95CI)。非HPI模型的合并AUROC为0.79(95CI:0.74,0.83)。
结论:HPI显示出预测低血压发作的优异能力,从而减少低血压的持续时间。其他AI模型,特别是那些基于深度学习的方法,也表明了很好的预测IOH的能力,而他们减少IOH相关指标的能力,如持续时间仍不清楚。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.