荷兰阿姆斯特丹UMC的Alexander P. Vlaar小组，比较了基于机器学习的早期预警系统和标准治疗对择期非心脏手术中低血压深度和持续时间的影响。相关论文于2020年2月17日发表于国际顶尖学术期刊《美国医学会杂志》。

术中低血压与发病率和死亡率的增加有关。目前已研发出一种基于机器学习的早期报警系统对术中低血压进行预测。

为了探讨早期预警系统结合血流动力学诊断指导治疗方案在临床应用中是否能减少术中低血压，研究组在荷兰阿姆斯特丹的一个三级医疗中心进行了一项初步、非盲、随机、临床试验，2018年5月至2019年3月，招募了68例计划行择期全麻非心脏手术的成人患者，均适应持续有创血压监测。

将68例患者随机分配，34例接受早期预警系统，34例接受标准治疗。低血压定义为平均动脉压（MAP）低于65mmHg至少1分钟。低血压时间加权平均值定义为血压低于65mmHg的深度×低于65mmHg的时间÷手术总持续时间。

最终有60例患者完成了试验。手术的中位时间为256分钟。干预组低血压的时间加权平均值为0.10mmHg，对照组为0.44mmHg，差异显著。干预组患者低血压的中位时间为8.0分钟，对照组为32.7分钟，差异显著。干预组未发生严重不良事件导致的死亡，对照组发生2例（7%）。

总之，对于择期非心脏手术患者，与标准治疗相比，基于机器学习的早期预警系统可有效减少术中低血压。

附：英文原文

Title: Effect of a Machine Learning–Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial

Author: Marije Wijnberge, Bart F. Geerts, Liselotte Hol, Nikki Lemmers, Marijn P. Mulder, Patrick Berge, Jimmy Schenk, Lotte E. Terwindt, Markus W. Hollmann, Alexander P. Vlaar, Denise P. Veelo

Issue&Volume: February 17, 2020

Abstract: Importance  Intraoperative hypotension is associated with increased morbidity and mortality. A machine learning–derived early warning system to predict hypotension shortly before it occurs has been developed and validated.Objective  To test whether the clinical application of the early warning system in combination with a hemodynamic diagnostic guidance and treatment protocol reduces intraoperative hypotension.Design, Setting, and Participants  Preliminary unblinded randomized clinical trial performed in a tertiary center in Amsterdam, the Netherlands, among adult patients scheduled for elective noncardiac surgery under general anesthesia and an indication for continuous invasive blood pressure monitoring, who were enrolled between May 2018 and March 2019. Hypotension was defined as a mean arterial pressure (MAP) below 65 mm Hg for at least 1 minute.Interventions  Patients were randomly assigned to receive either the early warning system (n=34) or standard care (n=34), with a goal MAP of at least 65 mm Hg in both groups.Main Outcomes and Measures  The primary outcome was time-weighted average of hypotension during surgery, with a unit of measure of millimeters of mercury. This was calculated as the depth of hypotension below a MAP of 65 mm Hg (in millimeters of mercury) × time spent below a MAP of 65 mm Hg (in minutes) divided by total duration of operation (in minutes).Results  Among 68 randomized patients, 60 (88%) completed the trial (median age, 64 [interquartile range {IQR}, 57-70] years; 26 [43%] women). The median length of surgery was 256 minutes (IQR, 213-430 minutes). The median time-weighted average of hypotension was 0.10 mm Hg (IQR, 0.01-0.43 mm Hg) in the intervention group vs 0.44 mm Hg (IQR, 0.23-0.72 mm Hg) in the control group, for a median difference of 0.38 mm Hg (95% CI, 0.14-0.43 mm Hg; P=.001). The median time of hypotension per patient was 8.0 minutes (IQR, 1.33-26.00 minutes) in the intervention group vs 32.7 minutes (IQR, 11.5-59.7 minutes) in the control group, for a median difference of 16.7 minutes (95% CI, 7.7-31.0 minutes; P.001). In the intervention group, 0 serious adverse events resulting in death occurred vs 2 (7%) in the control group.Conclusions and Relevance  In this single-center preliminary study of patients undergoing elective noncardiac surgery, the use of a machine learning–derived early warning system compared with standard care resulted in less intraoperative hypotension. Further research with larger study populations in diverse settings is needed to understand the effect on additional patient outcomes and to fully assess safety and generalizability.

DOI: 10.1001/jama.2020.0592

Source: https://jamanetwork.com/journals/jama/fullarticle/2761469