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. Trial Registration ClinicalTrials.gov Identifier: NCT03376347.

中文翻译：

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机器学习衍生的术中低血压预警系统与标准护理对择期非心脏手术期间术中低血压深度和持续时间的影响

重要性 术中低血压与发病率和死亡率增加有关。已经开发并验证了一种机器学习衍生的早期预警系统，可在低血压发生之前对其进行预测。目的检验预警系统的临床应用结合血流动力学诊断指导和治疗方案是否能降低术中低血压。设计、设置和参与者 在荷兰阿姆斯特丹的一个三级中心进行的初步非盲随机临床试验，对象是计划在全身麻醉下进行择期非心脏手术并有持续有创血压监测指征的成年患者，这些患者在 2018 年 5 月至2019 年 3 月。低血压定义为平均动脉压 (MAP) 低于 65 mmHg 至少 1 分钟。干预 患者被随机分配接受早期预警系统（n = 34）或标准治疗（n = 34），两组的目标 MAP 均至少为 65 mmHg。主要结果和测量主要结果是手术期间低血压的时间加权平均值，单位为毫米汞柱。这计算为 MAP 低于 65 毫米汞柱（以毫米汞柱为单位）的低血压深度 × 低于 65 毫米汞柱的时间（以分钟为单位）除以总手术时间（以分钟为单位）。结果 在 68 名随机分配的患者中，60 名 (88%) 完成了试验（中位年龄，64 [四分位距 {IQR}，57-70] 岁；26 [43%] 名女性）。手术时间中位数为 256 分钟（IQR，213-430 分钟）。干预组的低血压时间加权平均值中位数为 0.10 毫米汞柱（IQR，0.01-0.43 毫米汞柱），而对照组为 0.44 毫米汞柱（IQR，0.23-0.72 毫米汞柱），中位数差异为 0.38 毫米汞柱汞（95% CI，0.14-0.43 毫米汞柱；P = .001）。干预组每位患者的低血压中位时间为 8.0 分钟（IQR，1.33-26.00 分钟），而对照组为 32.7 分钟（IQR，11.5-59.7 分钟），中位差异为 16.7 分钟（95% CI， 7.7-31.0 分钟；P < .001)。干预组发生了 0 起导致死亡的严重不良事件，而对照组发生了 2 起 (7%)。结论和相关性 在这项针对接受择期非心脏手术的患者的单中心初步研究中，与标准护理相比，使用机器学习衍生的早期预警系统可减少术中低血压。需要在不同环境中对更大的研究人群进行进一步研究，以了解对额外患者结果的影响并全面评估安全性和普遍性。试验注册 ClinicalTrials.gov 标识符：NCT03376347。