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首页> 外文期刊>JAMA: the Journal of the American Medical Association >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
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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

机译:机床学习预警系统对术中低血压的影响与术中术后术后术后术后术后术治疗的疗效治疗炒作临床试验

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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) x 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.
机译:重要性术中低血压与发病率和死亡率增加有关。一台机器学习推导的预警系统,以在发生并验证之前不久预测低血压。目的探讨预警系统与血流动力学诊断引导和治疗方案组合的临床应用是否减少了术中低血压。在荷兰阿姆斯特丹阿姆斯特丹的第三节中心进行初步未粘连的随机临床试验,该临床试验在一般性麻醉下进行选修非心动手术的成年患者,以及在2018年5月和5月之间注册的持续侵入性血压监测的迹象2019年3月。低血压定义为低于65 mm Hg的平均动脉压(MAP)至少1分钟。干预患者被随机分配接收预警系统(n = 34)或标准护理(n = 34),两组中的至少65mm Hg的目标映射。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,其中汞毫米的单位。这是计算为低于65mm Hg(毫米汞)x的映射下方的低血压的深度,在65mm Hg(以分钟以分钟)的映射下,除以操作总操作时间(以分钟为单位)。结果68例随机患者之间,60名(88%)完成了审判(中位年龄,64 [四分位数{IQR},57-70]年; 26 [43%]女性)。手术的中位数为256分钟(IQR,213-430分钟)。低血压的中值时间加权平均值为0.10mm Hg(IQR,0.01-0.43mm Hg),在对照组中Vs 0.44mm Hg(IQR,0.23-0.72mm Hg),中位数差0.38 mm Hg(95%CI,0.14-0.43 mm Hg; p = .001)。每位患者的低血压的中位时间为8.0分钟(IQR,1.33-26.00分钟)在介入组VS 32.7分钟(IQR,11.5-59.7分钟)中,控制组中位数差异为16.7分钟(95%CI, 7.7-31.0分钟; p <.001)。在干预组中,对照组导致死亡导致死亡的0严重不良事件发生在2(7%)。结论与相关性在接受选修术患者的单中心初步研究,使用机器学习衍生的预警系统与标准治疗相比,导致术中低血压较少。需要在各种环境中进行更大的研究群体的进一步研究,以了解对额外患者结果的影响,并充分评估安全性和普遍性。

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