一种机器学习算法在手术中预测低血压的应用，结合个性化治疗指导：一项随机临床试验的研究方案。

The use of a machine-learning algorithm that predicts hypotension during surgery in combination with personalized treatment guidance: study protocol for a randomized clinical trial.

机构信息

Department of Anesthesiology, Amsterdam UMC, location Academic Medical Center, University of Amsterdam, Meibergdreef 9, Postbus 22660, 1105 AZ, Amsterdam, The Netherlands.

Department of Intensive Care Medicine, Amsterdam UMC, location Academic Medical Center, University of Amsterdam, Meibergdreef 9, Postbus 22660, 1105 AZ, Amsterdam, The Netherlands.

出版信息

Trials. 2019 Oct 11;20(1):582. doi: 10.1186/s13063-019-3637-4.

BACKGROUND

Intraoperative hypotension is associated with increased morbidity and mortality. Current treatment is mostly reactive. The Hypotension Prediction Index (HPI) algorithm is able to predict hypotension minutes before the blood pressure actually decreases. Internal and external validation of this algorithm has shown good sensitivity and specificity. We hypothesize that the use of this algorithm in combination with a personalized treatment protocol will reduce the time weighted average (TWA) in hypotension during surgery spent in hypotension intraoperatively.

METHODS/DESIGN: We aim to include 100 adult patients undergoing non-cardiac surgery with an anticipated duration of more than 2 h, necessitating the use of an arterial line, and an intraoperatively targeted mean arterial pressure (MAP) of > 65 mmHg. This study is divided into two parts; in phase A baseline TWA data from 40 patients will be collected prospectively. A device (HemoSphere) with HPI software will be connected but fully covered. Phase B is designed as a single-center, randomized controlled trial were 60 patients will be randomized with computer-generated blocks of four, six or eight, with an allocation ratio of 1:1. In the intervention arm the HemoSphere with HPI will be used to guide treatment; in the control arm the HemoSphere with HPI software will be connected but fully covered. The primary outcome is the TWA in hypotension during surgery.

DISCUSSION

The aim of this trial is to explore whether the use of a machine-learning algorithm intraoperatively can result in less hypotension. To test this, the treating anesthesiologist will need to change treatment behavior from reactive to proactive.

TRIAL REGISTRATION

This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT03376347 . The trial was submitted on 4 November 2017 and accepted for registration on 18 December 2017.

背景

术中低血压与发病率和死亡率增加有关。目前的治疗方法主要是被动的。低血压预测指数（HPI）算法能够在血压实际下降前几分钟预测低血压。该算法的内部和外部验证均显示出良好的灵敏度和特异性。我们假设在这种算法的基础上结合个性化治疗方案，将会减少手术中低血压时间加权平均值（TWA）。

方法/设计：我们计划纳入 100 例接受非心脏手术的成年患者，预计手术时间超过 2 小时，需要使用动脉导管，术中目标平均动脉压（MAP）> 65mmHg。该研究分为两个阶段；在阶段 A 中，前瞻性地收集 40 例患者的基线 TWA 数据。将连接一个带有 HPI 软件的设备（HemoSphere），但完全覆盖。阶段 B 设计为单中心、随机对照试验，将随机分配 60 例患者，采用计算机生成的四、六或八块分组，分配比例为 1:1。在干预组中，使用 HemoSphere 加 HPI 来指导治疗；在对照组中，将连接带有 HPI 软件的 HemoSphere，但完全覆盖。主要结局是手术中低血压的 TWA。