Effect of a Machine Learning-Derived Early Warning Tool With Treatment Protocol on Hypotension During Cardiac Surgery and ICU Stay: The Hypotension Prediction 2 (HYPE-2) Randomized Clinical Trial.

IF 7.7 1区 医学 Q1 CRITICAL CARE MEDICINE Critical Care Medicine Pub Date : 2024-11-22 DOI:10.1097/CCM.0000000000006518
Jaap Schuurmans, Santino R Rellum, Jimmy Schenk, Björn J P van der Ster, Ward H van der Ven, Bart F Geerts, Markus W Hollmann, Thomas G V Cherpanath, Wim K Lagrand, Paul R Wynandts, Frederique Paulus, Antoine H G Driessen, Lotte E Terwindt, Susanne Eberl, Henning Hermanns, Denise P Veelo, Alexander P J Vlaar
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Abstract

Objectives: Cardiac surgery is associated with perioperative complications, some of which might be attributable to hypotension. The Hypotension Prediction Index (HPI), a machine-learning-derived early warning tool for hypotension, has only been evaluated in noncardiac surgery. We investigated whether using HPI with diagnostic guidance reduced hypotension during cardiac surgery and in the ICU.

Design: Randomized clinical trial conducted between May 2021 and February 2023.

Setting: Single-center study conducted in an academic hospital in the Netherlands.

Patients: Adults undergoing elective on-pump coronary artery bypass grafting, with or without single heart valve surgery, were enrolled if a mean arterial pressure (MAP) greater than or equal to 65 mm Hg was targeted during the surgical off-pump phases and ICU stay. After eligibility assessment, 142 of 162 patients approached gave informed consent for participation.

Interventions: Patients randomized 1:1 received either diagnostic guidance in addition to standard care if HPI reached greater than or equal to 75 (n = 72) or standard care alone (n = 70).

Measurements and main results: The primary outcome was the severity of hypotension, measured as time-weighted average (TWA) of MAP less than 65 mm Hg. Secondary outcomes encompassed hypertension severity and intervention disparities. Of 142 patients randomized, 130 were included in the primary analysis. The HPI group showed 63% reduction in median TWA of hypotension compared with the standard care group, with a median of differences of -0.40 mm Hg (95% CI, -0.65 to -0.27; p < 0.001). In the HPI group, patients spent a median 28 minutes (95% CI, 17-44 min) less in hypotension, with a measurement duration of 322 minutes in the HPI group and 333 minutes in the standard care group. No significant differences were observed in hypertension severity, treatment choice, or fluid, vasopressors, and inotrope amounts.

Conclusions: Using HPI combined with diagnostic guidance on top of standard care significantly decreased hypotension severity in elective cardiac surgery patients compared with standard care.

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机器学习早期预警工具与治疗方案对心脏手术和重症监护室住院期间低血压的影响:低血压预测 2 (HYPE-2) 随机临床试验。
目的:心脏手术与围手术期并发症有关,其中一些并发症可能可归因于低血压。低血压预测指数(HPI)是一种通过机器学习得出的低血压预警工具,目前仅在非心脏手术中进行过评估。我们研究了在诊断指导下使用 HPI 是否能减少心脏手术和重症监护室中的低血压:设计:2021 年 5 月至 2023 年 2 月期间进行的随机临床试验:在荷兰一家学术医院进行的单中心研究:接受择期体外循环冠状动脉旁路移植术、单心瓣膜手术或非单心瓣膜手术的成人,如果在体外循环手术阶段和重症监护室住院期间平均动脉压(MAP)大于或等于 65 mm Hg,均可入选。经过资格评估,162 名患者中有 142 人在知情同意的情况下同意参与:患者按 1:1 随机分配,如果 HPI 达到大于或等于 75(n = 72),则在接受标准护理的同时接受诊断指导(n = 70),或仅接受标准护理(n = 70):主要结果是低血压的严重程度,以MAP低于65 mm Hg的时间加权平均值(TWA)来衡量。次要结果包括高血压严重程度和干预差异。在 142 名接受随机治疗的患者中,有 130 人被纳入主要分析。与标准护理组相比,HPI 组的低血压中位 TWA 降低了 63%,差异中位数为 -0.40 mm Hg(95% CI,-0.65 至 -0.27;P <0.001)。在 HPI 组中,患者在低血压状态下花费的时间中位数减少了 28 分钟(95% CI,17-44 分钟),HPI 组的测量持续时间为 322 分钟,标准护理组为 333 分钟。在高血压严重程度、治疗选择或液体、血管加压药和肌力药物用量方面未观察到明显差异:结论:与标准护理相比,在标准护理基础上使用 HPI 结合诊断指导可显著降低择期心脏手术患者的低血压严重程度。
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来源期刊
Critical Care Medicine
Critical Care Medicine 医学-危重病医学
CiteScore
16.30
自引率
5.70%
发文量
728
审稿时长
2 months
期刊介绍: Critical Care Medicine is the premier peer-reviewed, scientific publication in critical care medicine. Directed to those specialists who treat patients in the ICU and CCU, including chest physicians, surgeons, pediatricians, pharmacists/pharmacologists, anesthesiologists, critical care nurses, and other healthcare professionals, Critical Care Medicine covers all aspects of acute and emergency care for the critically ill or injured patient. Each issue presents critical care practitioners with clinical breakthroughs that lead to better patient care, the latest news on promising research, and advances in equipment and techniques.
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