Performance of the Hypotension Prediction Index as an indicator of hypotensive haemodynamic instability in surgical patients.

IF 2.5 3区 医学 Q2 ANESTHESIOLOGY
Simon Davies, Sai Buddi, Zhongping Jian, Neal W Fleming, Maxime Cannesson, Michael Sander, Denise P Veelo, Alexander P J Vlaar, Thomas W L Scheeren, Monty Mythen, Feras Hatib
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Abstract

Background: The Hypotension Prediction Index (HPI) is a machine learning algorithm that was designed to detect alterations in the cardiovascular physiological control mechanisms that may lead to hypotension through multidimensional analyses of the physiological interactions of core haemodynamic parameters. Hypotension is a physiological condition where the central perfusion pressure in the cardiovascular system is too low to ensure adequate flow regulation of local organs and its threshold is the pressure at the lower limit of autoregulation. As such, hypotension can occur in a wide pressure range that can exceed considerably the conventional MAP threshold of 65 mmHg and can vary between individuals as well as within the same individual. We hypothesize that HPI is an indicator of haemodynamic instability. We used direct physiological signs of haemodynamic instability and hypoperfusion from the cardiovascular haemodynamic parameters available on the HPI technology. We conducted a retrospective analysis to investigate the performance of HPI as an indicator of haemodynamic instability related to hypotension and hypoperfusion.

Methods: A dataset of 1,683 cardiac and non-cardiac surgical patients were analyzed, including 871 patients monitored with invasive arterial line and 812 monitored with non-invasive finger cuff. There were 19,685 and 21,097 HPI alerts at the threshold of 85 and 50, respectively. Haemodynamic parameters used to define instability were MAP < 65 mmHg, SVV ≥ 13%, CI ≤ 2 L·min-1·m-2, and SVR ≤ 800 dyn·s·cm-5.

Results: In the invasive A-line dataset, 94.6% of the HPI alerts at threshold of 85 are followed in 15 min by haemodynamic instability, and 91.4% of HPI alerts at threshold 50 are followed by haemodynamic instability. In the non-invasive finger cuff dataset, they are 96.2% and 94.1% for HPI alerts at threshold 85 and 50, respectively.

Conclusion: The Hypotension Prediction Index predicts the upcoming hypotensive haemodynamic instability events with high accuracy. Further research is needed to investigate whether HPI alerts guided treatment of haemodynamic instability improves patient outcomes.

低血压预测指数作为外科患者低血压血流动力学不稳定指标的表现。
背景:低血压预测指数(HPI)是一种机器学习算法,旨在通过对核心血流动力学参数的生理相互作用的多维分析,检测可能导致低血压的心血管生理控制机制的改变。低血压是指心血管系统中心灌注压过低,无法保证局部器官充分的血流调节的生理状态,其阈值是处于自我调节下限的压力。因此,低血压可发生在很宽的血压范围内,可大大超过常规MAP阈值65mmhg,并且可在个体之间以及同一个体内变化。我们假设HPI是血流动力学不稳定的一个指标。我们使用了HPI技术上可用的心血管血流动力学参数中血流动力学不稳定和灌注不足的直接生理信号。我们进行了一项回顾性分析,以调查HPI作为与低血压和低灌注相关的血流动力学不稳定指标的性能。方法:分析1,683例心脏和非心脏手术患者的数据集,其中871例采用有创动脉线监测,812例采用无创指套监测。在阈值为85和50时,分别有19,685和21,097个HPI警报。用于定义不稳定性的血流动力学参数为MAP
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3.80%
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55
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10 weeks
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