Kho Eline, Immink Rogier V, van der Ster Bjorn J P, van der Ven Ward H, Schenk Jimmy, Hollmann Markus W, Tol Johan T M, Terwindt Lotte E, Vlaar Alexander P J, Veelo Denise P
From the Department of Anesthesiology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Department of Intensive Care, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Anesth Analg. 2025 Feb 1;140(2):444-452. doi: 10.1213/ANE.0000000000007315. Epub 2024 Oct 25.
Postinduction hypotension (PIH) may be associated with increased morbidity and mortality. In earlier studies, the definition of PIH is solely based on different absolute or relative thresholds. However, the time-course (eg, how fast blood pressure drops during induction) is rarely incorporated, whereas it might represent the hemodynamic instability of a patient. We propose a comprehensive model to distinguish hemodynamically unstable from stable patients by combining blood pressure thresholds with the magnitude and speed of decline.
This prospective study included 375 adult elective noncardiac surgery patients. Noninvasive blood pressure was continuously measured between 5 minutes before up to 15 minutes after the first induction agent had been administered. An expert panel rated whether the patient experienced clinically relevant hemodynamic instability or not. Interrater correlation coefficient and intraclass correlation were computed to check for consistency between experts. Next, an automated classification model for clinically relevant hemodynamic instability was developed using mean, maximum, minimum systolic, mean, diastolic arterial blood pressure (SAP, MAP, and DAP, respectively) and their corresponding time course of decline. The model was trained and tested based on the hemodynamic instability labels provided by the experts.
In total 78 patients were classified as having experienced hemodynamic instability and 279 as not. The hemodynamically unstable patients were significantly older (7 years, 95% confidence interval (CI), 4-11, P < .001), with a higher prevalence of chronic obstructive pulmonary disease (COPD) (3% higher, 95% CI, 1-8, P = .036). Before induction, hemodynamically unstable patients had a higher SAP (median (first-third quartile): 161 (145-175) mm Hg vs 150 (134-166) mm Hg, P < .001) compared to hemodynamic stable patients. Interrater agreement between experts was 0.92 (95% CI, 0.89-0.94). The random forest classifier model showed excellent performance with an area under the receiver operating curve (AUROC) of 0.96, a sensitivity of 0.84, and specificity of 0.94.
Based on the high sensitivity and specificity, the developed model is able to differentiate between clinically relevant hemodynamic instability and hemodynamic stable patients. This classification model will pave the way for future research concerning hemodynamic instability and its prevention.
诱导后低血压（PIH）可能与发病率和死亡率增加相关。在早期研究中，PIH的定义仅基于不同的绝对或相对阈值。然而，时间进程（例如，诱导期间血压下降的速度）很少被纳入，而它可能代表患者的血流动力学不稳定。我们提出一个综合模型，通过结合血压阈值与下降幅度和速度来区分血流动力学不稳定和稳定的患者。
这项前瞻性研究纳入了375例成年择期非心脏手术患者。在给予第一种诱导药物前5分钟至给药后15分钟期间持续测量无创血压。一个专家小组对患者是否经历临床相关的血流动力学不稳定进行评估。计算评分者间相关系数和组内相关系数以检查专家之间的一致性。接下来，使用平均、最大、最小收缩压、平均、舒张压动脉血压（分别为SAP、MAP和DAP）及其相应的下降时间进程，开发了一个用于临床相关血流动力学不稳定的自动分类模型。该模型基于专家提供的血流动力学不稳定标签进行训练和测试。
总共78例患者被分类为经历了血流动力学不稳定，279例被分类为未经历。血流动力学不稳定的患者年龄显著更大（7岁，95%置信区间（CI），4 - 11，P <.001），慢性阻塞性肺疾病（COPD）患病率更高（高3%，95%CI，1 - 8，P =.036）。诱导前，与血流动力学稳定的患者相比，血流动力学不稳定的患者SAP更高（中位数（第一 - 第三四分位数）：161（145 - 175）mmHg对150（134 - 166）mmHg，P <.001）。专家之间的评分者一致性为0.92（95%CI，0.89 - 0.94）。随机森林分类器模型表现出色，受试者工作特征曲线下面积（AUROC）为0.96，灵敏度为0.84，特异性为0.94。
基于高灵敏度和特异性，所开发的模型能够区分临床相关的血流动力学不稳定和血流动力学稳定的患者。这种分类模型将为未来关于血流动力学不稳定及其预防的研究铺平道路。
