BACKGROUND 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.

中文翻译：

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使用自动分类模型定义诱导后血流动力学不稳定。


背景 诱导后低血压 （PIH） 可能与发病率和死亡率增加有关。在早期的研究中，PIH 的定义完全基于不同的绝对或相对阈值。然而，时间进程（例如，诱导过程中血压下降的速度）很少被纳入，而它可能代表患者的血流动力学不稳定。我们提出了一个综合模型，通过将血压阈值与下降的幅度和速度相结合，来区分血流动力学不稳定的患者和稳定的患者。方法 这项前瞻性研究包括 375 例成人择期非心脏手术患者。在第一次诱导剂给药前 5 分钟至 15 分钟之间连续测量无创血压。专家组对患者是否经历过临床相关的血流动力学不稳定进行了评估。计算评分者间相关系数和类内相关性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、舒张动脉压 （分别为 SAP、MAP 和 DAP） 及其相应的下降时间进程开发了临床相关血流动力学不稳定的自动分类模型。该模型根据专家提供的血流动力学不稳定标签进行训练和测试。结果 共有 78 例患者被归类为血流动力学不稳定，279 例未被归类为未出现。血流动力学不稳定的患者年龄显著较大 （7 岁，95% 置信区间 （CI），4-11，P < .001），慢性阻塞性肺病 （COPD） 患病率较高 （高 3%，95% CI，1-8，P = .036）。 与血流动力学稳定的患者相比，诱导前血流动力学不稳定的患者具有更高的 SAP （中位数 （第一-三分之一） 四分位数：161 （145-175） mm Hg vs 150 （134-166） mm Hg，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优异的性能，受试者工作曲线下面积 （AUROC） 为 0.96，灵敏度为 0.84，特异性为 0.94。结论 基于高敏感性和特异性，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。该分类模型将为未来有关血流动力学不稳定及其预防的研究铺平道路。