Wood Michael D, Jacobson Jill A, Maslove David M, Muscedere John G, Boyd J Gordon
Centre for Neuroscience Studies, Queen's University, 18 Stuart St, Botterell Hall, Kingston, ON, Canada.
Department of Psychology, Queen's University, 62 Arch Street, 318 Craine Hall, Kingston, ON, Canada.
Intensive Care Med Exp. 2019 May 2;7(1):23. doi: 10.1186/s40635-019-0247-0.
To maintain adequate oxygen delivery to tissue, resuscitation of critically ill patients is guided by assessing surrogate markers of perfusion. As there is no direct indicator of cerebral perfusion used in routine critical care, identifying an accurate strategy to monitor brain perfusion is paramount. Near-infrared spectroscopy (NIRS) is a non-invasive technique to quantify regional cerebral oxygenation (rSO) that has been used for decades during cardiac surgery which has led to targeted algorithms to optimize rSO being developed. However, these targeted algorithms do not exist during critical care, as the physiological determinants of rSO during critical illness remain poorly understood.
This prospective observational study was an exploratory analysis of a nested cohort of patients within the CONFOCAL study ( NCT02344043 ) who received high-fidelity vital sign monitoring. Adult patients (≥ 18 years) admitted < 24 h to a medical/surgical intensive care unit were eligible if they had shock and/or required mechanical ventilation. Patients underwent rSO monitoring with the FORESIGHT oximeter for 24 h, vital signs were concurrently recorded, and clinically ordered arterial blood gas samples and hemoglobin concentration were also documented. Simultaneous multiple linear regression was performed using all available predictors, followed by model selection using the corrected Akaike information criterion (AICc).
Our simultaneous multivariate model included age, heart rate, arterial oxygen saturation, mean arterial pressure, pH, partial pressure of oxygen, partial pressure of carbon dioxide (PaCO), and hemoglobin concentration. This model accounted for a significant proportion of variance in rSO (R = 0.58, p < 0.01) and was significantly associated with PaCO (p < 0.05) and hemoglobin concentration (p < 0.01). Our selected regression model using AICc accounted for a significant proportion of variance in rSO (R = 0.54, p < 0.01) and was significantly related to age (p < 0.05), PaCO (p < 0.01), hemoglobin (p < 0.01), and heart rate (p < 0.05).
Known and established physiological determinants of oxygen delivery accounted for a significant proportion of the rSO signal, which provides evidence that NIRS is a viable modality to assess cerebral oxygenation in critically ill adults. Further elucidation of the determinants of rSO has the potential to develop a NIRS-guided resuscitation algorithm during critical illness.
This trial is registered on clinicaltrials.gov (Identifier: NCT02344043 ), retrospectively registered January 8, 2015.
为维持对组织的充足氧输送，危重症患者的复苏通过评估灌注替代指标来指导。由于在常规重症监护中没有用于评估脑灌注的直接指标，因此确定一种准确的监测脑灌注策略至关重要。近红外光谱（NIRS）是一种用于量化局部脑氧合（rSO）的非侵入性技术，已在心脏手术中使用了数十年，这促使人们开发了优化rSO的靶向算法。然而，在重症监护期间不存在这些靶向算法，因为对危重症期间rSO的生理决定因素仍知之甚少。
这项前瞻性观察性研究是对CONFOCAL研究（NCT02344043）中接受高保真生命体征监测的患者嵌套队列进行的探索性分析。年龄≥18岁、入住医疗/外科重症监护病房不足24小时且患有休克和/或需要机械通气的成年患者符合入选条件。患者使用FORESIGHT血氧仪进行24小时rSO监测，同时记录生命体征，并记录临床医嘱的动脉血气样本和血红蛋白浓度。使用所有可用预测因素进行多元线性回归，然后使用校正的赤池信息准则（AICc）进行模型选择。
我们的多元模型包括年龄、心率、动脉血氧饱和度、平均动脉压、pH值、氧分压、二氧化碳分压（PaCO）和血红蛋白浓度。该模型解释了rSO中很大一部分方差（R = 0.58，p < 0.01），并与PaCO（p < 0.05）和血红蛋白浓度（p < 0.01）显著相关。我们使用AICc选择的回归模型解释了rSO中很大一部分方差（R = 0.54，p < 0.01），并与年龄（p < 0.05）、PaCO（p < 0.01）、血红蛋白（p < 0.01）和心率（p < 0.05）显著相关。
已知的氧输送生理决定因素占rSO信号的很大一部分，这为NIRS是评估危重症成年患者脑氧合的可行方式提供了证据。进一步阐明rSO的决定因素有可能在危重症期间开发出一种NIRS指导的复苏算法。
本试验在clinicaltrials.gov上注册（标识符：NCT02344043），于2015年1月8日进行回顾性注册。
