Mather Rory Vu, Nipp Ryan, Balanza Gustavo, Stone Tom A D, Gutierrez Rodrigo, Raje Praachi, Higuchi Masaya, Liu Ran, Santa Cruz Mercado Laura A, Bittner Edward A, Kunitake Hiroko, Purdon Patrick L
Harvard/MIT MD-PhD Program, Boston, Massachusetts; Harvard-MIT Program in Health Sciences and Technology, Cambridge, Massachusetts; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts; Department of Anesthesia, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, California.
Section of Hematology/Oncology, Department of Internal Medicine, University of Oklahoma, Health Sciences Center, Stephenson Cancer Center, Oklahoma City, Oklahoma.
Anesthesiology. 2025 Mar 1;142(3):500-510. doi: 10.1097/ALN.0000000000005315. Epub 2024 Nov 27.
With estimated global postoperative mortality rates at 1% to 4% leading to approximately 3 million to 12 million deaths per year, an urgent need exists for reliable measures of perioperative risk. Existing approaches suffer from poor performance, place a high burden on clinicians to gather data, or do not incorporate intraoperative data. Previous work demonstrated that intraoperative anesthetics induce prefrontal electroencephalogram (EEG) oscillations in the alpha band (8 to 12 Hz) that correlate with postoperative cognitive outcomes.
The authors analyzed a retrospective cohort of 1,081 patients undergoing surgery with general anesthesia at Massachusetts General Hospital (Boston, Massachusetts) with intraoperative EEG recordings. The association between EEG alpha power and adverse outcomes was characterized using statistical models that were fitted on propensity weighted data. The primary outcome was postoperative mortality, measured from date of surgery to date of death or last follow-up. Secondary outcomes included mortality within prespecified time windows (30 days, 90 days, 180 days, and 1 yr), hospital and postanesthesia care unit lengths of stay, discharge to long-term care, and 30-day hospital readmission.
Alpha power was associated with mortality risk (hazard ratio, 0.92; 95% CI, 0.85 to 0.99; P = 0.039). Within specified time windows, alpha power was associated with 30-day mortality (odds ratio, 0.81; 95% CI, 0.66 to 0.95; P = 0.010), 90-day mortality (odds ratio, 0.68; 95% CI, 0.55 to 0.79; P < 0.001), 180-day mortality (odds ratio, 0.75; 95% CI, 0.66 to 0.83; P < 0.001), and 1-yr mortality (odds ratio, 0.85; 95% CI, 0.79 to 0.91; P < 0.001). Additionally, alpha power was associated with discharge to long-term care (odds ratio, 0.91; 95% CI, 0.86 to 0.96; P < 0.001). We did not find significant associations among alpha power and 30-day readmission and hospital or postanesthesia care unit lengths of stay.
Intraoperative EEG alpha power is independently associated with postoperative mortality and adverse outcomes, suggesting it could represent a broad measure of postoperative physical resilience and provide clinicians with a low-burden, personalized measure of postoperative risk.
据估计,全球术后死亡率为1%至4%,每年导致约300万至1200万人死亡,因此迫切需要可靠的围手术期风险评估措施。现有方法存在性能不佳、临床医生收集数据负担过重或未纳入术中数据等问题。先前的研究表明,术中麻醉会诱发前额叶脑电图(EEG)在α波段(8至12赫兹)出现振荡,这与术后认知结果相关。
作者分析了马萨诸塞州综合医院(马萨诸塞州波士顿)1081例接受全身麻醉手术且有术中脑电图记录的患者的回顾性队列。使用倾向加权数据拟合的统计模型来描述脑电图α波功率与不良结局之间的关联。主要结局是术后死亡率,从手术日期至死亡日期或最后一次随访进行测量。次要结局包括在预先指定的时间窗口(30天、90天、180天和1年)内的死亡率、住院时间和麻醉后护理单元住院时间、转至长期护理机构以及30天内再次入院情况。
α波功率与死亡风险相关(风险比,0.92;95%置信区间,0.85至0.99;P = 0.039)。在特定时间窗口内,α波功率与30天死亡率相关(优势比,0.81;95%置信区间,0.66至0.95;P = 0.010)、90天死亡率相关(优势比,0.68;95%置信区间,0.55至0.79;P < 0.001)、180天死亡率相关(优势比,0.75;95%置信区间,0.66至0.83;P < 0.001)以及1年死亡率相关(优势比,0.85;95%置信区间,0.79至0.91;P < 0.001)。此外,α波功率与转至长期护理机构相关(优势比,0.91;95%置信区间,0.86至0.96;P < 0.001)。我们未发现α波功率与30天再次入院情况以及住院时间或麻醉后护理单元住院时间之间存在显著关联。
术中脑电图α波功率与术后死亡率和不良结局独立相关,这表明它可能代表术后身体恢复能力的一种广泛衡量指标,并为临床医生提供一种低负担、个性化的术后风险评估方法。