Department of Neurology, South China Hospital of Shenzhen University.
Department of Neurology, the First Affiliated Hospital of Shenzhen University Shenzhen Second People's Hospital.
Neurologist. 2022 Nov 1;27(6):319-323. doi: 10.1097/NRL.0000000000000422.
Dynamic cerebral autoregulation (CA) is known to be impaired in patients with acute ischemic stroke (AIS), but whether or not dynamic CA can predict long-term outcomes is unclear.
This prospective study included 103 patients with AIS between September 2017 and April 2019. We measured the middle cerebral artery blood flow velocity and blood pressure within 7 days of AIS onset using a transcranial Doppler and Finometer, respectively. We conducted transfer function analysis to calculate dynamic CA indices (phase and gain), with lower phase and higher gain parameters reflecting less efficient CA. We followed up all patients after 3 and 12 months. Patients with 12-month modified Rankin Scale scores of <2 and ≥2 were defined as having favorable and unfavorable outcomes, respectively. We then analyzed the predictors of unfavorable outcomes after 3 and 12 months using logistic regression.
The ipsilesional phase parameter was significantly lower in patients with unfavorable outcomes than in those with favorable outcomes. Multiple logistic regression analysis revealed that the ipsilesional phase parameter and the National Institutes of Health Stroke Scale score were nonmodifiable predictors of short-term and long-term outcomes. Moreover, in receiver operating characteristic analysis, the area under the curve of the ipsilesional phase parameter was 0.646 (95% confidence interval: 0.513-0.779, P =0.044). Notably, the optimal cut-off value was 20.33 degrees (sensitivity: 63%, specificity: 70%).
Dynamic CA is an independent predictor of outcomes at 3 and 12 months in patients with AIS.
已知急性缺血性脑卒中(AIS)患者的动态脑自动调节(CA)受损,但动态 CA 是否能预测长期预后尚不清楚。
本前瞻性研究纳入了 2017 年 9 月至 2019 年 4 月期间的 103 例 AIS 患者。我们分别使用经颅多普勒和 Finometer 在 AIS 发病后 7 天内测量大脑中动脉血流速度和血压。我们进行传递函数分析以计算动态 CA 指数(相位和增益),较低的相位和较高的增益参数反映 CA 效率较低。所有患者在 3 个月和 12 个月后进行随访。将 12 个月时改良 Rankin 量表评分<2 和≥2 的患者分别定义为预后良好和预后不良。然后我们使用逻辑回归分析了 3 个月和 12 个月时预后不良的预测因素。
预后不良的患者患侧相位参数明显低于预后良好的患者。多变量逻辑回归分析显示,患侧相位参数和美国国立卫生研究院卒中量表评分是短期和长期预后的不可变预测因素。此外,在接受者操作特征分析中,患侧相位参数的曲线下面积为 0.646(95%置信区间:0.513-0.779,P=0.044)。值得注意的是,最佳截断值为 20.33 度(敏感性:63%,特异性:70%)。
动态 CA 是 AIS 患者 3 个月和 12 个月时结局的独立预测因素。