Department of Neurology, Inselspital, University Hospital Bern, Bern, Switzerland.
Neurological Clinic, Klinikum Stuttgart, Stuttgart, Germany.
Eur J Neurol. 2021 Jul;28(7):2229-2237. doi: 10.1111/ene.14832. Epub 2021 May 5.
In order to identify risk periods with an increased demand in technical and human resources, we tried to determine patterns and associations in the incidence of acute ischemic stroke due to embolic large vessel occlusions (eLVO) requiring mechanical thrombectomy (MT).
We conducted a time series analysis over a 9-year period (2010-2018) based on observational data in order to detect seasonal patterns in the incidence of MT due to eLVO (n = 2628 patients). In a series of sequential negative binominal regression models, we aimed to detect further associations (e.g., temperature, atmospheric pressure, air pollution).
There was a 6-month seasonal pattern in the incidence of MT due to eLVO (p = 0.024) peaking in March and September. Colder overall temperature was associated with an increase in MT due to eLVO (average marginal effect [AME], [95% CI]: -0.15 [-0.30-0.0001]; p = 0.05; per °C). A current increase in the average monthly temperature was associated with a higher incidence of MT due to eLVO (0.34 [0.11-0.56]; p = 0.003). Atmospheric pressure was positively correlated with MT due to eLVO (0.38 [0.13-0.64]; p = 0.003; per hectopascal [hPa]). We could detect no causal correlation between air pollutants and MT due to eLVO.
Our data suggest a 6-month seasonal pattern in the incidence of MT due to eLVO peaking in spring and early autumn. This might be attributed to two different factors: (1) a current temperature rise (comparing the average monthly temperature in consecutive months) and (2) colder overall temperature. These results could help to identify risk periods requiring an adaptation in local infrastructure.
为了确定需要增加技术和人力资源的风险期,我们试图确定需要机械血栓切除术(MT)的栓塞性大血管闭塞(eLVO)引起的急性缺血性卒中的发病模式和关联。
我们在 9 年期间(2010-2018 年)进行了时间序列分析,以检测 MT 治疗 eLVO 发病率的季节性模式(n=2628 例患者)。在一系列连续负二项式回归模型中,我们旨在检测进一步的关联(例如,温度、大气压、空气污染)。
eLVO 引起的 MT 发病率呈 6 个月季节性模式(p=0.024),高峰在 3 月和 9 月。总体温度较低与 eLVO 引起的 MT 增加有关(平均边际效应[AME],[95%置信区间]:-0.15[-0.30-0.0001];p=0.05;每摄氏度)。当前平均月温度升高与 eLVO 引起的 MT 发病率较高相关(0.34[0.11-0.56];p=0.003)。大气压与 eLVO 引起的 MT 呈正相关(0.38[0.13-0.64];p=0.003;每百帕斯卡[hPa])。我们无法检测到空气污染物与 eLVO 引起的 MT 之间存在因果关系。
我们的数据表明,eLVO 引起的 MT 发病率呈 6 个月季节性模式,高峰在春季和初秋。这可能归因于两个不同的因素:(1)当前温度升高(比较连续几个月的平均每月温度)和(2)整体温度较低。这些结果有助于确定需要适应当地基础设施的风险期。