Seasonal patterns and associations in the incidence of acute ischemic stroke requiring mechanical thrombectomy.

Bücke, Philipp; Henkes, Hans; Arnold, Guy; Herting, Birgit; Jüttler, Eric; Klötzsch, Christof; Lindner, Alfred; Mauz, Uwe; Niehaus, Ludwig; Reinhard, Matthias; Waibel, Stefan; Horvath, Thomas; Bäzner, Hansjörg; Aguilar Pérez, Marta (2021). Seasonal patterns and associations in the incidence of acute ischemic stroke requiring mechanical thrombectomy. European journal of neurology, 28(7), pp. 2229-2237. Wiley 10.1111/ene.14832

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BACKGROUND

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).

METHODS

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).

RESULTS

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.

CONCLUSIONS

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.

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