The impact of COVID-19 on acute ischemic stroke admissions: Analysis from a community-based tertiary care center.

ABSTRACT

BACKGROUND/OBJECTIVE: To evaluate the impact of the COVID-19 pandemic on hospital admissions and outcomes in patients admitted with acute ischemic stroke. METHODS: Single-center retrospective analysis of patients admitted to the hospital with acute ischemic stroke, between December 1st, 2019 and June 30th, 2020. Outcomes were classified as none-to-minimal disability, moderate-to-severe disability, and death based on discharge disposition, and compared between two time periods pre-COVID-19 era (December 1st, 2019 to March 11th, 2020) and COVID-19 era (March 12th to June 30th, 2020). We also performed a comparative trend analysis for the equivalent period between 2019 and 2020. RESULTS: Five hundred and seventy-five patients with a mean age (years±SD) of 68±16 were admitted from December 1st, 2019 to June 30th, 2020, with a clinical diagnosis of acute ischemic stroke. Of these, 255 (44.3%) patients were admitted during the COVID-19 era. We observed a 22.1% and 39.5% decline in admission for acute ischemic stroke in April and May 2020, respectively. A significantly higher percentage of patients with acute ischemic stroke received intravenous thrombolysis during the COVID-19 era (p = 0.020). In patients with confirmed COVID-19, we found a higher percentage of older men with preexisting comorbidities such as hyperlipidemia, coronary artery disease, and diabetes mellitus but a lower rate of atrial fibrillation. In addition, we found a treatment delay in both intravenous thrombolysis (median 94.5 min versus 38 min) and mechanical thrombectomy (median 244 min versus 86 min) in patients with confirmed COVID-19 infection. There were no differences in patients' disposition including home, short-term, and long-term facility (p = 0.60). CONCLUSIONS: We observed a reduction of hospital admissions in acute ischemic strokes and some delay in reperfusion therapy during the COVID-19 pandemic. Prospective studies and a larger dataset analysis are warranted.