Abstract
Objectives: Our objective was to identify characteristics associated with having an acute ischemic stroke (AIS) among hospitalized COVID-19 patients and the subset of these patients with a neurologic symptom.Materials and Methods: Our derivation cohort consisted of COVID-19 patients admitted to Yale-New Haven Health between January 3, 2020 and August 28, 2020 with and without AIS. We also studied a sub-cohort of hospitalized COVID-19 patients demonstrating a neurologic symptom with and without an AIS. Demographic, clinical, and laboratory results were compared between AIS and non-AIS patients in the full COVID-19 cohort and in the sub-cohort of COVID-19 patients with a neurologic symptom. Multivariable logistic regression models were built to predict ischemic stroke risk in these two COVID-19 cohorts. These 2 models were externally validated in COVID-19 patients hospitalized at a major health system in New York. We then compared the distribution of the resulting predictors in a non-COVID ischemic stroke control cohort.Results: A total of 1,827 patients were included in the derivation cohort (AIS N = 44; no AIS N = 1,783). Among all hospitalized COVID-19 patients, history of prior stroke and platelet count ≥ 200 × 1,000/μL at hospital presentation were independent predictors of AIS (derivation AUC 0.89, validation AUC 0.82), irrespective of COVID-19 severity. Among hospitalized COVID-19 patients with a neurologic symptom (N = 827), the risk of AIS was significantly higher among patients with a history of prior stroke and age <60 (derivation AUC 0.83, validation AUC 0.81). Notably, in a non-COVID ischemic stroke control cohort (N = 168), AIS patients were significantly older and less likely to have had a prior stroke, demonstrating the uniqueness of AIS patients with COVID-19.Conclusions: Hospitalized COVID-19 patients who demonstrate a neurologic symptom and have either a history of prior stroke or are of younger age are at higher risk of ischemic stroke.
Highlights
As of March 2021, the global pandemic caused by coronavirus disease 2019 (COVID-19) has infected more than 120 million individuals worldwide and has resulted in more than 2.6 million deaths [1]
We conducted a retrospective study of patients with COVID19 admitted to the Yale-New Haven Health System (YNHHS) System which consists of hospitals located in the State of Connecticut, USA, including YNHH-York Street Campus (New Haven), YNHH-St Raphael’s Campus (New Haven), Greenwich Hospital (Greenwich), Lawrence and Memorial Hospital (New London), and Bridgeport Hospital (Bridgeport)
To identify acute ischemic stroke (AIS) patients among those hospitalized with COVID-19, we first applied any of the following screening criteria to identify patients with a potential neurologic symptom: [1] a hospital ICD-10 diagnosis of ischemic stroke, intracerebral hemorrhage, subarachnoid hemorrhage, or transient ischemic attack; [2] a CT, CT angiogram, or MRI of the head performed during hospitalization; [3] a neurology consultation ordered during hospitalization, or [4] a nursing National Institutes of Health Stroke Scale assessment documented during hospitalization
Summary
As of March 2021, the global pandemic caused by coronavirus disease 2019 (COVID-19) has infected more than 120 million individuals worldwide and has resulted in more than 2.6 million deaths [1]. Among patients with COVID-19 in a cohort in Wuhan, China, those with a history of ischemic stroke had more co-morbidities, lower platelet and leukocyte counts, and higher D-dimer, pro-brain natriuretic peptide, and interleukin-6 levels compared to their counterparts without a prior history of stroke [6]. Data regarding predictors of ischemic stroke among all COVID patients and those with neurologic symptoms are very limited. Strokes in the context of COVID-19 have been previously associated with increased mortality and morbidity [8,9,10]. Given these observations, there is a need to better predict, diagnose, and prevent ischemic strokes in patients with COVID-19
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