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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.
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Background: Inpatient stroke-codes (ISC) have traditionally seen low treatment rates with IV-thrombolytic (IVT). The purpose of this study was to identify the predictors of true stroke, prevalent IVT-treatment gap and study the factors associated with such missed treatment opportunities (MTO). Methods: A retrospective chart review identified ISC from March 2017 to March 2018. Clinical, radiographic and demographic data were collected. Primary analysis was performed between stroke vs. non-stroke diagnoses. Dichotomous variables were analyzed using Chi-Square test of proportions and continuous variables with Wilcoxon-Ranked-Sum test. Significant factors were then tested in a multivariate logistic regression model for independence. Results: From 211 ISC, 36% (n = 76) had an acute stroke. Hemorrhagic stroke (HS) was present in 5.7% (n = 12). Of the remaining 199, 44% (n = 87) were IVT-eligible but only 3.4% (n = 3) were treated. Of the remaining 84 IVT-eligible-but-untreated patients, 69(82.1%) were mimics, while 15 (17.9%) had an ischemic stroke (IS), constituting a MTO of 1 in 6 IVT-eligible patients, with National Institutes of Health Stroke Scale (NIHSS) ≤4 being the commonest deterrent. Independent predictors of stroke were ejection fraction (EF) <30% (p = 0.030, OR = 3.06), post-operative status (p = 0.001, OR = 3.71), visual field-cut (p = 0.008, OR = 3.70), and facial droop (p = 0.010, OR = 2.59). Conclusion: In our study, one in three ISC were true strokes. IVT treatment rates were low with a MTO of 1 in 6 IVT-eligible patients. The most common reason for not treating was NIHSS ≤4. Knowing predictors of true stroke and the common barriers to IVT treatment can help narrow this treatment gap.
