Impact of SARS-CoV-2 Pandemic on "Stroke Code" Imaging Utilization and Yield.

BACKGROUND AND PURPOSE: Indirect consequences of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) pandemic include those related to failure of patients to seek or receive timely medical attention for seemingly unrelated disease. We report our experience with stroke code imaging during the early pandemic months of 2020. MATERIALS AND METHODS: Retrospective review of stroke codes during the 2020 pandemic and both 2020 and matched 2019 prepandemic months was performed. Patient variables were age, sex, hospital location, and severity of symptoms based on the NIHSS. We reviewed the results of CT of the head, CTA, CTP, and MR imaging examinations and classified a case as imaging-positive if any of the imaging studies yielded a result that related to the clinical indication for the study. Both year-to-year and sequential comparisons were performed between pandemic and prepandemic months. RESULTS: A statistically significant decrease was observed in monthly stroke code volumes accompanied by a statistically significant increased proportion of positive imaging findings during the pandemic compared with the same months in the prior year (P < .001) and prepandemic months in the same year (P < .001). We also observed statistically significant increases in average NIHSS scores (P = .045 and P = .03) and the proportion of inpatient stroke codes (P = .003 and P = .03). CONCLUSIONS: During our pandemic period, there was a significantly decreased number of stroke codes but simultaneous increases in positivity rates, symptom severity, and inpatient codes. We postulate that this finding reflects the documented reluctance of patients to seek medical care during the pandemic, with the shift toward a greater proportion of inpatient stroke codes potentially reflecting the neurologic complications of the virus itself.

COVID-19 Severity and Stroke: Correlation of Imaging and Laboratory Markers.

BACKGROUND AND PURPOSE: Coronavirus disease 2019 (COVID-19) appears to be an independent risk factor for stroke. We hypothesize that patients who develop stroke while hospitalized for severe COVID-19 will have higher inflammatory markers and distinct stroke imaging patterns compared with patients positive for COVID-19 with out-of-hospital stroke onset and milder or no COVID-19 symptoms. MATERIALS AND METHODS: This is a retrospective case series of patients positive for COVID-19 on polymerase chain reaction testing with imaging-confirmed stroke treated within a large health care network in New York City and Long Island between March 14 and April 26, 2020. Clinical and laboratory data collected retrospectively included complete blood counts and creatinine, alanine aminotransferase, lactate dehydrogenase, C-reactive protein, ferritin, and D-dimer levels. All CT and MR imaging studies were independently reviewed by 2 neuroradiologists who recorded stroke subtype and patterns of infarction and intracranial hemorrhage. RESULTS: Compared with patients with COVID-19 with outside-of-hospital stroke onset and milder or no COVID-19 symptoms (n = 45, 52.3%), patients with stroke already hospitalized for severe COVID-19 (n = 41, 47.7%) had significantly more frequent infarctions (95.1% versus 73.3%, P = .006), with multivascular distributions (56.4% versus 33.3%, P = .022) and associated hemorrhage (31.7% versus 4.4%, P = .001). Patients with stroke admitted with more severe COVID-19 had significantly higher C-reactive protein and ferritin levels, elevated D-dimer levels, and more frequent lymphopenia and renal and hepatic injury (all, P < .003). CONCLUSIONS: Patients with stroke hospitalized with severe COVID-19 are characterized by higher inflammatory, coagulopathy, and tissue-damage biomarkers, supporting proposed pathogenic mechanisms of hyperinflammation activating a prothrombotic state. Cautious balancing of thrombosis and the risk of hemorrhagic transformation is warranted when considering anticoagulation.

Automated face analysis by feature point tracking has high concurrent validity with manual FACS coding.

The face is a rich source of information about human behavior. Available methods for coding facial displays, however, are human-observer dependent, labor intensive, and difficult to standardize. To enable rigorous and efficient quantitative measurement of facial displays, we have developed an automated method of facial display analysis. In this report, we compare the results with this automated system with those of manual FACS (Facial Action Coding System, Ekman & Friesen, 1978a) coding. One hundred university students were videotaped while performing a series of facial displays. The image sequences were coded from videotape by certified FACS coders. Fifteen action units and action unit combinations that occurred a minimum of 25 times were selected for automated analysis. Facial features were automatically tracked in digitized image sequences using a hierarchical algorithm for estimating optical flow. The measurements were normalized for variation in position, orientation, and scale. The image sequences were randomly divided into a training set and a cross-validation set, and discriminant function analyses were conducted on the feature point measurements. In the training set, average agreement with manual FACS coding was 92% or higher for action units in the brow, eye, and mouth regions. In the cross-validation set, average agreement was 91%, 88%, and 81% for action units in the brow, eye, and mouth regions, respectively. Automated face analysis by feature point tracking demonstrated high concurrent validity with manual FACS coding.