Neck CT angiography in acute stroke: An open window for fast detection of COVID-19 lung involvement? Applicability in telemedicine.

BACKGROUND: Chest CT has been proposed as a screening test to rule out SARS-CoV-2 lung infection in acute stroke. Our objectives are to analyze the predictive value of neck CT angiography (CTA) source images compared with conventional chest CT, the interobserver concordance and the reliability of the diagnosis using a mobile app. METHODS: A retrospective observational study that included acute stroke patients admitted to a stroke center. Two raters blinded to the clinical data evaluated and classified the pulmonary findings in chest CT and neck CTA source images according to the COVID-19 Reporting and Data System (CO-RADS). CTA findings were evaluated using a conventional workstation and the JOIN mobile app. Scores of 3-5 were grouped as appearing typical or indeterminate for COVID-19 lung involvement and 0-2 as appearing atypical or negative for pneumonia. SARS-CoV-2 infection was confirmed by polymerase chain reaction (PCR). RESULTS: A total of 242 patients were included (42 with PCR-confirmed COVID-19). In the cohort of 43 patients with both neck CTA and chest CT, the predictive value for COVID-19 was equivalent (sensitivity, 53.8%; specificity, 92.9%). The interobserver agreement in the classification into CO-RADS 3-5 or 1-2 in CTA was good (K = 0.694; standard error, 0.107). In the cohort of 242 patients with neck CTA, the intraobserver agreement between the workstation and the JOIN app was perfect (K = 1.000; standard error 0.000). CONCLUSIONS: Neck CTA enables the accurate identification of COVID-19-associated lung abnormalities in acute stroke. CO-RADS evaluations through mobile applications have a predictive value similar to the usual platforms.

The use of a smartphone application to improve stroke code workflow metrics: A pilot study in a comprehensive stroke centre.

Background: Timely coordination between stroke team members is of relevance for stroke code management. We explore the feasibility and potential utility of a smartphone application for clinical and neuroimaging data sharing for improving workflow metrics of stroke code pathways, and professionals' opinions about its use. Methods: We performed an observational pilot study including stroke code activations at La Paz University Hospital in Madrid, from June 2019 to March 2020. Patients were classified according to the activation or not of the JOIN app by the attending physician. Clinical data and time-to-procedures were retrieved from the app or from the hospital records and the Madrid regional stroke registry as appropriate and compared between both groups. An anonymous survey collected professionals' opinions about the app and its use. Results: A total of 282 stroke code activations were registered. The JOIN app was activated in 111 (39%) cases. They had a significant reduction in imaging-to-thrombolysis (31 vs 20 min, p = .026) and in door-to-thrombolysis times (51 vs 36 min, p = .004), with more patients achieving a door-to-needle time below 45 min (68.8% vs 37.8%, p = .016). About 50% of the users found the app useful for facilitating the diagnosis and decision-making; interoperability with clinical files was considered an opportunity for improvement. Conclusions: This pilot study suggests that JOIN helps improve and document workflow metrics in acute stroke management in a comprehensive stroke centre. These results support testing JOIN in a prospective randomised study to confirm its usefulness and the general applicability of the results.