DID THE STROKE CODE IN MADRID JAM DURING THE FIRST WAVE OF COVID-19?

Background and aims: Madrid was one of the epicentres of the COVID-19 pandemic in Spain. The entire healthcare system was severely affected by the first wave of the pandemic. We aimed to assess the extent to which the acute stroke care chain was impacted. Methods: Using the stroke code (SC) cohort of SUMMA 112 (the main emergency medical service in the region), we compared all patients in the first wave of the pandemic and in the same period of the previous year. Subsequently, we collected all anonymized records from the main hospital administrative database (minimum basic data set at hospital discharge). We used ambulance response times, concordance between pre-hospital and hospital diagnosis, hospital times, and mortality to evaluate the SC protocol. The study was approved by the Ethics Committee of the Community of Madrid. Results: 966 SC were analysed (514 pre-pandemic and 452 during the first wave). Pre-hospital attention times were longer (39 vs. 35 minutes), patients stayed longer in the emergency room before admission (7.5 vs. 6.1 hours), the concordance between pre-hospital and in-hospital diagnostic suspicion did not change significantly (86% vs. 89%) and mortality decreased (9% vs 13%) during the first wave of the pandemic Conclusions: During the first wave of the pandemic, there were delays in care, especially in the on-scene time. Improvements in training might have prevented it. The high qualification of pre-hospital teams enabled them to maintain their diagnostic accuracy. The reduction in mortality needs further exploration.

Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram.

OBJECTIVE: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). METHODS: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site. RESULTS: The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%. CONCLUSION: Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.

Effects of cytokine blocking agents on hospital mortality in patients admitted to ICU with acute respiratory distress syndrome by SARS-CoV-2 infection: Retrospective cohort study

Background: The use of cytokine-blocking agents has been proposed to modulate the inflammatory response in patients with COVID-19. Tocilizumab and anakinra were included in the local protocol as an optional treatment in critically ill patients with acute respiratory distress syndrome (ARDS) by SARS-CoV-2 infection. This cohort study evaluated the effects of therapy with cytokine blocking agents on in-hospital mortality in COVID-19 patients requiring mechanical ventilation and admitted to intensive care unit. Methods: The association between therapy with tocilizumab or anakinra and in-hospital mortality was assessed in consecutive adult COVID-19 patients admitted to our ICU with moderate to severe ARDS. The association was evaluated by comparing patients who received to those who did not receive tocilizumab or anakinra and by using different multivariable Cox models adjusted for variables related to poor outcome, for the propensity to be treated with tocilizumab or anakinra and after patient matching. Results: Sixty-six patients who received immunotherapy (49 tocilizumab, 17 anakinra) and 28 patients who did not receive immunotherapy were included. The in-hospital crude mortality was 30,3% in treated patients and 50% in non-treated (OR 0.77, 95% CI 0.56-1.05, p=0.069). The adjusted Cox model showed an association between therapy with immunotherapy and in-hospital mortality (HR 0.40, 95% CI 0.19-0.83, p=0.015). This protective effect was further confirmed in the analysis adjusted for propensity score, in the propensity-matched cohort and in the cohort of patients with invasive mechanical ventilation within 2 hours after ICU admission. Conclusions: Although important limitations, our study showed that cytokine-blocking agents seem to be safe and to improve survival in COVID-19 patients admitted to ICU with ARDS and the need for mechanical ventilation.