T Cell-mediated Immune Response and Correlates of Inflammation and their relationship with COVID-19 clinical severity: not an intuitive guess. (preprint)

Background: Predictors of the outcome of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) infection remain to be fully determined. We evaluated selected viral characteristics and immunological responses that might predict and/or correlate to the clinical outcome of COVID-19. Methods: The magnitude and breadth of T cell-mediated responses were measured within 36 hours of symptom onset for individuals developing divergent clinical outcomes. Peripheral Blood Mononuclear Cells (PBMCs) were subjected to in vitro stimulation with SARS-CoV-2-based peptides. In addition, SARS-CoV-2 sequences were generated by metagenome, and HLA typing was performed using Luminex technology. Findings: CD4+ T cell activation was found to be negatively correlated with SARS-CoV-2 basal viral load in patients with severe COVID-19 (p = 0·043). The overall cellular immune response, as inferred by IFN-γ signal, was higher at baseline for patients that progressed to mild disease compared to patients that progressed to severe disease (p = 0·0044). Subjects with milder disease developed higher T cell responses for MHC class I and II-restricted peptides (p = 0·033). Interpretation: Mounting specific cellular immune responses in the first days after symptom onset, as inferred by IFN-γ magnitude in the ELISPOT assay, may efficiently favor a positive outcome. In contrast, progression to severe COVID-19 was accompanied by stronger cellular immune responses, higher CD4+ T cell activation, and a higher number of in silico predicted high-affinity class I HLA alleles. Funding: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) - Grant 2020/10396-2, and Conselho Nacional de Desenvolvimento Científico e Tecnológico - Grant 441817/2018-1.

Long-Term Survival of Mechanically Ventilated Patients with Severe COVID-19: An Observational Cohort Study (preprint)

Background: Information is lacking regarding long-term survival and predictive factors for mortality in patients with acute hypoxemic respiratory failure due to coronavirus disease 2019 (COVID-19) and undergoing invasive mechanical ventilation. We aimed to estimate 90-day and 180-day survival of patients with COVID-19 requiring invasive ventilation and to develop a predictive model for intensive care unit mortality.Methods: Retrospective, multicentre, national cohort study between March 8 and April 30, 2020 in 16 intensive care units (ICU) in Spain. Participants were consecutive adults who received invasive mechanical ventilation for COVID–19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection detected in positive testing of a nasopharyngeal sample and confirmed by real time reverse-transcriptase polymerase chain reaction (rt-PCR). The primary outcomes were 90-day and 180-day survival after hospital admission. Secondary outcomes were length of ICU and hospital stay, and ICU and in-hospital mortality. A predictive model and a nomogram were developed to estimate the probability of ICU mortality. Results: 868 patients were included (median age, 64 years [interquartile range [IQR], 56-71 years]; 72% male). Severity at ICU admission, estimated by SAPS3, was 56 points [IQR 50-63]. Prior to intubation, 26% received some type of noninvasive respiratory support. The 90-day and 180-day survival rates were 69% (95% confidence interval [CI] 66%-72%) and 59% (95% CI 56%-62%) respectively. The predictive factors associated with ICU mortality were: age (odds ratio [OR] 1.049 [95% CI 1.032-1.066] per 1-year increase), SAPS3 (OR 1.025 [95% CI 1.008-1.041] per 1-point increase), neutrophil to lymphocyte ratio (OR 1.009 [95% CI 1.002-1.016]), a failed attempt of noninvasive positive pressure ventilation previous to orotracheal intubation(OR 2.131 [95% CI 1.279-3.550]), and use of selective digestive decontamination (OR 0.587 [95% CI 0.358-0.963]).Conclusion: The long-term survival of mechanically ventilated patients with severe COVID-19 reaches more than 50% and may help to provide individualized risk stratification and potential treatments.Trial registration: ClinicalTrials.gov Identifier: NCT04379258. Registered 10 April 2020 (retrospectively registered).

Comparison of Growth Patterns of COVID-19 Cases through the ARIMA and Gompertz Models. Case Studies: Austria, Switzerland, and Israel.

On May 19, 2020, data confirmed that coronavirus 2019 disease (COVID-19) had spread worldwide, with more than 4.7 million infected people and more than 316,000 deaths. In this article, we carry out a comparison of the methods to calculate and forecast the growth of the pandemic using two statistical models: the autoregressive integrated moving average (ARIMA) and the Gompertz function growth model. The countries that have been chosen to verify the usefulness of these models are Austria, Switzerland, and Israel, which have a similar number of habitants. The investigation to check the accuracy of the models was carried out using data on confirmed, non-asymptomatic cases and confirmed deaths from the period February 21-May 19, 2020. We use the root mean squared error (RMSE), the mean absolute percentage error (MAPE), and the regression coefficient index R2 to check the accuracy of the models. The experimental results provide promising adjustment errors for both models (R2>0.99), with the ARIMA model being the best for infections and the Gompertz best for mortality. It has also been verified that countries are affected differently, which may be due to external factors that are difficult to measure quantitatively. These models provide a fast and effective system to check the growth of pandemics that can be useful for health systems and politicians so that appropriate measures are taken and countries' health care systems do not collapse.