Cross-immunity against SARS-COV-2 variants of concern in naturally infected critically ill COVID-19 patients.

Critically ill patients infected with SARS-CoV-2 display adaptive immunity, but it is unknown if they develop cross-reactivity to variants of concern (VOCs). We profiled cross-immunity against SARS-CoV-2 VOCs in naturally infected, non-vaccinated, critically ill COVID-19 patients. Wave-1 patients (wild-type infection) were similar in demographics to Wave-3 patients (wild-type/alpha infection), but Wave-3 patients had higher illness severity. Wave-1 patients developed increasing neutralizing antibodies to all variants, as did patients during Wave-3. Wave-3 patients, when compared to Wave-1, developed more robust antibody responses, particularly for wild-type, alpha, beta and delta variants. Within Wave-3, neutralizing antibodies were significantly less to beta and gamma VOCs, as compared to wild-type, alpha and delta. Patients previously diagnosed with cancer or chronic obstructive pulmonary disease had significantly fewer neutralizing antibodies. Naturally infected ICU patients developed adaptive responses to all VOCs, with greater responses in those patients more likely to be infected with the alpha variant, versus wild-type.

Association between thrombocytopenia and the severity of Covid-19 infection among hospitalized Egyptian patients.

Background: COVID-19, which is caused by the corona virus 2 that causes severe acute respiratory syndrome, causes a respiratory and systemic illness that in 10-15% of patients escalates to a severe form of pneumonia. Thrombocytopenia is frequent in patients with COVID-19. We aimed to evaluate the association between thrombocytopenia and the severity of COVID-19 infection in hospitalized patients. Methods: A cross-sectional study was done on 800 Egyptian patients with confirmed covid-19 infection. They were divided into Group I (Mild): 200 symptomatic patients meeting the case definition for COVID-19 without radiological evidence of pneumonia or hypoxia. Group II (Moderate): 200 patients with clinical signs of non-severe pneumonia and radiological evidence of pneumonia. Group III (Severe): 200 patients with clinical signs of pneumonia plus: respiratory or lung dysfunction. Group IV: 200 critically ill patient in ICU: Acute respiratory distress syndrome (ARDS).Results: there was a highly statistically significant difference between the studied groups regarding thrombocytopenia (p < 0.001). Thrombocytopenia was statistically higher in severe and critically ill patients. In addition, a statistically significant difference found in outcome among the studied groups (p < 0.05) {critically ill (40%), severe (17.5%)}. The most common cause of death was respiratory failure, which occurred in 28 severe patients (80%) and 65 critically ill patients (81.25%), followed by hemorrhage due to thrombocytopenia, which occurred in 7 severe patients (20%) and 15 critically ill patients, respectively (18.75%). Conclusion: The Platelet count is a straightforward, inexpensive, as well as easily available laboratory parameter that is frequently linked to severe covid-19 infection and a significant death risk.

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients.

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients' Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs. 0.69, P < 0.01 [paired t-test with 95% confidence interval]). Conclusions: The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems (CDSSs) aimed at optimizing available resources.

Viral load and disease severity in COVID-19.

The relationship between COVID-19 severity and viral load is unknown. Our objective was to assess the association between viral load and disease severity in COVID-19. In this single center observational study of adults with laboratory confirmed SARS-CoV-2, the first positive in-hospital nasopharyngeal swab was used to calculate the log10 copies/ml [log10 copy number (CN)] of SARS-CoV-2. Four categories based on level of care and modified sequential organ failure assessment score (mSOFA) at time of swab were determined. Median log10CN was compared between different levels of care and mSOFA quartiles. Median log10CN was compared in patients who did and did not receive influenza vaccine, and the correlation between log10CN and D-dimer was examined. We found that of 396 patients, 54.3% were male, and 25% had no major comorbidity. Hospital mortality was 15.7%. Median mSOFA was 2 (IQR 0-3). Median log10CN was 5.5 (IQR 3.3-8.0). Median log10CN was highest in non-intubated ICU patients [6.4 (IQR 4.4-8.1)] and lowest in intubated ICU patients [3.6 (IQR 2.6-6.9)] (p value < 0.01). In adjusted analyses, this difference remained significant [mean difference 1.16 (95% CI 0.18-2.14)]. There was no significant difference in log10CN between other groups in the remaining pairwise comparisons. There was no association between median log10CN and mSOFA in either unadjusted or adjusted analyses or between median log10CN in patients with and without influenza immunization. There was no correlation between log10CN and D-dimer. We conclude, in our cohort, we did not find a clear association between viral load and disease severity in COVID-19 patients. Though viral load was higher in non-intubated ICU patients than in intubated ICU patients there were no other significant differences in viral load by disease severity.