Outcomes of COVID-19 Patients Admitted to the Intermediate Respiratory Care Unit: Non-Invasive Respiratory Therapy in a Sequential Protocol.

The intermediate respiratory care units (IRCUs) have a pivotal role managing escalation and de-escalation between the general wards and the intensive care units (ICUs). Since the COVID-19 pandemic began, the early detection of patients that could improve on non-invasive respiratory therapies (NRTs) in IRCUs without invasive approaches is crucial to ensure proper medical management and optimize limiting ICU resources. The aim of this study was to assess factors associated with survival, ICU admission and intubation likelihood in COVID-19 patients admitted to IRCUs. Observational retrospective study in consecutive patients admitted to the IRCU of a tertiary hospital from March 2020 to April 2021. Inclusion criteria: hypoxemic respiratory failure (SpO2 ≤ 94% and/or respiratory rate ≥ 25 rpm with FiO2 > 50% supplementary oxygen) due to acute COVID-19 infection. Demographic, comorbidities, clinical and analytical data, and medical and NRT data were collected at IRCU admission. Multivariate logistic regression models assessed factors associated with survival, ICU admission, and intubation. From 679 patients, 79 patients (12%) had an order to not do intubation. From the remaining 600 (88%), 81% survived, 41% needed ICU admission and 37% required intubation. In the IRCU, 51% required non-invasive ventilation (NIV group) and 49% did not (non-NIV group). Older age and lack of corticosteroid treatment were associated with higher mortality and intubation risk in the scheme, which could be more beneficial in severe forms. Initial NIV does not always mean worse outcomes.

Pre-existing Autoantibodies Neutralizing High Concentrations of Type I Interferons in Almost 10% of COVID-19 Patients Admitted to Intensive Care in Barcelona.

BACKGROUND: It is important to predict which patients infected by SARS-CoV-2 are at higher risk of life-threatening COVID-19. Several studies suggest that neutralizing auto-antibodies (auto-Abs) against type I interferons (IFNs) are predictive of critical COVID-19 pneumonia. OBJECTIVES: We aimed to test for auto-Abs to type I IFN and describe the main characteristics of COVID-19 patients admitted to intensive care depending on whether or not these auto-Abs are present. METHODS: Retrospective analysis of all COVID-19 patients admitted to an intensive care unit (ICU) in whom samples were available, from March 2020 to March 2021, in Barcelona, Spain. RESULTS: A total of 275 (70.5%) out of 390 patients admitted to ICU were tested for type I IFNs auto-antibodies (α2 and/or ω) by ELISA, being positive in 49 (17.8%) of them. Blocking activity of plasma diluted 1/10 for high concentrations (10 ng/mL) of IFNs was proven in 26 (9.5%) patients. Almost all the patients with neutralizing auto-Abs were men (92.3%). ICU patients with positive results for neutralizing IFNs auto-Abs did not show relevant differences in demographic, comorbidities, clinical features, and mortality, when compared with those with negative results. Nevertheless, some laboratory tests (leukocytosis, neutrophilia, thrombocytosis) related with COVID-19 severity, as well as acute kidney injury (17 [65.4%] vs. 100 [40.2%]; p = 0.013) were significantly higher in patients with auto-Abs. CONCLUSION: Auto-Abs neutralizing high concentrations of type I IFNs were found in 9.5% of patients admitted to the ICU for COVID-19 pneumonia in a hospital in Barcelona. These auto-Abs should be tested early upon diagnosis of SARS-CoV-2 infection, as they account for a significant proportion of life-threatening cases.

Individual outcome prediction models for patients with COVID-19 based on their first day of admission to the intensive care unit.

BACKGROUND: Currently, good prognosis and management of critically ill patients with COVID-19 are crucial for developing disease management guidelines and providing a viable healthcare system. We aimed to propose individual outcome prediction models based on binary logistic regression (BLR) and artificial neural network (ANN) analyses of data collected in the first 24 h of intensive care unit (ICU) admission for patients with COVID-19 infection. We also analysed different variables for ICU patients who survived and those who died. METHODS: Data from 326 critically ill patients with COVID-19 were collected. Data were captured on laboratory variables, demographics, comorbidities, symptoms and hospital stay related information. These data were compared with patient outcomes (survivor and non-survivor patients). BLR was assessed using the Wald Forward Stepwise method, and the ANN model was constructed using multilayer perceptron architecture. RESULTS: The area under the receiver operating characteristic curve of the ANN model was significantly larger than the BLR model (0.917 vs 0.810; p < 0.001) for predicting individual outcomes. In addition, ANN model presented similar negative predictive value than the BLR model (95.9% vs 94.8%). Variables such as age, pH, potassium ion, partial pressure of oxygen, and chloride were present in both models and they were significant predictors of death in COVID-19 patients. CONCLUSIONS: Our study could provide helpful information for other hospitals to develop their own individual outcome prediction models based, mainly, on laboratory variables. Furthermore, it offers valuable information on which variables could predict a fatal outcome for ICU patients with COVID-19.

Genetic Screening for TLR7 Variants in Young and Previously Healthy Men With Severe COVID-19.

Introduction: Loss-of-function TLR7 variants have been recently reported in a small number of males to underlie strong predisposition to severe COVID-19. We aimed to determine the presence of these rare variants in young men with severe COVID-19. Methods: We prospectively studied males between 18 and 50 years-old without predisposing comorbidities that required at least high-flow nasal oxygen to treat COVID-19. The coding region of TLR7 was sequenced to assess the presence of potentially deleterious variants. Results: TLR7 missense variants were identified in two out of 14 patients (14.3%). Overall, the median age was 38 (IQR 30-45) years. Both variants were not previously reported in population control databases and were predicted to be damaging by in silico predictors. In a 30-year-old patient a maternally inherited variant [c.644A>G; p.(Asn215Ser)] was identified, co-segregating in his 27-year-old brother who also contracted severe COVID-19. A second variant [c.2797T>C; p.(Trp933Arg)] was found in a 28-year-old patient, co-segregating in his 24-year-old brother who developed mild COVID-19. Functional testing of this variant revealed decreased type I and II interferon responses in peripheral mononuclear blood cells upon stimulation with the TLR7 agonist imiquimod, confirming a loss-of-function effect. Conclusions: This study supports a rationale for the genetic screening for TLR7 variants in young men with severe COVID-19 in the absence of other relevant risk factors. A diagnosis of TLR7 deficiency could not only inform on treatment options for the patient, but also enables pre-symptomatic testing of at-risk male relatives with the possibility of instituting early preventive and therapeutic interventions.