Predicting the effectiveness of high-flow oxygen therapy in COVID-19 patients: a single-centre observational study.

BACKGROUND: High-flow nasal cannula (HFNC) therapy is a helpful tool in the treatment of hypoxaemic respiratory failure. However, the clinical parameters predicting the effectiveness of HFNC in coronavirus-19 disease (COVID-19) patients remain unclear. METHODS: Sixteen COVID-19 patients undergoing HFNC in the Asklepios Lung Clinic Munich-Gauting, Germany between 16 March and 3 June 2020 were retrospectively included into the study. Seven patients successfully recovered after HFNC (Group 1), while 9 patients required intubation upon HFNC failure (Group 2). Relevant predictors for an effective HFNC therapy were analysed on day 0 and 4 after HFNC initiation via receiver operating characteristics. RESULTS: The groups did not differ significantly in terms of age, sex, body mass index, and comorbidities. Five patients died in Group 2 upon disease progression and HFNC failure. Group 1 required a lower oxygen supplementation (FiO2 0.46 [0.31-0.54] vs. 0.72 [0.54-0.76], P = 0.022) and displayed a higher PaO2/FiO2 ratio (115 [111-201] vs. 93.3 [67.2-145], P = 0.042) on day 0. In Group 2, fever persisted on day 4 (38.5 [38.0-39.4]°C vs. 36.5 [31.1-37.1]°C, P = 0.010). Serum C-reactive protein (CRP) levels > 108 mg L-1 (day 0) and persistent oxygen saturation < 89% and PaO2/FiO2 ratio < 91 (day 4) were identified as significant predictors for HFNC failure (area under curve 0.929, 0.933, and 0.893). CONCLUSIONS: Elevated oxygen saturation, decreased FiO2 and reduced serum CRP on day 4 significantly predict HFNC effectiveness in COVID-19 patients. Based on these parameters, larger prospective studies are necessary to further investigate the effectiveness of HFNC in the treatment of COVID-19-associated hypoxaemic respiratory failure.

Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through 'reverse phenotyping'.

The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for 'reverse phenotyping'. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.

Automated quantitative thin slice volumetric low dose CT analysis predicts disease severity in COVID-19 patients.

PURPOSE: This study aimed to identify predictive (bio-)markers for COVID-19 severity derived from automated quantitative thin slice low dose volumetric CT analysis, clinical chemistry and lung function testing. METHODS: Seventy-four COVID-19 patients admitted between March 16th and June 3rd 2020 to the Asklepios Lung Clinic Munich-Gauting, Germany, were included in the study. Patients were categorized in a non-severe group including patients hospitalized on general wards only and in a severe group including patients requiring intensive care treatment. Fully automated quantification of CT scans was performed via IMBIO CT Lung Texture analysis™ software. Predictive biomarkers were assessed with receiver-operator-curve and likelihood analysis. RESULTS: Fifty-five patients (44% female) presented with non-severe COVID-19 and 19 patients (32% female) with severe disease. Five fatalities were reported in the severe group. Accurate automated CT analysis was possible with 61 CTs (82%). Disease severity was linked to lower residual normal lung (72.5% vs 87%, p = 0.003), increased ground glass opacities (GGO) (8% vs 5%, p = 0.031) and increased reticular pattern (8% vs 2%, p = 0.025). Disease severity was associated with advanced age (76 vs 59 years, p = 0.001) and elevated serum C-reactive protein (CRP, 92.2 vs 36.3 mg/L, p < 0.001), lactate dehydrogenase (LDH, 485 vs 268 IU/L, p < 0.001) and oxygen supplementation (p < 0.001) upon admission. Predictive risk factors for the development of severe COVID-19 were oxygen supplementation, LDH >313 IU/L, CRP >71 mg/L, <70% normal lung texture, >12.5% GGO and >4.5% reticular pattern. CONCLUSION: Automated low dose CT analysis upon admission might be a useful tool to predict COVID-19 severity in patients.