Utility of dual-energy computed tomography in the association of COVID-19 pneumonia severity.

Aim: Coronavirus disease 2019 pneumonia differs from ordinary pneumonia in that it is associated with lesions that reduce pulmonary perfusion. Dual-energy computed tomography is well suited to elucidate the etiology of coronavirus disease 2019 pneumonia, because it highlights changes in organ blood flow. In this study, we investigated whether dual-energy computed tomography could be used to determine the severity of coronavirus disease 2019 pneumonia. Methods: Patients who were diagnosed with coronavirus disease 2019 pneumonia, admitted to our hospital, and underwent dual-energy computed tomography were included in this study. Dual-energy computed tomography findings, plane computed tomography findings, disease severity, laboratory data, and clinical features were compared between two groups: a critical group (18 patients) and a non-critical group (30 patients). Results: The dual-energy computed tomography results indicated that the percentage of flow loss was significantly higher in the critical group compared with the non-critical group (P < 0.001). Additionally, our data demonstrated that thrombotic risk was associated with differences in clinical characteristics (P = 0.018). Receiver operating characteristic analysis revealed that the percentage of flow loss, evaluated using dual-energy computed tomography, could predict severity in the critical group with 100% sensitivity and 77% specificity. However, there were no significant differences in the receiver operating characteristic values for dual-energy computed tomography and plane computed tomography. Conclusion: Dual-energy computed tomography can be used to associate the severity of coronavirus disease 2019 pneumonia with high accuracy. Further studies are needed to draw definitive conclusions.

Temporal change in Syndecan-1 as a therapeutic target and a biomarker for the severity classification of COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) pneumonitis associated with severe respiratory failure is associated with high mortality. The pathogenesis of COVID-19 is associated with microembolism or microvascular endothelial injuries. Here, we report that syndecan-1 (SDC-1), a component of the endothelial glycocalyx, may be a biomarker of severity classification for COVID-19 related to endothelial injury. METHODS AND ANALYSIS: We analyzed the data of COVID-19 patients for 1 year from February 2020 at Yokohama City University Hospital and Yokohama City University Medical Center Hospital. We selected COVID-19 patients who required admission care, including intensive care, and analyzed the classification of severe and critical COVID-19 retrospectively, using various clinical data and laboratory data with SDC-1 by ELISA. RESULTS: We analyzed clinical and laboratory data with SDC-1 in five severe COVID-19 and ten critical COVID-19 patients. In the two groups, their backgrounds were almost the same. In laboratory data, the LDH, CHE, and CRP levels showed significant differences in each group (P = 0.032, P < 0.0001, and P = 0.007, respectively) with no significant differences in coagulation-related factors (platelet, PT-INR, d-dimer, ISTH score; P = 0.200, 0.277, 0.655, and 0.36, respectively). For the clinical data, the SOFA score was significantly different from admission day to day 14 of admission (p < 0.0001). The SDC-1 levels of critical COVID-19 patients were significantly higher on admission day and all-time course compared with the levels of severe COVID-19 patients (P = 0.009 and P < 0.0001, respectively). CONCLUSIONS: Temporal change of SDC-1 levels closely reflect the severity of COVID-19, therefore, SDC-1 may be a therapeutic target and a biomarker for the severity classification of Covid-19.

Identification of serum prognostic biomarkers of severe COVID-19 using a quantitative proteomic approach.

The COVID-19 pandemic is an unprecedented threat to humanity that has provoked global health concerns. Since the etiopathogenesis of this illness is not fully characterized, the prognostic factors enabling treatment decisions have not been well documented. Accurately predicting the progression of the disease would aid in appropriate patient categorization and thus help determine the best treatment option. Here, we have introduced a proteomic approach utilizing data-independent acquisition mass spectrometry (DIA-MS) to identify the serum proteins that are closely associated with COVID-19 prognosis. Twenty-seven proteins were differentially expressed between severely ill COVID-19 patients with an adverse or favorable prognosis. Ingenuity Pathway Analysis revealed that 15 of the 27 proteins might be regulated by cytokine signaling relevant to interleukin (IL)-1ß, IL-6, and tumor necrosis factor (TNF), and their differential expression was implicated in the systemic inflammatory response and in cardiovascular disorders. We further evaluated practical predictors of the clinical prognosis of severe COVID-19 patients. Subsequent ELISA assays revealed that CHI3L1 and IGFALS may serve as highly sensitive prognostic markers. Our findings can help formulate a diagnostic approach for accurately identifying COVID-19 patients with severe disease and for providing appropriate treatment based on their predicted prognosis.

Combining IL-6 and SARS-CoV-2 RNAaemia-based risk stratification for fatal outcomes of COVID-19.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic rapidly increases the use of mechanical ventilation (MV). Such cases further require extracorporeal membrane oxygenation (ECMO) and have a high mortality. OBJECTIVE: We aimed to identify prognostic biomarkers pathophysiologically reflecting future deterioration of COVID-19. METHODS: Clinical, laboratory, and outcome data were collected from 102 patients with moderate to severe COVID-19. Interleukin (IL)-6 level and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA copy number in plasma were assessed with ELISA kit and quantitative PCR. RESULTS: Twelve patients died or required ECMO owing to acute respiratory distress syndrome despite the use of MV. Among various variables, a ratio of oxygen saturation to fraction of inspired oxygen (SpO2/FiO2), IL-6, and SARS-CoV-2 RNA on admission before intubation were strongly predictive of fatal outcomes after the MV use. Moreover, among these variables, combining SpO2/FiO2, IL-6, and SARS-CoV-2 RNA showed the highest accuracy (area under the curve: 0.934). In patients with low SpO2/FiO2 (< 261), fatal event-rate after the MV use at the 30-day was significantly higher in patients with high IL-6 (> 49 pg/mL) and SARS-CoV-2 RNAaemia (> 1.5 copies/µL) compared to those with high IL-6 or RNAaemia or without high IL-6 and RNAaemia (88% vs. 22% or 8%, log-rank test P = 0.0097 or P < 0.0001, respectively). CONCLUSIONS: Combining SpO2/FiO2 with high IL-6 and SARS-CoV-2 RNAaemia which reflect hyperinflammation and viral overload allows accurately and before intubation identifying COVID-19 patients at high risk for ECMO use or in-hospital death despite the use of MV.