Investigation of the relationship of CO-RADS and CT patterns with laboratory parameters in COVID-19 patients and a new perspective on the total CT scoring system.

BACKGROUND: It is important to determine the correlation of the CO-RADS classification and computed tomography (CT) patterns of the lung with laboratory data. To investigate the relationship of CO-RADS categories and CT patterns with laboratory data in patients with a positive RT-PCR test. We also developed a structured total CT scoring system and investigated its correlation with the total CT scoring system. METHOD: The CT examinations of the patients were evaluated in terms of the CO-RADS classification, pattern groups and total CT score. Structured total CT score values were obtained by including the total CT score values and pattern values in a regression analysis. The CT data were compared according to the laboratory data. RESULTS: A total of 198 patients were evaluated. There were significant differences between the CO-RADS groups in terms of age, ICU transfer, oxygen saturation, creatinine, LDH, D-dimer, high-sensitivity cardiac troponin-T (hs-TnT), CRP, structured total CT score values, and total CT score values. A significant difference was also observed between the CT pattern groups and oxygen saturation, creatinine and CRP values. When the structured total CT score values and total CT score values were compared they were observed to be correlated. CONCLUSIONS: Creatinine can be considered as an important marker for the CO-RADS and pattern classifications in lung involvement. LDH can be considered as an important marker of parenchymal involvement, especially bilateral and diffuse involvement. The structured total CT scoring system is a new system that can be used as an alternative.

Association of polymorphisms in tumor necrosis factors with SARS-CoV-2 infection and mortality rate: A case-control study and in silico analyses.

The present coronavirus disease 2019 (COVID-19) is spreading rapidly and existing data has suggested a number of susceptibility factors for developing a severe course of the disease.  The current case-control experiment is aimed to study the associations of genetic polymorphisms in tumor necrosis factors (TNFs) with COVID-19 and its mortality rate. A total of 550 participants (275 subjects and 275 controls) were enrolled. The tetra-amplification refractory mutation system polymerase chain reaction technique was recruited to detect -308G>A TNFα and +252A>G TNFß polymorphisms among the Iranian subjects. We demonstrated that carriers of the G allele of TNFß-252A/G, rs909253 A>G were more frequent in COVID-19 subjects compared to the healthy group and this allele statistically increased the disease risk (odds ratio [OR] = 1.55, 95% confidence interval [CI] = 1.23-1.96, p < 0.0001). At the same time, the A allele of TNFα-311A/G, rs1800629 G>A moderately decreased the risk of COVID-19 (OR = 0.68, 95% CI = 0.53-0.86, p < 0.002). Also, we analyzed the various genotypes regarding the para-clinical and disorder severity; we found that in the AA genotype of TNFß-252A/G (rs909253 A>G), the computed tomography scan pattern was different in comparison to cases carrying the AG genotype with p1 < 0.001. In addition, in the severe cases of COVID-19, leukocyte and neutrophil count and duration of intensive care unit hospitalization in the deceased patients were significantly increased (p < 0.001). Moreover, the TNFα-311A/G (rs1800629 G>A) variant is likely to change the pattern of splicing factor sites. Our findings provided deep insights into the relationship between TNFα/TNFß polymorphisms and severe acute respiratory syndrome coronavirus 2. Replicated studies may give scientific evidence for exploring molecular mechanisms of COVID-19 in other ethnicities.

A Pattern Categorization of CT Findings to Predict Outcome of COVID-19 Pneumonia.

Background: As global healthcare system is overwhelmed by novel coronavirus disease (COVID-19), early identification of risks of adverse outcomes becomes the key to optimize management and improve survival. This study aimed to provide a CT-based pattern categorization to predict outcome of COVID-19 pneumonia. Methods: One hundred and sixty-five patients with COVID-19 (91 men, 4-89 years) underwent chest CT were retrospectively enrolled. CT findings were categorized as Pattern 0 (negative), Pattern 1 (bronchopneumonia pattern), Pattern 2 (organizing pneumonia pattern), Pattern 3 (progressive organizing pneumonia pattern), and Pattern 4 (diffuse alveolar damage pattern). Clinical findings were compared across different categories. Time-dependent progression of CT patterns and correlations with clinical outcomes, i.e." discharge or adverse outcome (admission to ICU, requiring mechanical ventilation, or death), with pulmonary sequelae (complete absorption or residuals) on CT after discharge were analyzed. Results: Of 94 patients with outcome, 81 (86.2%) were discharged, 3 (3.2%) were admitted to ICU, 4 (4.3%) required mechanical ventilation, 6 (6.4%) died. 31 (38.3%) had complete absorption at median day 37 after symptom onset. Significant differences between pattern-categories were found in age, disease severity, comorbidity and laboratory results (all P < 0.05). Remarkable evolution was observed in Pattern 0-2 and Pattern 3-4 within 3 and 2 weeks after symptom-onset, respectively; most of patterns remained thereafter. After controlling for age, CT pattern significantly correlated with adverse outcomes [Pattern 4 vs. Pattern 0-3 [reference]; hazard-ratio [95% CI], 18.90 [1.91-186.60], P = 0.012]. CT pattern [Pattern 3-4 vs. Pattern 0-2 [reference]; 0.26 [0.08-0.88], P = 0.030] and C-reactive protein [>10 vs. ≤ 10 mg/L [reference]; 0.31 [0.13-0.72], P = 0.006] were risk factors associated with pulmonary residuals. Conclusion: CT pattern categorization allied with clinical characteristics within 2 weeks after symptom onset would facilitate early prognostic stratification in COVID-19 pneumonia.