Efficacy of serum apelin and galectin-3 as potential predictors of mortality in severe COVID-19 patients.

Apelin is a cardioprotective biomarker while galectin-3 is a pro-inflammatory and profibrotic biomarker. Endothelial dysfunction, hyperinflammation, and pulmonary fibrosis are key mechanisms that contribute to the development of adverse outcomes in Coronavirus disease 2019 (COVID-19) infection. This study aims to analyze the prognostic value of serum apelin and galectin-3 levels to early predict patients at high risk of mortality in patients hospitalized for severe COVID-19 pneumonia. The study included 78 severe COVID-19 patients and 40 healthy controls. The COVID-19 patients were divided into two groups, survivors and nonsurvivors, according to their in-hospital mortality status. Basic demographic and clinical data of all patients were collected, and blood samples were taken before treatment. In our study, serum apelin levels were determined to be significantly lower in both nonsurvivor and survivor COVID-19 patients compared to the control subjects (for both groups, p < 0.001). However, serum apelin levels were similar in survivor and nonsurvivor COVID-19 patients (p > 0.05). Serum galectin-3 levels were determined to be higher in a statistically significant way in nonsurvivors compared to survivors and controls (for both groups; p < 0.001). Additionally, serum galectin-3 levels were significantly higher in the survivor patients compared to the control subjects (p < 0.001). Positive correlations were observed between galectin-3 and age, ferritin, CK-MB and NT-proBNP variables (r = 0.32, p = 0.004; r = 0.24, p = 0.04; r = 0.24, p = 0.03; and r = 0.33, p = 0.003, respectively) while a negative correlation was observed between galectin-3 and albumin (r = -0.31, p = 0.006). Multiple logistic regression analysis revealed that galectin-3 was an independent predictor of mortality in COVID-19 patients (odds ratio [OR] = 2.272, 95% confidence interval [CI] = 1.106-4.667; p = 0.025). When the threshold value for galectin-3 was regarded as 2.8 ng/ml, it was discovered to predict mortality with 80% sensitivity and 57% specificity (area under the curve = 0.738, 95% CI = 0.611-0.866, p = 0.002). Galectin-3 might be a simple, useful, and prognostic biomarker that can be utilized to predict patients who are at high risk of mortality in severe COVID-19 patients.

Using artificial intelligence to improve the diagnostic efficiency of pulmonologists in differentiating COVID-19 pneumonia from community-acquired pneumonia.

Coronavirus disease 2019 (COVID-19) has quickly turned into a global health problem. Computed tomography (CT) findings of COVID-19 pneumonia and community-acquired pneumonia (CAP) may be similar. Artificial intelligence (AI) is a popular topic among medical imaging techniques and has caused significant developments in diagnostic techniques. This retrospective study aims to analyze the contribution of AI to the diagnostic performance of pulmonologists in distinguishing COVID-19 pneumonia from CAP using CT scans. A deep learning-based AI model was created to be utilized in the detection of COVID-19, which extracted visual data from volumetric CT scans. The final data set covered a total of 2496 scans (887 patients), which included 1428 (57.2%) from the COVID-19 group and 1068 (42.8%) from the CAP group. CT slices were classified into training, validation, and test datasets in an 8:1:1. The independent test data set was analyzed by comparing the performance of four pulmonologists in differentiating COVID-19 pneumonia both with and without the help of the AI. The accuracy, sensitivity, and specificity values of the proposed AI model for determining COVID-19 in the independent test data set were 93.2%, 85.8%, and 99.3%, respectively, with the area under the receiver operating characteristic curve of 0.984. With the assistance of the AI, the pulmonologists accomplished a higher mean accuracy (88.9% vs. 79.9%, p < 0.001), sensitivity (79.1% vs. 70%, p < 0.001), and specificity (96.5% vs. 87.5%, p < 0.001). AI support significantly increases the diagnostic efficiency of pulmonologists in the diagnosis of COVID-19 via CT. Studies in the future should focus on real-time applications of AI to fight the COVID-19 infection.

Can copeptin predict the severity of coronavirus disease 2019 infection?

OBJETIVE: Coronavirus disease 2019 (COVID-19) has quickly turned into a health problem globally. Early and effective predictors of disease severity are needed to improve the management of the patients affected with COVID-19. Copeptin, a 39-amino acid glycopeptide, is known as a C-terminal unit of the precursor pre-provasopressin (pre-proAVP). Activation of AVP system stimulates copeptin secretion in equimolar amounts with AVP. This study aimed to determine serum copeptin levels in the patients with COVID-19 and to examine the relationship between serum copeptin levels and the severity of the disease. METHODS: The study included 90 patients with COVID-19. The patients with COVID-19 were divided into two groups according to disease severity as mild/moderate disease (n=35) and severe disease (n=55). All basic demographic and clinical data of the patients were recorded and blood samples were collected. RESULTS: Copeptin levels were significantly higher in the patients with severe COVID-19 compared with the patients with mild/moderate COVID-19 (p<0.001). Copeptin levels were correlated with ferritin and fibrinogen levels positively (r=0.32, p=0.002 and r=0.25, p=0.019, respectively), and correlated with oxygen saturation negatively (r=-0.37, p<0.001). In the multivariate logistic regression analysis, it was revealed that copeptin (OR: 2.647, 95%CI 1.272-5.510; p=0.009) was an independent predictor of severe COVID-19 disease. A cutoff value of 7.84 ng/mL for copeptin predicted severe COVID-19 with a sensitivity of 78% and a specificity of 80% (AUC: 0.869, 95%CI 0.797-0.940; p<0.001). CONCLUSION: Copeptin could be used as a favorable prognostic biomarker while determining the disease severity in COVID-19.

MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net.

PURPOSE: The aim of this study was to establish and evaluate a fully automatic deep learning system for the diagnosis of COVID-19 using thoracic computed tomography (CT). MATERIALS AND METHODS: In this retrospective study, a novel hybrid model (MTU-COVNet) was developed to extract visual features from volumetric thoracic CT scans for the detection of COVID-19. The collected dataset consisted of 3210 CT scans from 953 patients. Of the total 3210 scans in the final dataset, 1327 (41%) were obtained from the COVID-19 group, 929 (29%) from the CAP group, and 954 (30%) from the Normal CT group. Diagnostic performance was assessed with the area under the receiver operating characteristic (ROC) curve, sensitivity, and specificity. RESULTS: The proposed approach with the optimized features from concatenated layers reached an overall accuracy of 97.7% for the CT-MTU dataset. The rest of the total performance metrics, such as; specificity, sensitivity, precision, F1 score, and Matthew Correlation Coefficient were 98.8%, 97.6%, 97.8%, 97.7%, and 96.5%, respectively. This model showed high diagnostic performance in detecting COVID-19 pneumonia (specificity: 98.0% and sensitivity: 98.2%) and CAP (specificity: 99.1% and sensitivity: 97.1%). The areas under the ROC curves for COVID-19 and CAP were 0.997 and 0.996, respectively. CONCLUSION: A deep learning-based AI system built on the CT imaging can detect COVID-19 pneumonia with high diagnostic efficiency and distinguish it from CAP and normal CT. AI applications can have beneficial effects in the fight against COVID-19.

The effect of radiological distribution on the COVID-19 disease.

INTRODUCTION: The aim of the study was to investigate the effects of radiological distribution on COVID-19 clinic and prognosis and to determine the relationship between laboratory parameters and thorax CT findings. MATERIALS AND METHODS: Patients with COVID-19 were evaluated retrospectively. Laboratory parameters were obtained from medical records. Ground-glass opacities (GGO) and consolidation were evaluated on thorax CT. The presence of a single lobe lesion was considered as limited while multiple lobe lesions were considered as diffuse involvement for both GGO and consolidation. RESULT: A total 200 patients with COVID-19 were evaluated. 178 of them (89%) were discharged, 17 patients (8.5%) were transferred to the ICU and five patients died (2.5%). The ratios of mortality and transfer to the ICU in patients with diffused GGO were significantly higher compared to patients with limited GGOs. It was observed that troponin ≥0.06 µg/L, platelet <140 and fibrinogen ≥350 mg/dl were independent predictors of the presences of diffused GGOs in thorax CT. CONCLUSIONS: Diffused GGOs on thorax CT are correlated with the rate of mortality and transfer to the ICU in patients with COVID-19. Also, troponin, fibrinogen, and platelet levels can be used while predicting extensive parenchymal disease on thorax CT.

Efficacy of copeptin in distinguishing COVID-19 pneumonia from community-acquired pneumonia.

The clinical symptoms of community-acquired pneumonia (CAP) and coronavirus disease 2019 (COVID-19)-associated pneumonia are similar. Effective predictive markers are needed to differentiate COVID-19 pneumonia from CAP in the current pandemic conditions. Copeptin, a 39-aminoacid glycopeptide, is a C-terminal part of the precursor pre-provasopressin (pre-proAVP). The activation of the AVP system stimulates copeptin secretion in equimolar amounts with AVP. This study aims to determine serum copeptin levels in patients with CAP and COVID-19 pneumonia and to analyze the power of copeptin in predicting COVID-19 pneumonia. The study consists of 98 patients with COVID-19 and 44 patients with CAP. The basic demographic and clinical data of all patients were recorded, and blood samples were collected. The receiver operating characteristic (ROC) curve was generated and the area under the ROC curve (AUC) was measured to evaluate the discriminative ability. Serum copeptin levels were significantly higher in COVID-19 patients compared to CAP patients (10.2 ± 4.4 ng/ml and 7.1 ± 3.1 ng/ml; p < .001). Serum copeptin levels were positively correlated with leukocyte, neutrophil, and platelet count (r = -.21, p = .012; r = -.21, p = .013; r = -.20, p = .018; respectively). The multivariable logistic regression analysis revealed that increased copeptin (odds ratio [OR] = 1.183, 95% confidence interval [CI], 1.033-1.354; p = .015) and CK-MB (OR = 1.052, 95% CI, 1.013-1.092; p = .008) levels and decreased leukocyte count (OR = 0.829, 95% CI, 0.730-0.940; p = .004) were independent predictors of COVID-19 pneumonia. A cut-off value of 6.83 ng/ml for copeptin predicted COVID-19 with a sensitivity of 78% and a specificity of 73% (AUC: 0.764% 95 Cl: 0.671-0.856, p < .001). Copeptin could be a promising and useful biomarker to be used to distinguish COVID-19 patients from CAP patients.