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    Efficacy of copeptin in distinguishing COVID-19 pneumonia from community-acquired pneumonia
    (wıley, 2021) Kuluozturk, Mutlu; Telo, Selda; Karabulut, Ercan
    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.
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    Using artificial intelligence to improve the diagnostic efficiency of pulmonologists in differentiating COVID-19 pneumonia from community-acquired pneumonia
    (Wiley, 2022) In, Erdal; Geckil, Aysegul A.; Kavuran, Gurkan; Sahin, Mahmut; Berber, Nurcan K.; Kuluozturk, Mutlu
    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.