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Öğe Can copeptin predict the severity of coronavirus disease 2019 infection?(Assoc Medica Brasileira, 2021) In, Erdal; Kuluozturk, Mutlu; Telo, Selda; Toraman, Zulal Asci; Karabulut, ErcanOBJETIVE: 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.Öğe Efficacy of copeptin in distinguishing COVID-19 pneumonia from community-acquired pneumonia(Wiley, 2021) Kuluozturk, Mutlu; In, Erdal; Telo, Selda; Karabulut, Ercan; Geckil, Aysegul AltintopThe 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.Öğe The role of endobronchial ultrasound-guided transbronchial needle aspiration in the differential diagnosis of isolated mediastinal and/or hilar lymphadenopathy(Wiley, 2021) Temiz, Dilek; In, Erdal; Kuluozturk, Mutlu; Kirkil, Gamze; Artas, Gokhan; Turgut, Teyfik; Deveci, FigenIntroduction Isolated mediastinal and/or hilar lymphadenopathy (IMHL) has become an increasingly common finding as a result of the increased use of thoracic imaging modalities. Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is accepted as the first step diagnostic method in the differential diagnosis of IMHL. Objective To determine the diagnostic yield of the procedure and to analyze clinical and sonographic findings that can be used to differentiate the etiology of lymph node pathologies. Methods Patients who underwent EBUS-TBNA procedure between March 2017 and March 2020 were included in this retrospective study. Demographic data, symptoms, comorbid diseases, and EBUS findings were obtained from the records of the patients. Results EBUS-TBNA provided a diagnosis in 88 patients out of 120 patients (granulomatous diseases n = 54, malignant diseases n = 21, and anthracotic lymph nodes n = 13), and 32 patients had a negative EBUS-TBNA. 22/32 negative EBUS-TBNA samples were true negatives (reactive lymphadenopathy). The sensitivity of the procedure was 89.8% while negative predict value was 68.7%, diagnostic yield of 91.6%. Patients with reactive lymph nodes had significantly more comorbidities (77.3%-19.4%, p < .001) and a lower number of lymph node stations (1.6 +/- 0.8-2.7 +/- 0.9, p < .001). Patients with anthracotic lymph nodes were older and mostly consisted of females (11/13, p < .001). Conclusion EBUS-TBNA has high-diagnostic efficiency in the differential diagnosis of IMHL. The number and size of lymph node stations can provide useful information for differential diagnosis. Clinical follow-up can be a more beneficial approach in patients with reactive and anthracotic lymph nodes before invasive sampling.Öğe 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, MutluCoronavirus 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.
