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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 Carbonic Anhydrase IX as a Marker of Disease Severity in Obstructive Sleep Apnea(Mdpi, 2022) Geckil, Aysegul Altintop; Kiran, Tugba Raika; Berber, Nurcan Kirici; Otlu, Onder; Erdem, Mehmet; In, ErdalBackground and Objectives: Carbonic anhydrase (CA) enzymes are a family of metalloenzymes that contain a zinc ion in their active sites. CA enzymes have been implied in important situations such as CO2 transport, pH regulation, and oncogenesis. CA-IX is a transmembrane glycoprotein and stimulates the expression of hypoxia-inducible factor-1 (HIF-1) CA-IX. This study aimed to determine serum CA-IX levels in OSA patients in whom intermittent hypoxia is important and to investigate the relationship between serum CA-IX levels and disease severity. Materials and Methods: The study included 88 people who applied to Malatya Turgut Ozal University Training and Research Hospital Sleep Disorders Center without a history of respiratory disease, malignancy, and smoking. Patients were divided into three groups: control (AHI < 5, n = 31), mild-moderate OSA (AHI = 5-30, n = 27) and severe OSA (AHI > 30, n = 30). The analysis of the data included in the research was carried out with the SPSS (IBM Statistics 25, NY, USA). The Shapiro-Wilk Test was used to check whether the data included in the study had a normal distribution. Comparisons were made with ANOVA in multivariate groups and the t-test in bivariate groups. ANCOVA was applied to determine the effect of the CA-IX parameter for OSA by controlling the effect of independent variables. The differentiation in CA-IX and OSA groups was analyzed regardless of BMI, age, gender, and laboratory variables. ROC analysis was applied to determine the parameter cut-off point. Sensitivity, specificity, and cut-off were calculated, and the area under the curve (AUC) value was calculated. Results: Serum CA-IX levels were 126.3 +/- 24.5 pg/mL in the control group, 184.6 +/- 59.1 pg/mL in the mild-moderate OSA group, and 332.0 +/- 39.7 pg/mL in the severe OSA group. Serum CA-IX levels were found to be higher in the severe OSA group compared to the mild-moderate OSA group and control group and higher in the mild-moderate OSA group compared to the control group (p < 0.001, p < 0.001, p < 0.001, respectively). In addition, a negative correlation between CA-IX and minimum SaO(2) and mean SaOI(2) (r = -0.371, p = 0.004; r = -0.319, p = 0.017, respectively). A positive correlation between CA-IX and desaturation index (CT90) was found (r = 0.369, p = 0.005). A positive correlation was found between CA-IX and CRP (r = 0.340, p = 0.010). When evaluated by ROC curve analysis, the area under the curve (AUC) value was determined as 0.940 (95% CI 0.322-0.557; p < 0.001). When the cut-off value for CA-IX was taken as 254.5 pg/mL, it was found to have 96.7% sensitivity and 94.8% specificity in demonstrating severe OSA. Conclusions: Our study found that serum CA-IX value was higher in OSA patients than in control patients, and this elevation was associated with hypoxemia and inflammation. CA-IX value can be a fast, precise, and useful biomarker to predict OSA.Öğe The effects of disease severity and comorbidity on oxidative stress biomarkers in obstructive sleep apnea(Springer Heidelberg, 2024) Kiran, Tugba Raika; Otlu, Onder; Erdem, Mehmet; Geckil, Aysegul Altintop; Berber, Nurcan Kirici; In, ErdalPurposeIschemia-modified albumin (IMA), total oxidant status (TOS), and total antioxidant status (TAS) are biomarkers used to evaluate oxidative stress status in various diseases including obstructive sleep apnea (OSA). In this study, we investigated the effects of disease severity and comorbidity on IMA, TOS and TAS levels in OSA.MethodsPatients with severe OSA (no-comorbidity, one comorbidity, and multiple comorbidities) and mild-moderate OSA (no-comorbidity, one and multiple comorbidities), and healthy controls were included in the study. Polysomnography was applied to all cases and blood samples were taken from each participant at the same time of day. ELISA was used to measure IMA levels in serum samples and colorimetric commercial kits were used to perform TOS and TAS analyses. In addition, routine biochemical analyses were performed on all serum samples.ResultsA total of 74 patients and 14 healthy controls were enrolled. There was no statistically significant difference between the disease groups according to gender, smoking status, age, body mass index (BMI), HDL, T3, T4, TSH, and B12 (p > 0.05). As the severity of OSA and comorbidities increased, IMA, TOS, apnea-hypopnea index (AHI), desaturation index (T90), cholesterol, LDL, triglyceride, AST, and CRP values increased significantly (p < 0.05). On the other hand, TAS, minimum desaturation, and mean desaturation values decreased significantly (p < 0.05).ConclusionsWe concluded that IMA, TOS, and TAS levels may indicate OSA-related oxidative stress, but as the severity of OSA increases and with the presence of comorbidity, IMA and TOS levels may increase and TAS levels decrease. These findings suggest that disease severity and presence/absence of comorbidity should be considered in studies on OSA.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 Efficacy of serum apelin and galectin-3 as potential predictors of mortality in severe COVID-19 patients(Wiley, 2023) Berber, Nurcan Kirici; Geckil, Aysegul Altintop; Altan, Nazife Ozge; Kiran, Tugba Raika; Otlu, Onder; Erdem, Mehmet; In, ErdalApelin 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.Öğe Leukocyte albumin ratio as an early predictor of mortality in critical COVID-19 patients(Bayrakol Medical Publisher, 2022) Geckil, Aysegul Altintop; In, Erdal; Berber, Nurcan Kirici; Kasapoglu, Umut Sabri; Karabulut, Ercan; Ozdemir, CengizAim: The aim of this study is to analyze the effectiveness of the leukocyte albumin ratio (LAR) in predicting mortality in critical COVID-19 patients. Material and Methods: In this retrospectively-designed study, we evaluated a total of 98 critical patients who were hospitalized in the intensive care unit. Patients were divided into two groups according to hospital mortality as survivors (n=43) and non-survivors (n=55). Results: The non-survivors group was statistically significantly older (67.3 +/- 9.7 versus 62.5 +/- 10.9; p=0.023). HT and DM were detected more in the non-survivors group than in the survivors group (p=0.031, p=0.018, respectively). Mean LAR values were significantly higher in non-survivors than in survivors (5.9 +/- 3.5 versus 3.3 +/- 1.4; p<0.001). LAR values was positively correlated with urea (r=0.43, p<0.001), LDH (r=0.35, p<0.001), ferritin (r=0.25, p=0.015), procalcitonin (r=0.34, p<0.001), and pro-BNP (r=0.24, p=0.015) levels. A cut-off value of 3.71 ng/mL for LAR predicted mortality with a sensitivity of 76% and a specificity of 70% (AUC:0.779 95% Cl:0.689-0.870; p<0.001). Multivariable logistic regression analysis revealed that older age (OR:1.114, 95% CI:1.020-1.218; p=0.017) and increased ferritin (OR:1.003, 95% CI:1.001-1.004; p=0.002) and LAR (OR:1.583, 95% CI:1.073-2.337; p=0.021) values were independent predictors of mortality in patients with critical COVID-19. Discussion: LAR can be a useful and prognostic marker that can be used to predict mortality in COVID-19 patients admitted to the intensive care unit.Öğe MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net(Elsevier Science Inc, 2022) Kavuran, Gurkan; In, Erdal; Geckil, Aysegul Altintop; Sahin, Mahmut; Berber, Nurcan KiriciPurpose: 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.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.
