Yazar "Gulbas, Gazi" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • [ X ]
    Öğe
    Carotid intima-media thickness in chronic obstructive pulmonary disease and survival: A multicenter prospective study
    (Wiley, 2019) Gulbas, Gazi; Turan, Onur; Sarioglu, Nurhan; Diken, Ozlem Ercen; Ogan, Nalan; Kadioglu, Esra Ekbic; Kurtipek, Ercan
    Introduction Chronic obstructive pulmonary disease (COPD) is associated with increased cardiovascular morbidity and mortality. Carotid intima-media thickness (CIMT) is a noninvasive method assessing atherosclerosis. Objective It was aimed to determine relationship and survival between COPD and CIMT. Methods CIMT was measured using Doppler ultrasound (USG) in 668 stable COPD patients at 24 centers. Patients were followed-up for 2 years. Results There were 610 patients who completed the study. There were 200 patients CIMT with <0.78 mm (group 1), and 410 with CIMT >= 0.78 mm (group 2). There was a significant difference at the parameters of age, gender, smoking load, biomass exposure, GOLD groups and degree of airway obstruction (FEV1) between groups 1 and 2. Our results revealed positive correlations between mean CIMT and age, smoking load (pack-years), biomass exposure (years), exacerbation rate (last year), duration of hypertension (years) and cholesterol level; negative correlations between CIMT and FEV1 (P < 0.05). According to logistic regression model, compared with group A, risk of CIMT increase was 2.2-fold in group B, 9.7-fold in group C and 4.4-fold in group D (P < 0.05). Risk of CIMT increase was also related with cholesterol level (P < 0.05). Compared with infrequent exacerbation, it was 2.8-fold in the patients with frequent exacerbation (P < 0.05). The mean survival time was slightly higher in group 1, but not significant (23.9 vs 21.8 months) (P > 0.05). Conclusion This study is the first regarding CIMT with combined GOLD assessment groups. It has revealed important findings supporting the increase in atherosclerosis risk in COPD patients. We recommend Doppler USG of the carotid artery in COPD patients at severe stages.
  • [ X ]
    Öğe
    Chronic obstructive pulmonary disease phenotypes in Turkey: the COPET study-a national, multicenter cross-sectional observational study
    (Tubitak Scientific & Technological Research Council Turkey, 2022) Yazar, Esra Ertan; Yigitbas, Burcu Arpinar; Ozturk, Can; Calikoglu, Mukadder; Gulbas, Gazi; Turan, Muzaffer Onur; Sahin, Hulya
    Background/aim: While mortality rates decrease in many chronic diseases, it continues to increase in COPD. This situation has led to the need to develop new approaches such as phenotypes in the management of COPD. We aimed to investigate the distribution, characteristics and treatment preference of COPD phenotypes in Turkey. Materials and methods: The study was designed as a national, multicenter, observational and cross-sectional. A total of 1141 stable COPD patients were included in the analysis. Results: The phenotype distribution was as follows: 55.7% nonexacerbators (NON-AE), 25.6% frequent exacerbators without chronic bronchitis (AE NON-CB), 13.9% frequent exacerbators with chronic bronchitis (AE-CB), and 4.8% with asthma and COPD overlap (ACO). The FEV 1 values were significantly higher in the ACO and NON- AE than in the AE-CB and AE NON-CB (p < 0.001). The symptom scores, ADO (age, dyspnoea and FEV 1) index and the rates of exacerbations were significantly higher in the AE-CB and AE NON-CB phenotypes than in the ACO and NON-AE phenotypes (p < 0.001). Treatment preference in patients with COPD was statistically different among the phenotypes (p < 0.001). Subgroup analysis was performed in terms of emphysema, chronic bronchitis and ACO phenotypes of 1107 patients who had thoracic computed tomography. A total of 202 patients had more than one phenotypic trait, and 149 patients showed no features of a specific phenotype. Conclusion: Most of the phenotype models have tried to classify the patient into a certain phenotype so far. However, we observed that some of the patients with COPD had two or more phenotypes together. Therefore, rather than determining which phenotype the patients are classified in, searching for the phenotypic traits of each patient may enable more effective and individualized treatment.