Neslihan Ozcelik

Background: Pulmonary involvement is one of the most serious extra-articular manifestations of rheumatoid arthritis (RA), frequently remaining clinically silent until irreversible lung damage develops. This study aimed to determine the prevalence of high-resolution computed tomography (HRCT)-detected pulmonary involvement in patients with RA; compare the clinical, laboratory, and pulmonary function characteristics of patients with and without pulmonary involvement; and identify factors associated with pulmonary abnormalities. Methods: In this single-center retrospective cross-sectional study, 141 patients fulfilling the 2010 ACR/EULAR classification criteria for RA who had undergone thoracic HRCT were included. Clinical characteristics, laboratory findings, disease activity scores, pulmonary function tests, and HRCT findings were compared between patients with and without pulmonary involvement. Logistic regression analyses were performed to identify independent predictors of pulmonary involvement, and receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of serum uric acid. Results: HRCT identified pulmonary involvement in 33 of 141 patients (23.4%), with nonspecific interstitial pneumonia being the most common radiological pattern (39.4%), followed by usual interstitial pneumonia (24.2%). Patients with pulmonary involvement had significantly higher serum uric acid concentrations (5.65 ± 1.62 vs. 4.51 ± 1.31 mg/dL, p < 0.001), lower serum albumin levels (38.8 ± 4.9 vs. 40.9 ± 3.5 g/L, p = 0.008), higher platelet counts (p = 0.006), and lower forced vital capacity and predicted FEV₁ values (p = 0.010 and p = 0.019, respectively). In multivariable logistic regression analysis, serum uric acid remained the only independent predictor of pulmonary involvement (odds ratio, 1.656; 95% confidence interval, 1.187–2.311; p = 0.003). ROC analysis demonstrated acceptable discriminatory performance for serum uric acid (AUC = 0.713), with an optimal cutoff value of >5 mg/dL yielding 68% sensitivity and 70% specificity. Conclusions: Nearly one-quarter of patients with RA demonstrated HRCT-detected pulmonary involvement despite having a similar prevalence of respiratory symptoms. Elevated serum uric acid was independently associated with pulmonary involvement and may represent an inexpensive, readily available biomarker to identify patients who may benefit from targeted pulmonary evaluation. These findings support the integration of simple laboratory markers with clinical assessment to facilitate earlier recognition of RA-associated pulmonary disease.

Background: The Prognostic Nutritional Index (PNI), derived from serum albumin and peripheral lymphocyte count, may reflect immunonutritional status in patients with community-acquired pneumonia (CAP). This study evaluated the association between admission PNI and 30-day mortality and compared its discriminatory performance with the Pneumonia Severity Index (PSI) and CURB-65. Methods: This retrospective single-center cohort included 214 adults hospitalized with CAP between January 2025 and June 2026. Demographic, clinical, and laboratory data obtained at admission were analyzed. Thirty-day all-cause mortality was the primary outcome. Receiver operating characteristic analysis and multivariable logistic regression were performed. Results: Thirty-day mortality occurred in 61 patients (28.5%). PNI was significantly lower in non-survivors than survivors (34.5 ± 5.6 vs. 39.1 ± 6.4; p < 0.001). PNI showed moderate discrimination for 30-day mortality (AUC 0.713, 95% CI 0.637–0.789), with a numerically higher AUC than PSI (0.656) and CURB-65 (0.670); however, pairwise differences were not statistically significant. A cohort-derived exploratory PNI cutoff of ≤36.6 yielded 70.5% sensitivity and 67.3% specificity. In multivariable analysis, continuous PNI remained independently associated with mortality (OR 0.869 per one-unit increase, 95% CI 0.814–0.927; p < 0.001). Adding PNI significantly improved discrimination compared with PSI alone (AUC 0.740 vs. 0.656; p = 0.034) and CURB-65 alone (AUC 0.771 vs. 0.670; p = 0.001). Conclusions: Lower admission PNI was independently associated with 30-day mortality and demonstrated moderate discrimination in hospitalized patients with CAP. Although PNI alone did not significantly outperform PSI or CURB-65, adding PNI significantly improved the discriminatory performance of both established severity scores, supporting its potential incremental prognostic value.

Background: Community-acquired pneumonia (CAP) is a common infectious disease associated with substantial morbidity and mortality. Acute kidney injury (AKI) is a frequent complication of CAP and may adversely affect clinical outcomes. This study aimed to determine the frequency of AKI and identify factors associated with AKI in hospitalized patients with CAP. Methods: This retrospective cohort study was conducted at a tertiary care university hospital in Türkiye and included patients aged >18 years hospitalized with CAP between July 2024 and July 2025. Only patients initially admitted to the general ward were included. Patients receiving dialysis for end-stage renal disease, those with active malignancy, those receiving immunosuppressive therapy, and those with incomplete data were excluded. Demographic characteristics, vital signs, laboratory findings, comorbidities, smoking status, and medication histories were recorded. AKI was defined according to KDIGO criteria using the closest clinically stable pre-admission serum creatinine value obtained within 7–365 days before hospitalization as baseline. Results: Among 161 patients, AKI was identified in 60 (37.3%), including 43 patients who fulfilled the KDIGO criteria at hospital admission and 17 who developed AKI during hospitalization. Eight patients with AKI (13.3%) required hemodialysis, although none became permanently dialysis-dependent. Patients with AKI had longer hospital stays (p = 0.038) and a greater need for intensive care (p = 0.028). They were older and had higher respiratory rates, blood urea nitrogen (BUN), and red cell distribution width (RDW) values and lower diastolic blood pressure. DM and CKD were more common in the AKI group. In multivariable analysis, confusion (adjusted OR 5.03, 95% CI 1.01–25.02; p = 0.048), CKD (adjusted OR 6.00, 95% CI 1.28–28.13; p = 0.023), and higher BUN levels (adjusted OR 1.095 per 1 mg/dL increase, 95% CI 1.046–1.146; p < 0.001) were independently associated with AKI. These associations were retained in a Firth penalized logistic regression sensitivity analysis. Conclusions: AKI was common among hospitalized patients with CAP and was associated with longer hospital stay and greater intensive care utilization. Confusion, pre-existing CKD, and elevated BUN levels were independently associated with AKI, whereas DM was not independently associated after multivariable adjustment. Given that most AKI cases were already present at hospital admission, these findings should be interpreted as associations rather than predictors of incident AKI.

Background: Artificial intelligence (AI), a set of algorithms that mimic human intelligence, is increasingly being used in the healthcare field. Objective: This study aims to evaluate the awareness, expectations, and concerns of pulmonologists and thoracic surgeons regarding AI, as well as to assess their level of adaptation to these technologies. Methods: A 23-item online survey was administered to specialists and residents in the fields of pulmonology and thoracic surgery between January and May 2025. Participation was voluntary, and informed consent was obtained from all respondents. Data were analyzed using SPSS version 27.0, with a p-value of < 0.05 considered statistically significant. Results: Among the 302 participants, 65.6% were pulmonologists and 34.4% were thoracic surgeons. A total of 85.4% believed that AI will be actively used in clinical practice, and 67.5% believed that AI would reduce the need for physician workforce. However, 59.3% considered AI less successful than physicians in diagnosis, and 65.6% in treatment. In addition, 86.8% emphasized the need for legal regulations, and 58.3% expressed concerns about data privacy. Conclusions: Participants generally expressed a positive attitude toward AI, although they remained cautious due to ethical, legal, and technical concerns. Comprehensive regulations regarding education, legislation, and data security are needed to ensure the safe and effective integration of AI into clinical practice.

Background: Differentiating exudative from transudative pleural effusions is essential for accurate diagnosis and management. Although Light’s criteria remain the standard diagnostic method, they may incorrectly classify a substantial proportion of transudative effusions as exudates. This study aimed to evaluate the diagnostic performance of pleural fluid cholesterol and compare it with Light’s criteria. Methods: This retrospective study included 751 patients with pleural effusion who underwent diagnostic evaluation between 2015 and 2024 and had a definitive etiological diagnosis. All patients underwent ultrasound-guided thoracentesis with simultaneous pleural fluid and serum biochemical analyses. Based on final etiological diagnosis, effusions were classified as transudative or exudative. Results: The mean age was 68.6 ± 15.8 years. Of the pleural effusions, 516 (68.7%) were exudative and 235 (31.3%) were transudative. Malignant pleural effusion was the most common cause of exudates, whereas congestive heart failure predominated among transudates. Pleural fluid cholesterol levels were significantly higher in exudative than transudative effusions (91.1 ± 30.7 vs. 28.9 ± 13.5 mg/dL, p < 0.001). Receiver operating characteristic (ROC) analysis demonstrated excellent diagnostic performance for pleural fluid cholesterol (AUC = 0.976, 95% CI 0.965–0.987, p < 0.001). A pleural cholesterol cut-off value of ≥50 mg/dL demonstrated 92.2% sensitivity, 94.9% specificity, 97.5% positive predictive value, and 84.8% negative predictive value for identifying exudative effusions. Light’s criteria showed higher sensitivity (99.2%) but lower specificity (84.4%). Light’s criteria misclassified 15.6% of transudative effusions as exudates, whereas pleural cholesterol misclassified only 5.1%. Conclusions: Pleural fluid cholesterol measurement demonstrated high diagnostic accuracy in differentiating exudative and transudative pleural effusions and substantially reduced false exudate classification compared with Light’s criteria. Routine assessment of pleural cholesterol may improve the diagnostic evaluation of pleural effusions.

Objective: This study investigates the relationship between levels of internet and social media addiction and nicotine dependence among tobacco users, along with the influencing factors. Material and Method: Data were collected through an online questionnaire using tools such as the Fagerström Test for Nicotine Dependence (FTND) the Young Internet Addiction Test Short Form (YIAT-SF), and the Bergen Social Media Addiction Scale (BSMAS). Sociodemographic details, tobacco use patterns, and addiction indicators were also assessed. Results: The study included 255 participants (average age: 33.6 years), predominantly male, with an average smoking duration of 13.5 years. Cigarettes were the most commonly used tobacco product. The mean FTND score was 4.0±2.7, with no significant gender differences. However, FTND scores were significantly higher in participants who consumed more tobacco while on social media (5.4±2.5, p=0.001). The primary variables associated with FTND score were found to be the YIAT-SF score, increased consuming tobacco products more while spending time on social media and age.Conclusion: Our study has shown that increased cigarette consumption during social media use is associated with nicotine dependence.

Background: Early risk assessment in acute pulmonary embolism (PE) remains challenging, particularly in normotensive patients. Lactate may offer incremental prognostic value beyond conventional tools. We investigated the association between arterial lactate and in-hospital mortality in acute PE. Methods: In this retrospective single-center study, 327 adult patients diagnosed with acute PE by computed tomography pulmonary angiography who underwent arterial blood gas analysis within the first six hours of emergency department presentation were included. Patients were categorized according to the occurrence of in-hospital mortality, including 103 (31.5%) non-survivors and 224 (68.5%) survivors, and their demographic, clinical, laboratory, and echocardiographic characteristics were compared accordingly. Results: Arterial lactate levels were significantly higher in non-survivors than survivors [4.1 vs. 1.9 mmol/L; p < 0.001], with a stepwise increase in mortality across lactate categories (4 mmol/L; p < 0.001). In normotensive patients (n = 211), lactate ≥2 mmol/L was associated with higher mortality compared with p < 0.001). In multivariable logistic regression analysis performed in normotensive patients, arterial lactate level, PESI score, and the presence of cerebrovascular disease were identified as independent predictors of in-hospital mortality, whereas troponin did not retain independent significance. In normotensive patients, lactate showed better discriminative ability than troponin I (AUC 0.718 vs. 0.553). Conclusions: Arterial lactate levels are independently associated with in-hospital mortality in acute PE. Elevated lactate may help identify high-risk patients even in the absence of hypotension and may provide incremental prognostic value beyond existing risk stratification tools. These findings suggest the use of arterial lactate in early risk assessment.

Background: Obstructive sleep apnea (OSA) is a significant cardiovascular risk factor, frequently co-existing with systemic hypertension. While the association between OSA and blood pressure elevation is well documented, its specific prevalence and impact among patients with resistant hypertension remain a critical area of clinical investigation. Objective: The primary aim of this study was to evaluate the prevalence and severity of obstructive sleep apnea (OSA) specifically within a cohort of patients with resistant hypertension (RHT). Additionally, we sought to identify the clinical and anthropometric factors that distinguish RHT patients with OSA from non-resistant hypertensive and normotensive controls, thereby clarifying the increased hypoxic burden and polysomnographic differences unique to this high-risk population. Methods: A total of 300 patients presenting with OSA symptoms were included. Participants were classified into three groups: Group 0 (n = 100), normotensive individuals; Group 1 (n = 100), non-resistant hypertension; and Group 2 (n = 100), confirmed resistant hypertension. Standard overnight polysomnography (PSG) was performed on all participants. Demographic data, comorbidities, anthropometric measurements, and PSG parameters were recorded and compared across groups. Results: Of the subjects, 50.3% were female (sex), and the mean age was 49.5 ± 12.3 years. Patients with RHT (Group 2) were significantly older and had a higher prevalence of diabetes mellitus. OSA prevalence was 94% in Group 2 (37% severe), 89% in Group 1 (22% severe), and 74% in Group 0 (only 2% severe). In PSG analysis, AHI and ODI values were significantly higher in hypertensive groups (Groups 1 and 2) compared to normotensive individuals (Group 0), while minimum and mean oxygen saturations were significantly lower. Conclusions: OSA is both more prevalent and more severe in patients with resistant hypertension. Furthermore, hypertensive OSA patients are characterized by an increased hypoxic load compared to normotensives. Systematic investigation and detailed polysomnographic evaluation of OSA are of paramount importance in hypertensive individuals, particularly those with resistant hypertension.

Background: Obstructive sleep apnea (OSA) is strongly associated with metabolic dysfunction and may contribute to the development of hepatic disorders such as metabolic dysfunction-associated steatotic liver disease (MASLD) and liver fibrosis. Early identification of hepatic involvement in OSA is important for preventing long-term complications. This study aimed to develop and compare artificial intelligence (AI)-based models using non-invasive clinical parameters to predict steatosis, MASLD, and liver fibrosis in patients with OSA. Methods: This prospective study included patients diagnosed with OSA based on polysomnography (PSG). Hepatic steatosis and fibrosis were assessed non-invasively using the controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) obtained by FibroScan. A total of 700 patients were screened, and 517 patients who met the inclusion criteria were analyzed, including 42 demographic, anthropometric, laboratory, and sleep-related variables. A total of 42 variables were recorded, comprising 37 candidate predictors and 4 target outcomes. Following feature selection, 11 of the 37 candidates were retained for model training. Four supervised machine learning algorithms—logistic regression, support vector machine, random forest, and artificial neural network were trained to predict steatosis, MASLD, and liver fibrosis. Model performance was evaluated by accuracy, F1-score, and area under the ROC curve. Feature importance analysis identified the most influential predictors. Results: Among the four models, the Random Forest algorithm achieved the best performance, with an accuracy of 96% for binary classification; in an exploratory five-class analysis, overall accuracy reached 92%, although predominantly driven by the majority F0 class. The most influential predictors were apnea–hypopnea index (AHI), minimum oxygen saturation (minSpO ₂ ), and oxygen desaturation index. A sensitivity analysis excluding elastography-derived features (CAP and LSM) was performed using 9 predictor variables (Model 2). Model 2 achieved best AUC values of 0.700, 0.706, and 0.634 for steatosis, MASLD, and fibrosis, respectively, compared with 0.967, 0.969, and 0.985 in the original 11-feature model (Model 1). Conclusion: AI-based models, particularly Random Forest, demonstrated high predictive performance in predicting MASLD and liver fibrosis in OSA patients. These models may support detection and risk stratification of hepatic involvement in sleep-disordered breathing.