Background and Objectives: Obstructive sleep apnea (OSA) is a prevalent chronic condition that has been associated with mental disorders like depression and anxiety. This study intends to provide a practical overview of the most relevant self-reported and self-rating scales that assess depression and anxiety in OSA patients. Materials and Methods: A search for articles was performed using PubMed, Google Scholar, and Semantic Scholar using a combination of words for obstructive sleep apnea, depression, anxiety, and scales. The tools were ordered by type (screening and rating) and arranged chronologically according to the year of publication. Results: Three scales were identified for assessing depression, which were the Center for Epidemiologic Studies Depression Scale (CES-D), the Hospital Anxiety and Depression Scale (HADS-D), and the Patient Health Questionnaire-9 (PHQ-9). For rating depression, two scales were discussed: the Zung Self-Rating Depression Scale (SDS) and the Beck Depression Inventory (BDI), which has three versions (the BDI, the BDI-II, and the Fast Screen (BDI-FS)). For assessing anxiety, the Generalized Anxiety Disorder-7 (GAD-7) scale was identified. Two scales were reviewed for rating anxiety: the State-Trait Anxiety Inventory (STAI) and the Beck Anxiety Inventory (BAI). Each scale is accompanied by a brief description of its practicality and psychometric qualities and an analysis of its strengths and limitations. Conclusions: The findings of this review will contribute to the understanding of the importance of assessing mental health comorbidities in the context of OSA, ultimately guiding clinical practice and future research in this area.

Asthma is a prevalent chronic disease, contributing significantly to the global burden of disease and economic costs. Despite advances in treatment, inadequate disease management and reliance on reliever medications lead to preventable deaths. Telemedicine, defined as the use of information and communication technology to improve healthcare access, has gained global attention, especially during the COVID-19 pandemic. This systematic review examines the effectiveness of home monitoring systems in managing severe asthma. A systematic literature search was conducted in PubMed, Web of Science, Scopus, and Cochrane Library, focusing on studies from 2014 to 2024. Fourteen studies involving 9093 patients were analyzed. The results indicate that telemedicine, through tools such as mobile applications and portable spirometers, positively impacts asthma control, self-management, and quality of life. Home spirometry, in particular, shows strong agreement with clinic spirometry, offering a feasible alternative for continuous monitoring. Digital coaching and machine learning-based telemedicine applications also demonstrate significant potential in improving asthma outcomes. However, challenges such as technology accessibility, data privacy, and the need for standardized protocols remain. This review highlights the promise of telemedicine in asthma management and calls for further research to optimize its implementation and address existing barriers.

Background/Objectives: Lung cancer remains a major cause of cancer-related mortality, with regional differences in incidence and patient characteristics. This study aimed to verify and quantify a perceived dramatic increase in lung cancer cases at a Romanian center, identify distinct patient phenotypes using unsupervised machine learning, and characterize contributing factors, including demographic shifts, changes in the healthcare system, and geographic patterns. Methods: A comprehensive retrospective analysis of 4206 lung cancer patients admitted between 2013 and 2024 was conducted, with detailed molecular characterization of 398 patients from 2023 to 2024. Temporal trends were analyzed using statistical methods, while k-means clustering on 761 clinical features identified patient phenotypes. The geographic distribution, smoking patterns, respiratory comorbidities, and demographic factors were systematically characterized across the identified clusters. Results: We confirmed an 80.5% increase in lung cancer admissions between pre-pandemic (2013–2020) and post-pandemic (2022–2024) periods, exceeding the 51.1% increase in total hospital admissions and aligning with national Romanian trends. Five distinct patient clusters emerged: elderly never-smokers (28.9%) with the highest metastatic rates (44.3%), heavy-smoking males (27.4%), active smokers with comprehensive molecular testing (31.7%), young mixed-gender cohort (7.3%) with balanced demographics, and extreme heavy smokers (4.8%) concentrated in rural areas (52.6%) with severe comorbidity burden. Clusters demonstrated significant differences in age (p < 0.001), smoking intensity (p < 0.001), geographic distribution (p < 0.001), as well as molecular characteristics. COPD prevalence was exceptionally high (44.8–78.9%) across clusters, while COVID-19 history remained low (3.4–8.3%), suggesting a limited direct association between the pandemic and cancer. Conclusions: This study presents the first comprehensive machine learning-based stratification of lung cancer patients in Romania, confirming genuine epidemiological increases beyond healthcare system artifacts. The identification of five clinically meaningful phenotypes—particularly rural extreme smokers and age-stratified never-smokers—demonstrates the value of unsupervised clustering for regional healthcare planning. These findings establish frameworks for targeted screening programs, personalized treatment approaches, and resource allocation strategies tailored to specific high-risk populations while highlighting the potential of artificial intelligence in identifying actionable clinical patterns for the implementation of precision medicine.

COVID-19 continues to impact global health systems even after being declared over, with some patients exhibiting severe complications linked to pre-existing conditions. This study aimed to investigate the association between comorbidities, complications, and survival outcomes among COVID-19 survivors in Western Romania. Our hypothesis posited that comorbidities and complications significantly influence survival rates. We conducted a retrospective analysis of 1948 COVID-19 survivors admitted from January to December 2021, with 192 selected for detailed analysis based on inclusion and exclusion criteria. The severity of COVID-19 was classified according to WHO guidelines, and conditions like hypertension and obesity were defined using criteria from the European Society of Hypertension (ESH), the European Society of Cardiology (ESC), and WHO, respectively. Among the 192 patients, 33 had mild, 62 had moderate, and 97 had severe COVID-19. The median age across the severity groups was 63.2 years. Patients undergoing tracheostomy had a mortality rate of 83.3% versus 22.2% for non-tracheostomy patients (p < 0.001) and presented with significantly higher lung injury, hospitalization duration, and complications. Remarkably, tracheostomized patients were 17.50 times more likely to succumb to the disease (95% CI 4.39–116.91, p < 0.001). Furthermore, pneumothorax increased the mortality risk significantly (OR 22.11, 95% CI 5.72–146.03, p < 0.001). Intriguingly, certain conditions like grade I hypertension and grade II obesity showed a protective effect against mortality, whereas type 2 diabetes mellitus increased mortality risk (univariate OR 2.89, p = 0.001). The presence of certain comorbidities and complications significantly impacts the survival rates of COVID-19 patients in Western Romania. Notably, tracheostomy, pneumothorax, and T2DM were associated with increased mortality. This study underscores the importance of personalized patient care and provides insights for healthcare policymakers in Western Romania to improve clinical management strategies.

Background: Status asthmaticus is a severe, life-threatening asthma exacerbation requiring urgent medical intervention. This study aims to examine its epidemiology in Timis County, Romania, over 11 years. Methods: A retrospective analysis was conducted using hospital records from 2013 to 2023, focusing on demographic, geospatial, and temporal distributions. Network analysis of the recorded comorbidities was used to identify phenotypic clusters among patients. Results: Females and older adults were disproportionately affected. Several triggers and geospatial patterns were identified. Five phenotypic clusters were determined: two in the T2-high endotype, two in T2-low, and a mixed one. Conclusions: The findings highlight the need for personalized asthma management strategies and public healthcare interventions in Timiș County, addressing specific demographic and geospatial factors. This study also provides a valuable reference for similar regions.

Background/Objectives: Obstructive sleep apnea (OSA) is a highly prevalent disorder that significantly impacts quality of life and daily functioning. While continuous positive airway pressure (CPAP) therapy is effective, long-term adherence remains a challenge. This single-arm observational study aimed to evaluate clinical outcomes and adherence patterns during telemedicine-supported CPAP therapy and identify distinct phenotypic response clusters in Romanian patients with OSA. Methods: This prospective observational study included 86 adults diagnosed with OSA, treated with ResMed Auto CPAP devices at “Victor Babeș” University Hospital in Timișoara, Romania. All patients were remotely monitored via the AirView™ platform and received monthly telephone interventions to promote adherence when necessary. Clinical outcomes were assessed through objective telemonitoring data. K-means clustering and t-distributed stochastic neighbor embedding (t-SNE) were employed to explore phenotypic response patterns. Results: During telemedicine-supported CPAP therapy, significant clinical improvements were observed. The apnea–hypopnea index (AHI) decreased from 42.0 ± 21.1 to 1.9 ± 1.3 events/hour. CPAP adherence improved from 75.5% to 90.5% over six months. Average daily usage increased from 348.4 ± 85.8 to 384.2 ± 65.2 min. However, post hoc analysis revealed significant concerns about the validity of self-reported psychological improvements. Self-esteem changes showed negligible correlation with objective clinical measures (r < 0.2, all p > 0.1), with only 3.3% of variance being explained by measurable therapeutic factors (R² = 0.033). Clustering analysis identified four distinct adherence and outcome profiles, yet paradoxically, patients with lower adherence showed greater self-esteem improvements, contradicting therapeutic causation. Conclusions: Telemedicine-supported CPAP therapy with structured monthly interventions was associated with substantial clinical improvements, including excellent AHI reduction (22-fold) and high adherence rates (+15% after 6 months). Data-driven phenotyping successfully identified distinct patient response profiles, supporting personalized management approaches. However, the single-arm design prevents definitive attribution of improvements to telemonitoring versus natural adaptation or placebo effects. Self-reported psychological outcomes showed concerning patterns suggesting predominant placebo responses rather than therapeutic benefits. While the overall findings demonstrate the potential value of structured telemonitoring for objective CPAP outcomes, controlled trials are essential to establishing true therapeutic efficacy and distinguishing intervention effects from measurement bias.