Advances in respiratory medicine最新文献

Pulmonary veno-occlusive disease (PVOD) is a rare and under-recognized cause of pulmonary hypertension. It is characterized by fibrotic obstruction of small pulmonary veins and venules. Its clinical presentation closely mimics pulmonary arterial hypertension (PAH), leading to frequent misdiagnosis, delayed recognition, and potentially harmful exposure to PAH-specific vasodilator therapy. This review aims to synthesize our evolving understanding of PVOD, discussing its etiologies, role of genetic underpinnings, histopathologic features, pathophysiology, clinical presentation, and characteristic imaging findings. It then discusses management strategies emphasizing early recognition, supportive care, avoidance of inappropriate PAH therapies due to poor response, and timely referral for lung transplantation. Despite advances in identification and management, PVOD remains a fatal condition with a median survival of less than two years, underscoring the importance of early recognition and multidisciplinary care.

Systemic sclerosis-associated pulmonary arterial hypertension (SSc-PAH) is a life-threatening vascular complication of SSc, marked by high morbidity and mortality. Early diagnosis remains a major challenge due to nonspecific symptoms and the limitations of conventional tools such as echocardiography (ECHO), pulmonary function tests (PFTs), and serum biomarkers. This review evaluates the emerging role of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in improving the diagnostic landscape of SSc-PAH. A comprehensive literature search was conducted across PubMed, Scopus, IEEE Xplore, Embase and Google Scholar to identify studies involving AI applications in SSc, pulmonary arterial hypertension (PAH), and their intersection. Evidence indicates that AI models can assist interpretation across modalities, including heart sounds, ECGs, chest X-rays (CXRs), ECHOs, CT pulmonary angiography (CTPA), and omics-based biomarkers. While several models show encouraging diagnostic performance, their accuracy varies by dataset and modality, and most require external validation against right heart catheterization (RHC)-confirmed cohorts. Integrating multimodal data through AI frameworks may enhance early recognition and individualized risk stratification; however, these tools remain exploratory. Future work should emphasize harmonized hemodynamic definitions, transparent validation protocols, and SSc-specific datasets to ensure clinical applicability and reproducibility.

Obstructive sleep apnea (OSA) is associated with increased risks of systemic comorbidities, leading to significant morbidity and mortality. This study investigates predictors of all-cause mortality, emphasizing the interplay of clinical symptoms, polysomnographic findings, and comorbidities. The aim of this study was to identify and compare respiratory predictors of all-cause mortality over 5, 10, and 15 years. A single-center study was conducted at a Sleep Medicine Department between 2005 and 2019, 4025 patients with suspected OSA who underwent polysomnography were admitted, 853 died during the study. We performed Cox regression analyses with dynamic hazard ratios to evaluated predictors of mortality. Prevalence of OSA was high-75.6% in the cohort: 929 patients with mild OSA (23.1%), 770 with moderate OSA (19.1%), and 1343 with severe OSA (33.4%). Survival rates were 89.7%, 81.9%, and 78.8% at 5, 10, and 15 years, respectively. Cardiovascular causes dominated mortality (33.3%), followed by cancer (26.5%). AHI_REM was associated with higher mortality risk in 0-5, 0-10, 0-15 years of observation in contrast to AHIN_REM and AHI_TST. The hazard ratio analysis showed that mortality risk changed over time depending on sleep stage and event type: risk increased for AHI_REM and AHI_TST, while it stayed the same or decreased for AHI_NREM and most central apneas.

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Background: The core management of most individuals with asthma and COPD is daily treatment with inhalers such as inhaled corticosteroids (ICS) and long-acting bronchodilators. The two main types of inhalers used are pressurized metered-dose inhalers (pMDIs) and dry powder inhalers (DPIs). Different studies have shown low adherence to inhaler treatments among subjects with asthma and COPD. In this study, we explored the differences in adherence between pMDIs and DPIs of combined ICS and long-acting β₂-agonist inhalers (ICS + LABA) in a large cohort, free from commercial biases. Methods: In this historical prospective study, we included all adult subjects with asthma and/or COPD who acquired at least one ICS + LABA inhaler between 2016 and 2019. We carried out propensity score matching and then compared the maximal number of pMDIs and DPIs purchased in any continuous 12 months during the study period. We also compared once-a-day DPIs with twice-a-day DPIs. Results: Of the 36,998 matched subjects, 5897 (15.9%) purchased pMDIs. The overall median [IQR] inhalers purchased for pMDIs and DPIs were 1 [1, 4] and 3 [1, 8], respectively; for subjects with asthma, 1 [1, 3] and 2 [1, 6]; for subjects with COPD, 1 [1, 3] and 3 [1, 10]; and for subjects with asthma-COPD overlap, 2 [1, 7] and 6 [2, 12]. For all the comparisons, p < 0.001. The once-a-day DPI group had a slight but significantly better adherence than the twice-a-day DPI group. Conclusions: For ICS + LABA therapy, the number of DPIs purchased was significantly greater than the number of pMDIs purchased, as well as the once-a-day DPI relative to the other DPIs. Overall, subjects with asthma and/or COPD had low adherence to all inhalers, with the highest adherence observed among subjects with asthma-COPD overlap.

COVID-19 signs and symptoms varied among patients, with the most common being fever, fatigue, sore throat, cough, anorexia, and shortness of breath. (1) Background: This study aimed to assess effort, dyspnea, and cooperation scores in patients with mild and moderate post-COVID-19 forms, both at baseline and after completing a structured physical recovery program. (2) Methods: Our study included 160 post-COVID-19 patients who had experienced mild or moderate disease. (3) Results: Effort and dyspnea scores were significantly lower (p < 0.01), while cooperation scores were significantly higher after the rehabilitation program. Both men and women demonstrated significant increases in cooperation scores after recovery. Additionally, both groups showed statistically significant reductions in effort and dyspnea scores (p < 0.001). Among patients aged under and over 60 years, effort and dyspnea scores decreased after rehabilitation, and cooperation scores increased significantly (p < 0.001). No statistically significant differences were observed between genders in any of the three scores. Similarly, no significant differences by age were found in cooperation or dyspnea scores. A significant negative correlation was observed between cooperation and effort scores: patients with higher cooperation scores tended to report lower effort scores, and vice versa (p < 0.001, R = -0.571). (4) Conclusions: The improved cooperation demonstrated by patients during the physical recovery program was significantly associated with reductions in perceived effort and dyspnea, indicating a positive impact on post-COVID-19 rehabilitation outcomes.

Background and objectives: Hematological parameters are increasingly being investigated as readily accessible biomarkers for the diagnosis of obstructive sleep apnea syndrome (OSAS). In our study, we aimed to investigate the relationship between OSAS and albumin indices and the uric acid-to-HDL ratio (UHR).

Methods: The demographic and laboratory data and AHI (apnea-hypopnea index) values of 613 patients who underwent polysomnography were obtained retrospectively from their files. Blood parameters such as white blood cells (WBCs), red blood cell distribution width (RDW), red blood cells (RBCs), hemoglobin (Hb), hematocrit (Hct), platelets (PLTs), C-reactive protein (CRP), albumin, blood urea nitrogen (BUN), and high-density lipoproteins (HDLs) were obtained from the files. Laboratory indices such as the BUN-to-albumin ratio (BAR), neutrophil-to-albumin ratio (NAR), RDW-to-albumin ratio (RAR), CRP-to-albumin ratio (CAR), and UHR were calculated. OSAS was categorized as simple snoring (SS) (control) (AHI < 5), mild (5 ≤ AHI < 15), moderate (15 ≤ AHI < 30), and severe (AHI ≥ 30). The patients were also grouped as severe (AHI ≥ 30) and non-severe (5 > AHI < 30) OSAS and compared in terms of laboratory parameters and indices.

Results: Of the 613 participants, 366 (59.7%) were men, and the average age of participants was 55.22 ± 11.13 years. The biomarkers such as RBCs, Hb, Htc, CRP, BUN, creatinine, uric acid, HDLs, CAR, RAR, BAR, and UHR showed significant differences between OSAS patients and controls. WBCs, basophils, RBCs, RDW, Htc, PLTs, HDLs, uric acid, RAR, NAR, and UHR indices were significantly different between the severe OSAS and non-severe OSAS groups (p < 0.05). BAR (OR = 1.151; CI = 1.056 - 1.256; p = 0.001) and UHR (OR = 2.257; 95% CI = 1.507 - 3.382; p < 0.001) were the most important indices predicting OSAS, while RAR (OR = 1.844; CI = 1.224 - 2.778; p = 0.003) and UHR (OR = 2.203; 95% CI = 1.496 - 3.243; p < 0.001) were the strongest indices associated with severe OSAS.

Conclusion: In our study, RAR, BAR, and UHR indices were closely associated with the presence and severity of OSAS. These indices can be considered low-cost, readily available methods for predicting OSAS patients.

Background: Pulmonary hypertension (PH) is a frequent complication in patients with interstitial lung disease (ILD); its occurrence results in significant morbidity and mortality. Currently approved treatment options for PH-ILD include inhaled prostacyclin therapy, although this approach may be insufficient in patients who have developed simultaneous right ventricular failure. Moreover, there is no available treatment algorithm regarding the optimal therapy and timing of lung transplant referral for PH-ILD patients based on disease severity.

Design/methods: In this study, we created such a tool to guide PH-specific therapy in PH-ILD patients, especially as further treatment strategies are developed. We developed a 4-point PH-ILD Severity score that integrated both subjective and objective information (WHO FC, CI, TAPSE, PVR) from retrospective analysis of 57 PH-ILD patients.

Results: A score of 3 or greater in the PH-ILD Severity score yielded an AUC of 0.831 (p < 0.001) for the composite endpoint of clinical worsening (hospitalization due to a cardiopulmonary indication; decrease in 6 min walk distance by >15% at 2 consecutive visits; all-cause mortality; lung transplantation).

Conclusions: Further confirmation and evolution of this PH-ILD Severity score will assist in the development of optimal treatment plans in ILD patients diagnosed with concomitant PH.

Background: Uncontrolled asthma remains a significant clinical challenge, often linked to impaired lung function and increased diaphragmatic workload. Recent studies have shown promising results using a triple inhaled therapy comprising beclomethasone dipropionate/formoterol fumarate/glycopyrronium (BDP/FF/G). This study assessed the real-world efficacy of BDP/FF/G on lung function and diaphragmatic workload in patients with uncontrolled asthma. Methods: A prospective observational study enrolled 21 adult patients diagnosed with uncontrolled asthma despite high-dose ICS/LABA therapy. Patients underwent lung function tests and right diaphragmatic ultrasound assessments at baseline and after three months of treatment with BDP/FF/G (172/5/9 mcg, administered as two inhalations every 12 h). Results: After three months, significant improvements were observed in FEV₁ (from 57.75 ± 12.30% to 75.10 ± 18.94%, p < 0.001) and FEF_25-75 (from 47.80 ± 19.23% to 75.10 ± 36.06%, p < 0.001). Additionally, during the same period, we recorded significant reductions in residual volume (from 130.10 ± 28.20% to 92.55 ± 21.18%, p < 0.001) and total airway resistance (R_tot) (from 164.60 ± 83.21% to 140.70 ± 83.25%, p < 0.05). The mean asthma control test (ACT) score increased by 5.6 points (p < 0.001), surpassing the established minimal clinically important difference (MCID) of 3 points and raising the cohort mean above the well-controlled threshold. The right diaphragmatic workload was significantly decreased, as shown by a reduction in thickening fraction (TF) (from 63.86 ± 17.67% to 40.29 ± 16.65%, p < 0.01). Correlation analysis indicated significant associations between diaphragmatic function and some lung function parameters (FEV₁, FEF_25-75, and R_tot). Conclusions: In this real-world pilot, triple BDP/FF/G was linked to improvements in airflow, hyperinflation, symptoms, and a reduction in diaphragmatic thickening fraction, indicating potential physiological benefit. Due to the small sample size, single-centre design, and 3-month follow-up, these results should be viewed as hypothesis-generating and need to be confirmed in larger, controlled, multicentre studies with longer follow-up.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Accurate determination of epidermal growth factor receptor (EGFR) mutation status is essential for selecting patients eligible for tyrosine kinase inhibitors (TKIs). However, invasive genotyping is often limited by tissue accessibility and sample quality. This study presents a non-invasive machine learning model combining clinical data, CT morphological features, and radiomic descriptors to predict EGFR mutation status. A retrospective cohort of 138 patients with confirmed EGFR status and pre-treatment CT scans was analyzed. Radiomic features were extracted with PyRadiomics, and feature selection applied mutual information, Spearman correlation, and wrapper-based methods. Five Random Forest models were trained with different feature sets. The best-performing model, based on 11 selected variables, achieved an AUC of 0.91 (95% CI: 0.81-1.00) under stratified five-fold cross-validation, with an accuracy of 0.88 ± 0.03. Subgroup analysis showed that EGFR-WT had a performance of precision 0.93 ± 0.04, recall 0.92 ± 0.03, F1-score 0.91 ± 0.02, and EGFR-Mutant had a performance of precision 0.76 ± 0.05, recall 0.71 ± 0.05, F1-score 0.68 ± 0.04. SHapley Additive exPlanations (SHAP) analysis identified tobacco use, enhancement pattern, and gray-level-zone entropy as key predictors. Decision curve analysis confirmed clinical utility, supporting its role as a non-invasive tool for EGFR-screening.