BMC Pulmonary Medicine最新文献

Background: Oxygen uptake (V'_O2) obtained from expiratory gas analysis is generally calculated using minute ventilation (V'_E) and the inspired‒expired mean oxygen (O₂) concentration difference (ΔF_O2) during cardiopulmonary exercise testing (CPET). We have reported that ΔF_O2, which is associated with ventilatory efficiency, is independent of V'_E at peak exercise and affects exercise tolerance in respiratory diseases other than idiopathic pulmonary fibrosis (IPF). We hypothesized that similar results are obtained in IPF, and that ΔF_O2 is a prognostic factor for survival in IPF.

Methods: Forty-three patients with IPF, who underwent CPET with blood gas analysis were enrolled from our database.

Results: At peak exercise, ΔF_O2 was strongly correlated with variables related to ventilatory efficiency, i.e., V'_E/carbon dioxide output (V'_CO2) ratio at the nadir during exercise (r=‒0.91) and correlated well with peak V'_O2 (r = 0.67), but it was independent of V'_E (r = 0.24) at peak exercise. Two multivariate Cox proportional hazards models with adjustment for age, including the previously reported prognostic factors, showed that ΔF_O2 at peak exercise was a stronger predictor of survival than (1) peak V'_O2, V'_E at peak exercise in a first analysis (hazard ratio: 0.195, 95% CI 0.070 to 0.500; p = 0.0005) and (2) than tidal volume at peak exercise, body mass index, and arterial oxygen tension (PaO₂)-slope, i.e., the decrease in PaO₂ per the increase in V'_O2 during exercise in a second analysis (hazard ratio: 0.437, 95% CI 0.201 to 0.958; p = 0.0389).

Conclusions: These results show that ∆F_O2 at peak exercise, which is correlated with ventilatory efficiency related to carbon dioxide clearance, is independent of ventilatory ability and is a stronger prognostic factor for survival than physiological ventilatory impairments with hypoxemia in IPF. CPET is essential for evaluating exercise alveolar O₂ extraction and guiding the optimal management of patients with IPF.

Background: Blood eosinophils are recommended by GOLD as a biomarker for inhaled corticosteroid (ICS) therapy in COPD, yet their stability during acute exacerbations of COPD (AECOPD) remains uncertain.

Methods: We retrospectively analyzed 3,311 hospitalized AECOPD patients (2018-2023). Blood eosinophil distribution, longitudinal trends, and within-patient variability were assessed. Linear mixed-effects models estimated overall trajectories of ln(1 + EOS) over standardized hospital days (SHD). Variability was quantified by coefficient of variation (CV%), within-patient SD (wSD), and median absolute change between consecutive tests. Robustness of thresholds (100 and 300 cells/µL) was examined by crossing and reclassification rates, with simulations of 20-30% overall decline. Near-threshold "decision bands" were derived from the empirical median change (≈ 50 cells/µL).

Results: Blood eosinophils showed a right-skewed distribution with a median of 100 cells/µL (IQR 20-200). Mixed-effects models indicated only a mild overall increase (+ 2.16% per + 1 SD in SHD). In patients with repeated tests (n = 1,548), within-patient variability was substantial (median CV% 76.6%; wSD 57 cells/µL; median |Δ| 50 cells/µL). Threshold crossing and first-last reclassification were frequent (≥ 100: 45.7% and 32.9%; ≥300: 22.7% and 16.0%), and declined only slightly in simulated steroid-reduction scenarios. Approximately 13% of patients fluctuated between < 100 and ≥ 300 cells/µL during one admission.

Conclusion: EOS should be interpreted dynamically, with retesting or trend assessment recommended to guide clinical decisions during acute exacerbations.

Background: The Controlling Nutritional Status (CONUT) score has been proposed as a simple tool for assessing nutritional and immunological status and has been associated with prognosis in various malignancies. This study aimed to assess the relationship between pre-transplant CONUT scores and pneumonia development within one year after allogeneic stem cell transplantation (allo-SCT) in patients with acute leukemia.

Methods: In this retrospective single-center study, 158 patients who underwent allo-SCT for acute leukemia between 2013 and 2023 were included. Patients who developed pneumonia during their 1-year post-transplant follow-up were evaluated and their nutritional status was calculated using the CONUT score. The CONUT score was calculated from serum albumin, total cholesterol, and total lymphocyte count measured prior to transplantation. Pneumonia diagnosis was based on thoracic CT findings consistent with clinical symptoms (new infiltrate or consolidation), with microbiological confirmation when available.

Results: Pneumonia occurred in 40 patients (25.3%) within one year after transplantation. The one-year mortality rate was 42.5% (n = 17) in patients with pneumonia compared to 22.9% (n = 27) in those without pneumonia. Although mortality was higher among patients with pneumonia, no statistically significant association was found between pre-transplant CONUT scores and pneumonia development.

Conclusion: To our knowledge, this is the first study to examine the association between pre-transplant CONUT scores and pneumonia risk after allo-SCT in acute leukemia patients. Our findings indicate no significant predictive value. The multifactorial etiology of pneumonia and the dynamic nutritional and immunological changes in this high-risk population may limit the usefulness of static nutritional indices such as the CONUT score in predicting infectious complications. Incorporating supplemental nutritional or immunological markers may improve risk assessment in this population.

Objectives: This study investigated the relationship between weight-derived markers and in-hospital mortality in patients with Corona Virus Disease 2019 (COVID-19).

Methods: Various body composition including Weight, Body Mass Index (BMI), Body Fat Percentage (BFP), Whole-Body Fat Mass (WBFM), Lean Body Mass (LBM), and Basal Metabolic Rate (BMR) were calculated based on height, weight, gender, and age. In-hospital mortality served as the primary clinical outcome. The associations between these indicators and patient prognosis were evaluated using a crude model, a logistic Model adjusted for confounders, and a Propensity Score Matching (PSM) model. The corresponding 95% confidence intervals (95% CI) and odds ratio (OR) values were calculated. Additionally, four machine-learning predictive models (Decision Tree Classifier, Random Forest, Gaussian Naive Bayes, Gradient Boosting Classifier) were developed to assess the clinical utility of weight-derived markers.

Results: A total of 509 patients with COVID-19 were included in the study. Among the weight-derived markers, only BMI consistently demonstrated a protective effect against in-hospital mortality (crude model: OR (95% CI) = 0.84 (0.77-0.92); adjusted model 1: OR (95% CI) = 0.84 (0.77-0.92); PSM: OR (95% CI) = 0.85 (0.74-0.97), all P < 0.05). Restricted Cubic Spline regression indicated significant nonlinear associations between BMI, Weight, LBM, and WBFM with in-hospital mortality (P for overall < 0.05). Conversely, no significant nonlinear associations were observed between BFP, BMR, and in-hospital mortality. The BMI-based Random Forest prediction model effectively forecasted in-hospital mortality (ROC (95% CI) = 0.84 (0.76-0.92)).

Conclusions: Higher BMI was associated with reduced in-hospital mortality in patients with COVID-19, with the BMI-based predictive model demonstrating strong predictive capabilities.

Background: Early identification of lung fibrosis remains difficult. In Japan, the serum biomarkers surfactant protein-D (SP-D) and KL-6 are commonly used to monitor interstitial lung diseases (ILD) in clinical practice, but their potential role in the early detection of lung fibrosis has not yet been fully clarified. Although chest radiography is also considered a possible tool for identifying subclinical pulmonary fibrosis, detecting early-stage disease remains challenging. A deep learning-based software, BMAX, was recently developed to identify fibrosing ILD on chest radiographs. Its capability to detect lung fibrosis in a health-checkup setting requires validation.

Methods: Study participants were randomly recruited from individuals undergoing routine health examinations. All participants underwent chest radiography and serum SP-D and KL-6 testing. Those with elevated biomarker levels (≥ 110 ng/mL for SP-D and ≥ 500 IU/mL for KL-6) or radiographic abnormalities were advised to undergo further evaluation with chest computed tomography (CT). Lung fibrosis on CT was assessed independently by one pulmonologist and one thoracic radiologist. BMAX assigned a confidence score for lung fibrosis (ranging from 0 to 1) on each radiograph. In participants who underwent CT, the sensitivity and specificity of BMAX (using a confidence score > 0.3 as the threshold), SP-D, and KL-6 for detecting lung fibrosis were evaluated.

Results: Among the 2,751 individuals enrolled, 228 were recommended for CT, and 81 underwent the scan. Lung fibrosis was identified on chest CT in 8 of the 81 participants. The positivity rates for SP-D, KL-6, and BMAX (confidence score > 0.3) were 5.9%, 2.4%, and 5.9%, respectively. SP-D showed a sensitivity of 1.000 and a specificity of 0.315, while KL-6 showed a sensitivity of 0.750 and a specificity of 0.753. BMAX demonstrated a sensitivity of 1.000 and a specificity of 0.904.

Conclusions: SP-D and KL-6 may be useful screening biomarkers for lung fibrosis in health checkup settings, offering high sensitivity and moderate positivity rates. BMAX also appears promising as a standalone screening tool for detecting lung fibrosis on chest radiographs.

Background: The Albumin-Bilirubin (ALBI) grade was initially used to assess liver reserve function in patients with cirrhosis and has since been applied in the prognostic evaluation of various diseases. This study explored the relationship between the ALBI grade and the prognosis of patients with pulmonary edema (PE).

Methods: We conducted a retrospective analysis with 1,562 ICU patients with pulmonary edema from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. This study investigated the correlation between the ALBI grade and prognosis in patients diagnosed with acute or chronic pulmonary edema at different stages of the disease. Kaplan-Meier survival analysis, multivariate Cox regression, and ROC curves were used to evaluate the prognostic value of ALBI. Kaplan‒Meier survival curves and multivariate Cox proportional hazards regression models assessed the associations between ALBI grade and short-term and long-term prognoses. Receiver operating characteristic (ROC) analysis was used to evaluate the prognostic prediction ability, sensitivity, specificity, and area under the curve (AUC) of short-term and long-term ALBI grades of PE. Subgroup analyses were also conducted. The Boruta algorithm was used to assess the predictive ability of ALBI, and a predictive model was built using machine learning algorithms. Machine learning algorithms including Gradient Boosting Machine (GBM), Random Survival Forest (RSF), Lasso Cox Regression (Lasso_Cox), etc., were employed to build predictive models.

Results: This study included a cohort of 1,562 participants. Cox proportional hazards models revealed independent associations between ALBI grade and 30-, 90-, 180-day, and 1-year outcomes in PE patients before and after confounder adjustment. The survival curves indicated that patients with an ALBI grade of 3 (-1.39 to 3) had lower survival rates at 30, 90, and 180 days and 1 year. High ALBI grade (Grade 3) was independently associated with increased mortality at 30 days (HR = 47.4, 95% CI: 6.55-344), 90 days (HR = 8.56, 95% CI: 4.11-17.8), and 1 year (HR = 8.01, 95% CI: 4.58-14.0). The AUC values for ALBI in predicting 30-day and 1-year mortality were 0.741 and 0.700, respectively. Subgroup analysis revealed no significant interactions between ALBI grade and any subgroup. Elevated ALBI levels were associated with increased short-term and long-term all-cause mortality (ACM) in PE patients, suggesting that the ALBI grade may be an independent prognostic factor across disease stages.

Conclusion: The ALBI grade is significantly associated with ACM and prognosis in PE patients, which provides a scientific basis for the development of precise treatment strategies.

Background: Chronic cough (CC), defined as a cough persisting for over eight weeks, presents diagnostic and management challenges in primary care, particularly in tuberculosis (TB)-endemic countries like Malaysia. This study aimed to assess the knowledge, practices and diagnostic barriers of primary care doctors in Klang Valley regarding CC.

Methods: A cross-sectional online survey was conducted from October to December 2024 involving 170 primary care physicians from public and private clinics. The questionnaire covered sociodemographic, diagnostic tools, clinical strategies, perceived barriers, and training needs. Descriptive and inferential statistics, including regression analysis, were used to identify patterns and associations.

Findings: Only 18·8% of participants correctly defined CC as lasting more than 8 weeks. Most emphasized environmental and occupational history-taking but underutilized spirometry (available to only 33·5% of clinics). While chest radiography was widely available (61·2%), high patient load was significantly associated with reduced medical history-taking and chest X-ray ordering (p = 0·011). Empirical treatments, including antihistamines and bronchodilators, were commonly used. Barriers included limited diagnostic tools (53·7%), unclear referral pathways (12·8%), lack of standardized guidelines (8·7%), and time constraints (16·8%).

Interpretation: Primary care doctors in Malaysia face significant limitations in CC management, particularly in defining, diagnosing, and accessing diagnostic tools. There is an urgent need for national guidelines tailored to primary care, broader access to spirometry, and structured training. Addressing these issues can improve diagnostic accuracy and reduce unnecessary investigations and treatment delays in chronic cough care.

Background & aims: Sarcopenia has emerged as a significant prognostic factor for lung cancer. However, it remains unclear how the lateral pterygoid muscle index (LPMI) calculated based on the lateral pterygoid muscle area correlates with sarcopenia in lung cancer patients with brain metastasis (BM). This study aims to determine whether LPMI can serve as a predictor for sarcopenia in lung cancer patients with BM and assess its utility in predicting survival.

Methods: This study retrospectively analyzed the initial brain magnetic resonance imaging (MRI) parameters of non-small cell lung cancer (NSCLC) patients with BM at our hospital between March 2017 and June 2024. We examined the correlation between LPMI and the lumbar skeletal muscle index (LSMI) at the third lumbar vertebra (L3). After adjusting for key prognostic factors, we performed a multivariate Cox regression analysis of overall survival (OS) to evaluate the prognostic significance of LPMI.

Results: A total of 223 patients were included. The LPMI was positively correlated with LSMI in NSCLC patients with BM (r = 0.618, p < 0.001). Multivariate linear regression analysis indicated LPMI was associated with the risk of sarcopenia (β = 0.427,p < 0.001). The optimal cut-off value of LPMI for identifying NSCLC with sarcopenia was 120.06. Multivariate logistic regression showed an increased blood urea nitrogen/creatinine ratio (BUN/Cr) was associated with lower LPMI values (OR = 1.014, p = 0.013). Patients with an LPMI thicker than the median had a longer OS compared to those with an LPMI thinner than the median (9 months vs. 14.5 months, p = 0.011). Multivariate Cox analysis showed a thicker LPMI measurement was a predictor of longer OS (HR = 0.993,p = 0.036).

Conclusions: Our results indicate that LPMI has the potential to serve as an alternative diagnostic indicator for sarcopenia in NSCLC patients with BM and as an independent predictor of OS.

Background: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally, with smoking as its primary risk factor. The relationship between menopausal age and COPD remains unknown. This study examines this potential association.

Methods: Using NHANES 1999-2018 data, we analyzed associations between menopausal age and COPD via weighted logistic regression, weighted linear regression, restricted cubic spline (RCS) analysis, and explored the potential mediating role of smoking.

Results: This study included a total of 8,731 participants. Every year increase in menopausal age was associated with a 1.2% reduction in COPD risk after completely adjusting the Model (odds ratio [OR] = 0.988, 95% confidence interval [CI]: 0.979-0.998, P < 0.001). After grouping participants based on tertiles of menopausal age, COPD risks decreased by 19.0% in the T2 (OR = 0.810, 95% CI: 0.662-0.991, P = 0.040) and 21.4% in the T3 (OR = 0.786, 95% CI: 0.644-0.958, P = 0.018) relative to T1, respectively. RCS regression uncovered a linear association between menopausal age and COPD risk (P-overall < 0.001, P-nonlinear = 0.080). Subgroup analysis demonstrated that menopausal age was inversely related to COPD risk in general populations. Mediation analysis suggested that smoking partially mediated (38.0%) the link between menopausal age and COPD.

Conclusion: Later age at menopause is significantly associated with a reduced risk of COPD, and smoking may partially explain this association. These findings suggest the potential value of targeted screening and highlight the importance of smoking cessation interventions in this population.