BMC Pulmonary Medicine最新文献

Background: The prognosis of non-small cell lung cancer (NSCLC) is substantially affected by lymph node metastasis (LNM), but there are no noninvasive, inexpensive methods of relatively high accuracy available to predict LNM in NSCLC patients.

Methods: Clinical data on NSCLC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for LNM were recognized LASSO and multivariate logistic regression. Six predictive models were constructed with machine learning based on risk factors. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Subgroup analysis with different T-stages was performed on an optimal model. A webpage LNM risk calculator for optimal model was built using the Shinyapps.io platform.

Results: We enrolled 64,012 NSCLC patients, of whom 26,611 (41.57%) had LNM. Using multivariate logistic regression, we finally identified 10 independent risk factors for LNM: age, sex, race, histology, primary site, grade, T stage, M stage, tumor size, and bone metastases. GLM is the optimal model among all six machine learning models in both the training and validation cohorts. Subgroup analyses revealed that GLM has good predictability for populations with different T staging. A webpage LNM risk calculator based on GLM was posted on the shinyapps.io platform ( https://wubopredict.shinyapps.io/dynnomapp/ ).

Conclusion: The predictive model based on GLM can be used to precisely predict the probability of LNM in NSCLC patients, which was proven effective in all subgroup analyses according to T staging.

Background: This cost of illness study aimed to determine economic burden of short-acting β2-agonist (SABA) overuse in Türkiye from payer perspective with respect to the updated GINA 2022 treatment recommendations.

Methods: A total of 3,034,879 asthma patients comprised the study population, via estimations extrapolated from the Türkiye arm of the global SABINA III study. The economic burden (costs related to the drug use and severe exacerbations) was compared in subgroups of overall (≥ 0 canisters/year) vs. GINA-recommended (0-2 canisters/year, hypothetical population) SABA use and in subgroups of appropriate use (0-2 canisters/year, real population) vs. overuse (≥ 3 canisters/year) of SABA with extrapolation of SABINA Türkiye data to the Türkiye asthma population.

Results: Recommended SABA use was predicted to prevent 127,505 of 157,512 severe exacerbations per year in mild asthma patients and 2,668,916 of 3,262,800 severe exacerbations per year in moderate-severe asthma patients. Annual cost burden of not applying recommended SABA use (overall [≥ 0 canisters/year] vs. GINA-recommended [0-2 canisters/year] SABA use) in mild asthma and moderate-severe asthma patients was calculated to be €20.43 million and €427.65 million in terms of severe exacerbations, and to be €829,352 and €7.20 million in terms of drug costs, respectively. The total annual economic burden arising from not applying recommended SABA use was estimated to be €456.11 million. Appropriate use (0-2 canisters/year) vs. overuse (≥ 3 canisters/year) of SABA was associated with decreased frequency of severe exacerbations per year in mild asthma (from 129,878 to 27,634) and moderate-severe asthma (from 2,834,611 to 428,189) patients. SABA overuse in mild and moderate-severe asthma patients was estimated to yield an additional annual cost of €16.38 million and €385.59 million, respectively in terms of severe exacerbations, and a total €11.30 million additional drug cost. The overall annual economic burden arising from SABA overuse was estimated to be €413.27 million.

Conclusions: The estimated annual total economic burden arising from not applying recommended SABA use (€456.11 million) and SABA overuse (€413.27 million) with respect to the updated GINA 2022 treatment recommendations indicates the substantial cost burden of SABA overuse to the Turkish National Health System, corresponding up to 26% of the total direct cost of asthma reported in our country.

Background: The quantitative analysis of computed tomography (CT) and Krebs von den Lungen-6 (KL-6) serum level has gained importance in the diagnosis, monitoring, and prognostication of interstitial lung disease (ILD). However, the associations between quantitative analysis of CT and serum KL-6 level remain poorly understood.

Methods: In this retrospective observational study conducted at tertiary hospital between June 2020 and March 2022, quantitative analysis of CT was performed using the deep learning-based method including reticulation, ground glass opacity (GGO), honeycombing, and consolidation. We investigated the associations between CT-based phenotypes and serum KL-6 measured within three months of the CT scan. Furthermore, we evaluated the performance of the combined CT-based phenotypes and KL-6 levels in predicting hospitalizations due to respiratory reasons of ILD patients.

Results: A total of 131 ILD patients (104 males) with a median age of 67 years were included in this study. Reticulation, GGO, honeycombing, and consolidation extents showed a positive correlation with KL-6 levels. [Reticulation, correlation coefficient (r) = 0.567, p < 0.001; GGO, r = 0.355, p < 0.001; honeycombing, r = 0.174, p = 0.046; and consolidation, r = 0.446, p < 0.001]. Additionally, the area under the ROC of the combined reticulation and KL-6 for hospitalizations due to respiratory reasons was 0.810 (p < 0.001).

Conclusions: Quantitative analysis of CT features and serum KL-6 levels ascertained a positive correlation between the two. In addition, the combination of reticulation and KL-6 shows potential for predicting hospitalizations of ILD patients due to respiratory causes. The combination of reticulation, focusing on phenotypic change in lung parenchyma, and KL-6, as an indicator of lung injury extent, could be helpful for monitoring and predicting the prognosis of various types of ILD.

Background: Patients with spinal cord injury (SCI) are at higher risk of developing pulmonary infection (PI), and plasma fibrinogen level may be an independent risk factor for PI. However, the relationship between fibrinogen level and PI incidence in the SCI population remains unclear. This study aimed to elucidate the association between plasma fibrinogen level and the occurrence of PI among SCI patients.

Methods: We conducted a retrospective analysis of 576 SCI patients admitted to the Rehabilitation Medicine Department between January 1, 2017, and December 31, 2021. Following exclusions, 491 patients were included in the final analysis, with 139 PI cases identified.

Results: Surgery, level of injury and chest comorbidities were covariates in the relationship between fibrinogen level and PI incidence. Other identified potential risk factors for PI included age, D-dimer level, urinary tract infections (UTI), deep vein thrombosis (DVT), anticoagulant therapy, injury mechanism, and the American Spinal Injury Association Impairment Scale (AIS) grades. After adjusting for these factors, we found that for every 1 g/L increase in fibrinogen level, the risk of developing PI increased by 18% (HR = 1.18, P = 0.011), and indicating a positive linear relationship between fibrinogen level and PI incidence.

Conclusion: Plasma fibrinogen was an independent risk factor for PI in patients with SCI, especially for AIS-B and C grades. Proactive management of fibrinogen level after admission to rehabilitation medicine department could be crucial in reducing the incidence of PI in this vulnerable population.

Clinical trial number: Not applicable.

Background: India, with the highest global burden of tuberculosis (TB) and drug-resistant TB, aims to eliminate TB by 2025. Yet, limited evidence exists on drug resistance patterns and retreatment among patients with silico-tuberculosis. This study explores these patterns and assesses the impact of silicosis on TB retreatment in India.

Methods: This secondary data analysis stems from a larger retrospective cohort study conducted in Khambhat, Gujarat, between January 2006 and February 2022. It included 138 patients with silico-tuberculosis and 2,610 TB patients without silicosis. Data from the Nikshay TB information portal were linked with silicosis diagnosis reports from the Pneumoconiosis Board using the unique Nikshay ID as the linking variable. Drug-resistant TB was defined as resistance to any anti-TB drug recorded in Nikshay. Retreatment refers to TB patients who have previously undergone anti-TB treatment for one month or more and need further treatment. Recurrent TB denotes patients who were previously declared cured or had completed treatment but later tested positive for microbiologically confirmed TB. Multivariable logistic regression was used to determine the impact of co-prevalent silicosis on drug resistance and retreatment.

Results: Patients with silico-tuberculosis showed a higher proportion of retreatment compared to those without silicosis (55% vs. 23%, p < 0.001). Notably, 28% of patients with silico-tuberculosis were recurrent TB cases, compared to 11% among those without silicosis. Regarding drug resistance, the silico-tuberculosis group exhibited a higher rate (6% vs. 3%), largely due to rifampicin resistance (5% vs. 2%, p = 0.022). Co-prevalent silicosis was associated with a 2.5 times greater risk of drug-resistant TB (adjusted OR 2.5, 95% CI, 1.1-5.3; p = 0.021). Additionally, patients with silico-tuberculosis had a fourfold increased risk of retreatment for TB (adjusted OR 4, 95% CI, 3-6; p < 0.001).

Conclusions: Co-prevalent silicosis significantly elevates the risk of drug resistance, recurrence, and retreatment among TB patients in India. This study indicates a need for improved treatment protocols and suggests that future research should focus on randomized controlled trials to evaluate appropriate anti-TB regimen and duration of therapy for this high-risk group. Given India's goal to eliminate TB by 2025, addressing the challenges posed by silico-tuberculosis is critical.

Background: Pseudomonas aeruginosa (PA) isolation in patients with chronic obstructive pulmonary disease (COPD) has been associated with a poor prognosis. This meta-analysis aimed to determine significant risk factors for PA isolation among patients with COPD.

Methods: A systematic literature retrieval from PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) was conducted, including studies from January 2003 to September 2024. Case-control and cohort studies exploring the risk factors for PA isolation in patients with COPD were included in this analysis. A random-effects model was applied to estimate the pooled adjusted odds ratio (paOR) or hazard ratio (paHR) with the corresponding 95% confidence intervals (CI).

Results: Thirteen eligible studies with a total of 25,802 participants were included in this meta-analysis. Prior systemic steroid therapy (paOR: 2.67; 95% CI: 1.29-5.53; P = 0.008), previous antibiotic treatment (paOR: 2.83; 95% CI: 1.14-6.97; P = 0.02), high "Body mass index, airflow Obstruction, Dyspnea, Exercise capacity" (BODE) index (paOR: 4.13; 95% CI: 1.67-10.23; P = 0.002), 6-min walking distance (6MWD) < 250 m (paOR: 4.27; 95% CI: 2.59-7.01; P < 0.001), COPD assessment test (CAT) score > 20 points (paOR: 2.49; 95% CI: 1.46-4.23; P = 0.001), hypoproteinemia (paOR: 2.62; 95%CI: 1.32-5.19; P = 0.006), hospitalizations in the previous year (paOR: 3.74; 95%CI: 1.22-11.49; P = 0.021), Bronchiectasis (paOR = 4.81; 95% CI: 3.66-6.33; P < 0.001) and prior PA isolation (paOR: 16.39; 95% CI: 7.65-35.10; P < 0.001) were associated with PA isolation in patients with COPD.

Conclusions: Our study identified nine risk factors associated with an increased risk of PA isolation in COPD patients. These findings are significant for the early identification of patients at risk for PA isolation, which might contribute to reducing mortality and improving clinical outcomes.

Background: In small cell lung cancer (SCLC), the pathological N category is identical to it in non-small cell lung cancer (NSCLC) and remains unchanged over a decade. Here we verified the discriminability of number of involved nodal stations (nS) in SCLC and compared its efficacy in predicting survival with currently used pathological nodal (pN) staging.

Methods: We retrospectively analyzed the patients who received operations and were pathologically diagnosed as SCLC at Shanghai Pulmonary Hospital between 2009 and 2019. X-tile software was adopted to determine optimal cut-off values for nS groups. Kaplan-Meier method and Cox regression analysis were used to compare survival between different groups. Decision curve analysis (DCA) was employed to evaluate the standardized net benefit.

Results: A total of 369 patients were included. The median number of sampled stations was 6 (range 3-11), and the median number of positive stations was 1 (range 0-7). The optimal cutoff for nS groups was: nS0 (no station involved), nS1-2 (one or two stations involved), and nS ≥ 3 (three or more stations involved). Overall survival (OS) and relapse-free survival (RFS) were statistically different among all adjacent categories within the nS classification (p < 0.001, for both OS and RFS between each two subgroups), but survival curves for subgroups in pN overlapped (OS, p = 0.067; RFS, p = 0.068, pN2 vs. pN1). After adjusting for other confounders, nS was a prognostic indicator for OS and RFS. The DCA revealed that nS had improved predictive capability than pN.

Conclusions: Our cohort study demonstrated that the nS might serve as a superior indicator to predict survival than pN in SCLC and was worth considering in the future definition of the N category.

Background: Osimertinib is an irreversible third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI). It is the preferred first-line treatment for EGFR-mutated non-small cell lung cancer (NSCLC) compared to first-generation EGFR-TKIs. However, limited research has compared its clinical effectiveness with second-generation (2^nd G) EGFR-TKIs.

Materials and methods: This study recruited patients diagnosed with stage IIIb-IV EGFR-mutated NSCLC who received first-line treatment with either 2^nd G EGFR-TKIs (afatinib and dacomitinib) or osimertinib between April 2020 and April 2023.

Results: The final analysis included 168 patients, of whom 113 received 2^nd G EGFR-TKIs (afatinib or dacomitinib) and 55 received osimertinib. The median progression-free survival (PFS) did not differ significantly between 2^nd G EGFR-TKIs and osimertinib (del 19: 17.6 months; L858R: 20.0 months vs. 28.3 months, p = 0.081). In patients with the EGFR exon 19 deletion, osimertinib conferred a longer median PFS (28.3 vs. 17.6 months, p = 0.118) and time to treatment failure (30.2 vs. 22.7 months, p = 0.722) than 2^nd G EGFR-TKIs. However, the differences were not statistically significant. In patients with with the EGFR exon 19 deletion and central nervous system metastasis, the median PFS did not differ significantly between those treated with osimertinib (14.3 months) and those treated with 2nd G EGFR-TKIs (17.6 months; p = 0.881). Multivariate regression analysis revealed that the NSCLC stage was the only independent negative predictor of PFS. The treatment patterns in the second line also differed significantly between groups (p = 0.008).

Conclusions: This study found comparable effectiveness between osimertinib and 2^nd G EGFR-TKIs as first-line treatment for advanced EGFR-mutated NSCLC, with only the NSCLC stage identified as a negative predictor of PFS. However, whether the different second-line treatments affect overall survival should be examined.

Background: Previous studies have shown that patients with pre-existing chronic obstructive pulmonary diseases (COPD) were more likely to be infected with coronavirus disease (COVID-19) and lead to more severe lung lesions. However, few studies have explored the severity and prognosis of COVID-19 patients with different phenotypes of COPD.

Purpose: The aim of this study is to investigate the value of the deep learning and radiomics features for the severity evaluation and the nucleic acid turning-negative time prediction in COVID-19 patients with COPD including two phenotypes of chronic bronchitis predominant patients and emphysema predominant patients.

Methods: A total of 281 patients were retrospectively collected from Hohhot First Hospital between October 2022 and January 2023. They were divided to three groups: COVID-19 group of 95 patients, COVID-19 with emphysema group of 94 patients, COVID-19 with chronic bronchitis group of 92 patients. All patients underwent chest computed tomography (CT) scans and recorded clinical data. The U-net model was pretrained to segment the pulmonary involvement area on CT images and the severity of pneumonia were evaluated by the percentage of pulmonary involvement volume to lung volume. The 107 radiomics features were extracted by pyradiomics package. The Spearman method was employed to analyze the correlation of the data and visualize it through a heatmap. Then we establish a deep learning model (model 1) and a fusion model (model 2) combined deep learning with radiomics features to predict nucleic acid turning-negative time.

Results: COVID-19 patients with emphysema was lowest in the lymphocyte count compared to COVID-19 patients and COVID-19 companied with chronic bronchitis, and they have the most extensive range of pulmonary inflammation. The lymphocyte count was significantly correlated with pulmonary involvement and the time for nucleic acid turning negative (r=-0.145, P < 0.05). Importantly, our results demonstrated that model 2 achieved an accuracy of 80.9% in predicting nucleic acid turning-negative time.

Conclusion: The pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement of COVID-19 patients. Deep learning and radiomics features may provide more information to accurately predict the nucleic acid turning-negative time, which is expected to play an important role in clinical practice.