The introduction of inhaled corticosteroids (ICS) for the management of asthma has led to a decrease in acute exacerbation of asthma. However, there are concerns regarding the safety of long-term ICS use, particularly pneumonia. Growing evidence indicates that ICS use is associated with an increased risk of pneumonia in patients with chronic obstructive pulmonary disease, whereas the risk in patients with asthma remains unclear. This review discusses the effect of ICS on pneumonia among patients with asthma to update the existing literature. Asthma is associated with an increased risk of pneumonia. Several hypotheses have been proposed to explain this association, including that asthma impairs the clearance of bacteria owing to chronic inflammation. Therefore, controlling airway inflammation with ICS may prevent the occurrence of pneumonia in asthma. In addition, two meta-analyses investigating randomized control trials showed that ICS use was associated with a protective effect against pneumonia in asthma.

Asthma is a chronic inflammatory airway disease that is characterized by variable airflow obstruction. The Korean Asthma Study Group of the Korean Academy of Tuberculosis and Respiratory Diseases has recently updated the Korean Asthma Guideline. This review summarizes the updated Korean Asthma Guideline. Asthma prevalence is increasing worldwide, and in Korea. Variable airflow obstruction can be confirmed by bronchodilator response or other tests, and should be established prior to the controller medication. A low-dose inhaled corticosteroid-formoterol is used to alleviate symptoms in all treatment step, and it can be used as a controller as well as reliever in steps 3-5. This approach is preferred, because it reduces the risk of severe exacerbations, compared to the use of short-acting β2-agonist as reliever. In severe asthma, phenotype/endotype based on the underlying inflammation should be evaluated. For type 2 severe asthma, the biologics should be considered.

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. The lower airways contain a rich and diverse microbiome, which may play a significant regulatory role in both health and disease. In COPD, the microbiome becomes perturbed, causing dysbiosis. Increased representation of members in the Proteobacteria phylum and certain members in the Firmicutes phylum has been associated with increased risk of exacerbations and mortality. Therapies such as inhaled corticosteroids and azithromycin may modulate the airway microbiome or its metabolites in patients with COPD. This paper provides an up-to-date overview of the airway microbiome and its importance in the pathophysiology of COPD and as potential therapeutic target in the future.

Since the introduction of low-dose computed tomography (CT) screening for patients at high risk of lung cancer, the detection rate of suspicious lung cancer has increased. In addition, there have been many advances in therapeutics targeting oncogenic drivers in non-small cell lung cancer. Therefore, accurate pathological diagnosis of lung cancer, including molecular diagnosis, is increasingly important. This review examines the problems in the pathological diagnosis of suspected lung cancer. For successful pathological diagnosis of lung cancer, clinicians should determine the appropriate modality of the diagnostic procedure, considering individual patient characteristics, CT findings, and the possibility of complications. Furthermore, clinicians should make efforts to obtain a sufficient amount of tissue sample using non- or less-invasive procedures for pathological diagnosis and biomarker analysis.

Bronchiectasis, which is characterized by irreversibly damaged and dilated bronchi, causes significant symptoms, poor quality of life, and increased economic burden and mortality rates. Despite its increasing prevalence and clinical significance, bronchiectasis was previously regarded as an orphan disease, and ideal treatment of this disease has been poorly understood. The European Respiratory Society and British Thoracic Society have recently published guidelines to assist physicians in the clinical field. Guidelines and reports suggest comprehensive management that includes both non-pharmacological and pharmacological treatment. Physiotherapy and pulmonary rehabilitation are two of the most important non-pharmacologic therapies in bronchiectasis patients; long-term inhaled antibiotics and macrolide therapy have gained significant evidence in reducing exacerbation risk in frequent exacerbators. In this review, we summarize recent updates on bronchiectasis treatment to prevent exacerbation and manage clinical deterioration.

Background: The Global Initiative for Chronic Obstructive Lung Disease (GOLD) update 2023 proposed new definitions of chronic obstructive pulmonary disease (COPD) and COPD exacerbation. However, an agreement on the definitions has not been made, either internationally or domestically. This study aimed to reach an agreement between experts on the new definitions of COPD and COPD exacerbation in South Korea. Methods: A modified Delphi method was used to make an agreement on the definitions of COPD and COPD exacerbation proposed by the GOLD update 2023. We performed two rounds of the survey including 15 Korean experts on COPD, asthma, and tuberculosis. Results: More than two-thirds of the experts agreed on 12 of the 13 statements related to the definitions of COPD and COPD exacerbation in the two rounds of the survey. The experts agreed on the definitions of COPD and COPD exacerbation that should be revised in line with the definitions proposed by the GOLD update 2023. However, the experts showed an uncertain opinion on the statement that the definition of COPD includes patients with persistent airflow obstruction due to bronchiectasis. Conclusion: Based on this Delphi survey, experts' agreement was made on the definitions of COPD and COPD exacerbation proposed by the GOLD update 2023.

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R² and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R² value was 0.27 and in set II, LightGBM was the best model with the highest R² value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R² value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function.

Background: Tuberculosis (TB)-related stigma has been well-documented. Since the emergence of the coronavirus disease 2019 (COVID-19), different organizations have been alerted to the fact that stigma could arise again. Due to stigma's negative effects, this qualitative study aimed to explore the stigma felt by patients by evaluating the following: COVID-19 stigma and its temporal progression through the pandemic; stigma perceived by different patients with TB before and during COVID-19 pandemic; and difference perceived by individuals who contracted both diseases. Methods: A semi-structured interview was developed according to the available literature on the theme. It was performed individually in 2022 upon receiving signed informed consent. Participants were recruited with a purposive sampling approach by searching medical records. Those who currently or previously had pulmonary TB and/or COVID-19 were included. Data were subjected to thematic analysis. Results: Nine patients were interviewed, including six (66.7%) females. The median age of patients was 51±14.7 years. Four participants (44.4%) had completed high school and four (44.4%) were never smokers. Three had both TB and COVID-19. Four only had TB and two only had COVID-19. Interviews identified eight main themes: knowledge and beliefs, with several misconceptions identified; attitudes towards the disease, varying from social support to exclusion; knowledge and education, assumed as of extreme importance; internalized stigma, with self-rejection; experienced stigma, with discrimination episodes; anticipated stigma, modifying actions for avoiding stigma; perceived stigma, with judgment by others prevailed; and temporal evolution of stigma. Conclusion: Individuals expressed strong stigma for both diseases. De-stigmatization of respiratory infectious diseases is crucial for limiting stigma's negative impact.

Background: Inactive or old, healed tuberculosis (TB) on chest radiograph (CR) is often found in high TB incidence countries, and to avoid unnecessary evaluation and medication, differentiation from active TB is important. This study develops a deep learning (DL) model to estimate activity in a single chest radiographic analysis. Methods: A total of 3,824 active TB CRs from 511 individuals and 2,277 inactive TB CRs from 558 individuals were retrospectively collected. A pretrained convolutional neural network was fine-tuned to classify active and inactive TB. The model was pretrained with 8,964 pneumonia and 8,525 normal cases from the National Institute of Health (NIH) dataset. During the pretraining phase, the DL model learns the following tasks: pneumonia vs. normal, pneumonia vs. active TB, and active TB vs. normal. The performance of the DL model was validated using three external datasets. Receiver operating characteristic analyses were performed to evaluate the diagnostic performance to determine active TB by DL model and radiologists. Sensitivities and specificities for determining active TB were evaluated for both the DL model and radiologists. Results: The performance of the DL model showed area under the curve (AUC) values of 0.980 in internal validation, and 0.815 and 0.887 in external validation. The AUC values for the DL model, thoracic radiologist, and general radiologist, evaluated using one of the external validation datasets, were 0.815, 0.871, and 0.811, respectively. Conclusion: This DL-based algorithm showed potential as an effective diagnostic tool to identify TB activity, and could be useful for the follow-up of patients with inactive TB in high TB burden countries.

Background: Effective treatment of fluoroquinolone-resistant multidrug-resistant tuberculosis (FQr-MDR-TB) is difficult because of the limited number of available core anti-TB drugs and high rates of resistance to anti-TB drugs other than FQs. However, few studies have examined anti-TB drugs that are effective in treating patients with FQr-MDR-TB in a real-world setting. Methods: The impact of anti-TB drug use on treatment outcomes in patients with pulmonary FQr-MDR-TB was retrospectively evaluated using a nationwide integrated TB database (Korean Tuberculosis and Post-Tuberculosis). Data from 2011 to 2017 were included. Results: The study population consisted of 1,082 patients with FQr-MDR-TB. The overall treatment outcomes were as follows: treatment success (69.7%), death (13.7%), lost to follow-up or not evaluated (12.8%), and treatment failure (3.9%). On a propensity-score-matched multivariate logistic regression analysis, the use of bedaquiline (BDQ), linezolid (LZD), levofloxacin (LFX), cycloserine (CS), ethambutol (EMB), pyrazinamide, kanamycin (KM), prothionamide (PTO), and para-aminosalicylic acid against susceptible strains increased the treatment success rate (vs. unfavorable outcomes). The use of LFX, CS, EMB, and PTO against susceptible strains decreased the mortality (vs. treatment success). Conclusion: A therapeutic regimen guided by drug-susceptibility testing can improve the treatment of patients with pulmonary FQr-MDR-TB. In addition to core anti-TB drugs, such as BDQ and LZD, treatment of susceptible strains with later-generation FQs and KM may be beneficial for FQr-MDR-TB patients with limited treatment options.