Tuberculosis and Respiratory Diseases最新文献

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.

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.

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: 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.

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.

A recent understanding of the dynamic continuous spectrum of Mycobacterium tuberculosis infection has led to the recognition of incipient tuberculosis, which refers to the latent infection state that has begun to progress to active tuberculosis. The importance of early detection of these individuals with a high-risk of progression to active tuberculosis is emphasized to efficiently implement targeted tuberculosis preventive therapy. However, the tuberculin skin test or interferon-γ release assay, which is currently used for the diagnosis of latent tuberculosis infection, does not aid in the prediction of the risk of progression to active tuberculosis. Thus, a novel test is urgently needed. Recently, simultaneous and systematic analysis of differentially expressed genes using a high-throughput platform has enabled the discovery of key genes that may serve potential biomarkers for the diagnosis or prognosis of diseases. This host transcriptional investigation has been extended to the field of tuberculosis, providing promising results. The present review focuses on recent progress and challenges in the field of blood transcriptional signatures to predict progression to active tuberculosis.

Coronavirus disease 2019 (COVID-19) has become a major health burden worldwide, with over 600 million confirmed cases and 6 million deaths by 15 December 2022. Although the acute phase of COVID-19 management has been established, the long-term clinical course and complications due to the relatively short outbreak is yet to be assessed. The current COVID-19 pandemic is causing significant morbidity and mortality around the world. Interestingly, epidemiological studies have shown that fatality rates vary considerably across different countries, and men and elderly patients are at higher risk of developing severe diseases. There is increasing evidence that COVID-19 infection causes neurological deficits in a substantial proportion to patients suffering from acute respiratory distress syndrome. Furthermore, lack of physical activity and smoking are associated with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) susceptibility. We should therefore explore why lack of physical activity, smoking, etc causing a population more susceptible to SARS-CoV-2 infection, and mechanism involved. Thus, in this review article, we summarize epidemiological evidence related to risk factors and lifestyle that affect COVID-19 severity and the mechanism involved. These risk factors or lifestyle interventions include smoking, cardiovascular health, obesity, exercise, environmental pollution, psychosocial social stress, and diet.

Background: The present study evaluated the association between participation in a rehabilitation program during a hospital stay and 1-year survival of patients requiring at least 21 days of mechanical ventilation (prolonged mechanical ventilation [PMV]) with various respiratory diseases as their main diagnoses that led to mechanical ventilation.

Methods: Retrospective data of 105 patients (71.4% male, mean age 70.1±11.3 years) who received PMV in the past 5 years were analyzed. Rehabilitation included physiotherapy, physical rehabilitation, and dysphagia treatment program that was individually provided by physiatrists.

Results: The main diagnosis leading to mechanical ventilation was pneumonia (n=101, 96.2%) and the 1-year survival rate was 33.3% (n=35). One-year survivors had lower Acute Physiology and Chronic Health Evaluation (APACHE) II score (20.2±5.8 vs. 24.2±7.5, p=0.006) and Sequential Organ Failure Assessment score (6.7±5.6 vs. 8.5±2.7, p=0.001) on the day of intubation than non-survivors. More survivors participated in a rehabilitation program during their hospital stays (88.6% vs. 57.1%, p=0.001). The rehabilitation program was an independent factor for 1-year survival based on the Cox proportional hazard model (hazard ratio, 3.513; 95% confidence interval, 1.785 to 6.930; p<0.001) in patients with APACHE II scores ≤23 (a cutoff value based on Youden's index).

Conclusion: Our study showed that participation in a rehabilitation program during hospital stay was associated with an improvement of 1-year survival of PMV patients who had less severe illness on the day of intubation.

Background: To systematically review studies on inhaled corticosteroids (ICS) and lung cancer incidence in chronic airway disease patients.

Methods: We conducted electronic bibliographic searches on OVID-MEDLINE, EM- BASE, and the Cochrane Database before May 2020 to identify relevant studies. Detailed data on the study population, exposure, and outcome domains were reviewed.

Results: Of 4,058 screened publications, 13 eligible studies in adults with chronic obstructive pulmonary disease (COPD) or asthma evaluated lung cancer incidence after ICS exposure. Pooled hazard ratio and odds ratio for developing lung cancer in ICS exposure were 0.81 (95% confidence interval, 0.64 to 1.02; I2=95.7%) from 10 studies and 1.02 (95% confidence interval 0.50 to 2.07; I2=94.7%) from three studies. Meta-regression failed to explain the substantial heterogeneity of pooled estimates. COPD and asthma were variously defined without spirometry in 11 studies. Regarding exposure assessment, three and 10 studies regarded ICS exposure as a time-dependent and fixed variable, respectively. Some studies assessed ICS use for the entire study period, whereas others assessed ICS use for 6 months to 2 years within or before study entry. Smoking was adjusted in four studies, and only four studies introduced 1 to 2 latency years in their main or subgroup analysis.

Conclusion: Studies published to date on ICS and lung cancer incidence had heterogeneous study populations, exposures, and outcome assessments, limiting the generation of a pooled conclusion. The beneficial effect of ICS on lung cancer incidence has not yet been established, and understanding the heterogeneities will help future researchers to establish robust evidence on ICS and lung cancer incidence.