It is well known that air pollution causes respiratory morbidity and mortality by inducing airway inflammation. However, whether long-term exposure to air pollution is associated with increased incidence of chronic obstructive pulmonary disease (COPD) is controversial. We conducted a systematic review and meta-analysis with a random-effects model to calculate the pooled risk estimates of COPD development per 10 μg/m3 increase in individual air pollutants. PubMed, Embase, and Cochrane Library were searched from the date of their inception to August 2019 to identify long-term (at least three years of observation) prospective longitudinal studies that reported the risk of COPD development due to exposure to air pollutants. The air pollutants studied included particulate matter (PM2.5 and PM10) and nitrogen dioxide (NO2). Of the 436 studies identified, seven met our eligibility criteria. Among the seven studies, six, three, and five had data on PM2.5, PM10, and NO2, respectively. The meta-analysis results showed that a 10 μg/m3 increase in PM2.5 is associated with increased incidence of COPD (pooled HR 1.18, 95% CI 1.13-1.23). We also noted that a 10 μg/m3 increase in NO2 is marginally associated with increased incidence of COPD (pooled HR 1.07, 95% CI 1.00-1.16). PM10 seems to have no significant impact on the incidence of COPD (pooled HR 0.95, 95% CI 0.83-1.08), although the number of studies was too small. Meta-regression analysis found no significant effect modifiers. Long-term exposure to PM2.5 and NO2 can be associated with increased incidence of COPD.

Background: Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available. Objective: The purpose of this study was to develop a machine learning model that predicts the need for intensive care for patients with COVID-19 using easily obtainable characteristics—baseline demographics, comorbidities, and symptoms. Methods: A retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25, 2020, to June 3, 2020, were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20, 2020, were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21, 2020. The machine learning model with the best discrimination performance was selected and compared against the CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65 years of age or older) score using the area under the receiver operating characteristic curve (AUC). Results: A total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUC of the XGBoost model was 0.897 (95% CI 0.877-0.917) for the derivation group and 0.885 (95% CI 0.855-0.915) for the validation group. Both the AUCs were superior to those of CURB-65, which were 0.836 (95% CI 0.825-0.847) and 0.843 (95% CI 0.829-0.857), respectively. Conclusions: We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19.

Hyung-Jun Kim, Sohyun Park, Seonghyeon Jeong, Jihoon Kim, Young-Jae Cho. IJSC 2024;17:30-7. https://doi.org/10.15283/ijsc23090

Background: Various combinations of inhaled corticosteroid (ICS), long-acting muscarinic antagonist (LAMA), and long-acting beta-agonist (LABA) have been used as triple therapy for stable chronic obstructive pulmonary disease (COPD). Objective: Our study was conducted to answer whether there were significant differences among various combinations in efficacy, for reducing exacerbation or mortality, and in safety, for increasing cardiovascular events or pneumonia. Method: We searched parallel-group randomized controlled trials (RCTs) comparing ICS/LAMA/LABA with other inhaled drugs in patients with stable COPD for at least 12 weeks in PubMed, EMBASE, the Cochrane Library, and clinical trial registries from inception to December 31, 2019. We conducted a network meta-analysis with Bayesian statistics using a random-effects model with heterogeneous variance structure (PROSPERO, CRD42019126757). Results: Nine different combinations of ICS/LAMA/LABA were identified in 21 RCTs containing 29,892 patients with moderate to very severe COPD. We could not find any significant evidence suggesting a better treatment for reducing total exacerbations or all-cause mortality among ICS/LAMA/LABA combinations. There were also no significant differences in moderate to severe exacerbation, COPD-related mortality, or cardiovascular disease-related mortality among ICS/LAMA/LABA combinations, and the risk of major adverse cardiovascular events was not different. A significantly lower risk of pneumonia was found in fluticasone propionate (FP)/glycopyrrolate/salmeterol (SAL) than FP/tiotropium/SAL {median odds ratio [OR] (95% credible interval [CrI]) = 0 [0–0.72]} and FP/umeclidinium/SAL {median OR (95% Crl) = 0 [0–0.97]}. Conclusion: There were no significant differences in clinical outcomes, including acute exacerbation and all-cause mortality among various ICS/LAMA/LABA combinations in patients with moderate to very severe COPD.

To develop in vitro respiratory models, it is crucial to identify the factors involved in epithelial cell differentiation. In this study, we comprehensively analyzed the effects of air–liquid interface (ALI) culture on epithelial cell differentiation using single-cell RNA sequencing (scRNA-seq). ALI culture induced a pronounced shift in cell composition, marked by a fivefold increase in ciliated cells and a reduction of more than half in basal cells. Transcriptional signatures associated with epithelial cell differentiation, analyzed using iPathwayGuide software, revealed the downregulation of VEGFA and upregulation of CDKN1A as key signals for epithelial differentiation. Our findings highlight the efficacy of the ALI culture for replicating the human lung airway epithelium and provide valuable insights into the crucial factors that influence human ciliated cell differentiation.

Background: Bacterial colonization is an essential aspect of bronchiectasis. Although Haemophilus influenzae is a frequent colonizer in some regions, its clinical impacts are poorly understood. This study aimed to elucidate the impact of H. influenzae colonization in patients with bronchiectasis. Methods: This retrospective study screened adult patients diagnosed with bronchiectasis at a tertiary referral center between April 1, 2003, and May 16, 2021, in South Korea. Propensity score matching was used to match patients with and without H. influenzae colonization. We assessed the severity of bronchiectasis as per the bronchiectasis severity index, the incidence of exacerbation, differences in lung function, and all-cause mortality. Results: Out of the 4,500 patients with bronchiectasis, 79 (1.8%) were colonized by H. influenzae. After 1:2 propensity score matching, 78 and 154 patients were selected from the H. influenzae colonizer and non-colonizer groups, respectively. Although there were no significant differences between the groups regarding baseline demographics, patients colonized with H. influenzae had a higher bronchiectasis severity index (median 6 [interquartile range 4–8] vs. 4 [2–7], p = 0.002), associated with extensive radiographic involvement (52.2% vs. 37.2%, p = 0.045) and mild exacerbation without hospitalization (adjusted incidence rate ratio 0.15; 95% confidence interval 0.12–0.24). Lung function and mortality rates did not reveal significant differences, regardless of H. influenzae colonization. Conclusion: H. influenzae colonization in bronchiectasis was associated with more severe disease and greater incidence of mild exacerbation, but not lung function and mortality. Attention should be paid to patients with bronchiectasis with H. influenzae colonization.