The association between airflow limitation and blood eosinophil levels with treatment outcomes in patients with chronic obstructive pulmonary disease and prolonged mechanical ventilation

Synergistic effects of COVID-19 and Pseudomonas aeruginosa in chronic obstructive pulmonary disease: a polymicrobial perspective

This article discusses the connection between the novel coronavirus disease 2019 (COVID-19) caused by the coronavirus-2 (SARS-CoV-2) and chronic obstructive pulmonary disease (COPD). COPD is a multifaceted respiratory illness that is typically observed in individuals with chronic exposure to chemical irritants or severe lung damage caused by various pathogens, including SARS-CoV-2 and Pseudomonas aeruginosa. The pathogenesis of COPD is complex, involving a variety of genotypes and phenotypic characteristics that result in severe co-infections and a poor prognosis if not properly managed. We focus on the role of SARS-CoV-2 infection in severe COPD exacerbations in connection to P.  aeruginosa infection, covering pathogenesis, diagnosis, and therapy. This review also includes a thorough structural overview of COPD and recent developments in understanding its complicated and chronic nature. While COVID-19 is clearly linked to emphysema and chronic bronchitis at different stages of the disease, our understanding of the precise interaction between microbial infections during COPD, particularly with SARS-CoV-2 in the lungs, remains inadequate. Therefore, it is crucial to understand the host-pathogen relationship from the clinician's perspective in order to effectively manage COPD. This article aims to provide a comprehensive overview of the subject matter to assist clinicians in their efforts to improve the treatment and management of COPD, especially in light of the COVID-19 pandemic.

Multicentre double-blind randomised controlled trial of systematic corticosteroid therapy in patients with acute exacerbations of chronic obstructive pulmonary disease admitted to hospital with higher eosinophil levels: the ECHO protocol

INTRODUCTION

Corticosteroid is one of the most commonly used medications in patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). The increasing understanding of these side-effects of systematic corticosteroids and their better response to treatment among patients with COPD with higher blood eosinophil counts has led to an interest in a more targeted approach to systematic corticosteroid treatment. However, there is a lack of evidence from high-quality randomised controlled trial (RCT) studies about whether initial systematic corticosteroids should be given to patients with AECOPD with elevated eosinophilia. The aim of the present research was to test this hypothesis.

METHODS AND ANALYSIS

This is a multicentre, double-blind, superiority RCT in the respiratory departments of 12 general hospitals in China. It is anticipated that 456 patients with AECOPD with a blood eosinophil count >2% or >300 cells/µL at admission will be recruited. Eligible patients will be randomised (1:1) to the intervention group receiving 40 mg oral prednisone daily or identical-appearing placebo (control group) for five consecutive days. Follow-up visits are performed during hospitalisation, followed by clinic interviews on days 30, 60 and 90 after discharge. The primary outcome is treatment failure rates comprising requiring or receiving invasive or non-invasive mechanical ventilation, requiring or transferring to intensive care unit during the index hospitalisation, length of index hospitalisation longer than 14 days, death during the index hospitalisation or within 30 days after discharge and readmission with acute exacerbations of COPD within 30 days after discharge. The results of this trial will provide insight into the value of using blood eosinophil counts as a biomarker of eosinophilic exacerbation and initiating systematic corticosteroid treatment for patients with AECOPD with higher eosinophil levels.

ETHICS AND DISSEMINATION

This study was approved by Beijing Chaoyang Hospital Institutional Review Board (approval number: 2020-KE-544) and the main results and secondary results will be published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT05059873.

Machine Learning Methods for the Diagnosis of Chronic Obstructive Pulmonary Disease in Healthy Subjects: Retrospective Observational Cohort Study

BACKGROUND

Airflow limitation is a critical physiological feature in chronic obstructive pulmonary disease (COPD), for which long-term exposure to noxious substances, including tobacco smoke, is an established risk. However, not all long-term smokers develop COPD, meaning that other risk factors exist.

OBJECTIVE

This study aimed to predict the risk factors for COPD diagnosis using machine learning in an annual medical check-up database.

METHODS

In this retrospective observational cohort study (ARTDECO [Analysis of Risk Factors to Detect COPD]), annual medical check-up records for all Hitachi Ltd employees in Japan collected from April 1998 to March 2019 were analyzed. Employees who provided informed consent via an opt-out model were screened and those aged 30 to 75 years without a prior diagnosis of COPD/asthma or a history of cancer were included. The database included clinical measurements (eg, pulmonary function tests) and questionnaire responses. To predict the risk factors for COPD diagnosis within a 3-year period, the Gradient Boosting Decision Tree machine learning (XGBoost) method was applied as a primary approach, with logistic regression as a secondary method. A diagnosis of COPD was made when the ratio of the prebronchodilator forced expiratory volume in 1 second (FEV₁) to prebronchodilator forced vital capacity (FVC) was <0.7 during two consecutive examinations.

RESULTS

Of the 26,101 individuals screened, 1213 met the exclusion criteria, and thus, 24,815 individuals were included in the analysis. The top 10 predictors for COPD diagnosis were FEV₁/FVC, smoking status, allergic symptoms, cough, pack years, hemoglobin A_1c, serum albumin, mean corpuscular volume, percent predicted vital capacity, and percent predicted value of FEV₁. The areas under the receiver operating characteristic curves of the XGBoost model and the logistic regression model were 0.956 and 0.943, respectively.

CONCLUSIONS

Using a machine learning model in this longitudinal database, we identified a number of parameters as risk factors other than smoking exposure or lung function to support general practitioners and occupational health physicians to predict the development of COPD. Further research to confirm our results is warranted, as our analysis involved a database used only in Japan.