BACKGROUND: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (RPM) and digital medicine. RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems.
OBJECTIVE: This study aimed to conduct a dual systematic review. The first review focuses on randomized controlled trials where RPM was used as an intervention to treat or improve AECOPD. The second review examines studies that combined machine learning with RPM to predict AECOPD. We review the evidence and concepts behind RPM and machine learning and discuss the strengths, limitations, and clinical use of available systems. We have generated a list of recommendations needed to deliver patient and health care system benefits.
METHODS: A comprehensive search strategy, encompassing the Scopus and Web of Science databases, was used to identify relevant studies. A total of 2 independent reviewers (HMGG and CM) conducted study selection, data extraction, and quality assessment, with discrepancies resolved through consensus. Data synthesis involved evidence assessment using a Critical Appraisal Skills Programme checklist and a narrative synthesis. Reporting followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.
RESULTS: These narrative syntheses suggest that 57% (16/28) of the randomized controlled trials for RPM interventions fail to achieve the required level of evidence for better outcomes in AECOPD. However, the integration of machine learning into RPM demonstrates promise for increasing the predictive accuracy of AECOPD and, therefore, early intervention.
CONCLUSIONS: This review suggests a transition toward the integration of machine learning into RPM for predicting AECOPD. We discuss particular RPM indices that have the potential to improve AECOPD prediction and highlight research gaps concerning patient factors and the maintained adoption of RPM. Furthermore, we emphasize the importance of a more comprehensive examination of patient and health care burdens associated with RPM, along with the development of practical solutions.

Asthma is a chronic respiratory disorder characterized by airway inflammation and narrowing often leading to acute exacerbations that necessitate a visit to the emergency department (ED). Whilst life threatening in sever cases, mild to moderate cases can be treated by the administration of bronchodilators delivered by nebulizers or metered dose inhalers (MDI). Numerous studies have attempted to compare between the two modalities and have drawn similar conclusions in that both are comparable in efficacy with minimal differences. What is evident, however, is that physicians remain inclined to favor nebulizers in the majority of acute asthma exacerbations. In this questionnaire-based study, a survey was distributed to physicians who treat asthma exacerbations to examine demographics, knowledge, beliefs and current practice in regard to bronchodilator therapy. Results found the majority (90.8%) of physicians prefer short-acting beta agonists via nebulizer, with 9.2% favoring MDI + spacer. Participants include consultants, residents, and specialists across various emergency disciplines. While 90.1% find MDI + spacer equally effective as nebulizers, advantages cited include cost-effectiveness (49.6%), shorter ED stays (63.4%), quicker administration (67.9%), and ease of use (58.8%). Challenges include availability (66.4%) and ineffectiveness in younger patients (45%). Despite this, 65.6% are willing to switch to MDI for initial asthma management in the ED, while 34.4% are resistant. Concerns about availability and effectiveness in younger patients remain barriers. However, a significant number are willing to adopt MDIs with spacers, indicating potential for broader use with better availability and training.

BACKGROUND: Previous studies have consistently reported a decrease in hospital admissions for respiratory diseases during the coronavirus disease 2019 (COVID-19) pandemic. However, the impact of the pandemic on idiopathic pulmonary fibrosis (IPF) admissions remains unknown.
METHODS: This study used data from the Korean National Health Insurance Service database. IPF was defined based on the International Classification of Diseases 10th Revision (ICD-10) and rare intractable disease (RID) codes. The rate of IPF admissions was calculated by dividing the number of IPF admissions by the prevalence of IPF. The rate of IPF admissions during the COVID-19 pandemic (2020-2021) was compared with the mean rate of admissions during the prepandemic period (2017-2019) and presented as the rate ratio (RR). A sensitivity analysis was conducted on patients treated with systemic corticosteroids during IPF admission.
RESULTS: In patients with IPF defined based on the ICD-10 (analysis 1), the RRs significantly decreased from March in 2020 to December 2021, except for June and September in 2020. Similarly, in patients with IPF defined based on the ICD-10 and RID (analysis 2), the RRs significantly decreased from March 2020 to December 2021, except for June and September 2020. In the sensitivity analysis of analysis 1, the RR significantly decreased in 2020 (0.93; 95%CI: 0.88-0.99; P = 0.029), whereas the RR in 2021 was not significantly different. The RRs in the sensitivity analysis of analysis 2 significantly decreased to 0.85 (0.79-0.92; P < 0.001) in 2020 and 0.82 (0.76-0.88; P < 0.001) in 2021. In the subgroup analysis, the rates of IPF admissions significantly decreased in 2020 and 2021 across both sexes, patients aged ≥ 60 years, and all household income groups.
CONCLUSIONS: The rate of IPF admissions significantly decreased during the COVID-19 pandemic. This result indicates that preventive measures against COVID-19 may effectively mitigate IPF exacerbation. Therefore, it is assumed that there is a close relationship between respiratory viral infections and IPF exacerbations.

BACKGROUND: Whether the ratio of forced expiratory volume in 1 s to forced vital capacity (FEV1/FVC) can be used as a biomarker to predict the risk of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is unclear.
METHODS: To investigate the predictive role of FEV1/FVC for AECOPD, we analyzed data from an observational and multicenter cohort study of 2043 patients with COPD in KOREA. Exposures were post-bronchodilator FEV1/FVC and/or percentage predicted FEV1 (FEV1%pred). The outcome was the development of AECOPD during the first year of follow-up.
RESULTS: During the first year of follow-up, the proportion of patients who developed AECOPD increased as FEV1/FVC decreased (P < 0.01). FEV1/FVC and FEV1%pred had similar predictive power for AECOPD, with optimal predictive cut-offs of approximately 0.5 for FEV1/FVC and 50 % for FEV1%pred. When the participants were classified into groups based on these cut-offs, compared with a high both-lung function group (FEV1/FVC≥0.5 and FEV1%pred≥50 %), the low-FEV1 group (FEV1/FVC≥0.5 and FEV1%pred<50) had a modestly increased risk of severe AECOPD (adjusted odds ratio[aOR] = 3.12; 95 % confidence interval[CI] = 1.59-6.16), while the risk of severe AECOPD was the highest in the low both-lung function group (FEV1%pred<50 % and FEV1/FVC<0.5) (aOR = 5.16; 95 % CI = 3.34-7.97).
CONCLUSIONS: FEV1/FVC is a spirometric biomarker predictive of AECOPD. In countries where FEV1%pred is not available for their population, FEV1/FVC could be used as a biomarker for assessing the risk of AECOPD. In countries where accurate FEV1%pred is available, both FEV1%pred and FEV1/FVC could be used to provide additional information to assess the risk of AECOPD.
CONCLUSIONS: This study showed that FEV1/FVC had similar predictive power for AECOPD compared with percentage predicted FEV1. Furthermore, the use of both FEV1 and FEV1/FVC provides additional information for the risk assessment of AECOPD.

UNASSIGNED: To synthesize current evidence of the association between body mass index (BMI) categories and the risk of exacerbation in patients with chronic obstructive pulmonary disease (COPD).
UNASSIGNED: A systematic search was conducted across three electronic databases: PubMed, Embase, and Scopus. Eligible studies should have reported on the association between BMI (either as continuous or categorical) and a risk of COPD exacerbation, as defined according to recognized clinical criteria. Observational studies (cohort, case-control, cross-sectional) were eligible for inclusion. The Newcastle Ottawa Scale (NOS) was used to evaluate the methodological quality. Combined effect sizes were reported as relative risk (RR) and corresponding 95% confidence intervals (CI).
UNASSIGNED: A total of 11 studies were included. Of them, four studies were prospective, and four were retrospective cohorts in design, two were cross-sectional studies and one study was a secondary data analysis from a randomized trial. Compared to patients with normal BMI, underweight patients had increased risk of COPD exacerbation (RR 1.90, 95% CI: 1.03, 3.48; N=7, I2=94.2%). Overweight and obese BMI status was associated with a similar risk of exacerbation.
UNASSIGNED: Our findings report that underweight, but not overweight or obese patients, have increased risk of COPD exacerbation, compared to individuals with normal BMI. This differential association emphasizes the need for nuanced investigations into the underlying mechanisms of the impact of BMI on the course of COPD. Further research is needed to inform personalized interventions and improved COPD management strategies.

BACKGROUND: Exacerbations in chronic obstructive pulmonary disease (COPD) can be life-threatening and lead to irreversible declines in lung function and quality of life. Medications that reduce exacerbation burden are an unmet need, as exacerbations put patients at risk for more exacerbations and decrease quality of life. Ensifentrine is a novel, first-in-class, selective, dual inhibitor of phosphodiesterase 3/4 with demonstrated nonsteroidal anti-inflammatory activity and bronchodilatory effects.
OBJECTIVE: Does ensifentrine reduce the rate and/or risk of COPD exacerbations?
METHODS: A prespecified, pooled analysis of the phase 3 clinical trials ENHANCE-1 (NCT04535986) and ENHANCE-2 (NCT04542057) was conducted to assess the effect of ensifentrine on exacerbation rate and risk (time to first exacerbation). The trials included symptomatic patients aged 40-80 years with moderate-to-severe COPD who received 3 mg twice-daily ensifentrine over 24 weeks or placebo. Subgroup analyses and frequent exacerbator transition risk were conducted post-hoc.
RESULTS: In total, 975 ensifentrine-treated and 574 placebo-treated patients were included in the pooled analysis, including 62% on concomitant LAMA or LABA therapy and 18% on concomitant inhaled corticosteroid therapy. Ensifentrine was associated with significant reductions in the rate (rate ratio, 0.59; 95% CI, 0.43-0.80; P < 0.001) and risk (hazard ratio, 0.59; 95% CI, 0.44-0.81; P < 0.001) of moderate/severe exacerbations compared with placebo. Reductions in the rate and risk of exacerbations were generally consistent across patient subgroups, including age, sex, race, background maintenance medication use, chronic bronchitis, eosinophil count, COPD severity, and exacerbation history. Ensifentrine was associated with a numerical delay in transitioning from an infrequent exacerbator (GOLD B) to a frequent exacerbator (GOLD E) compared with placebo.
CONCLUSIONS: Ensifentrine reduced the rate of exacerbations and increased the time to first exacerbation among patients with COPD across a broad range of clinically relevant subgroups.

BACKGROUND: Pneumonias are events of great prognostic significance in COPD, so it is important to identify predictive factors.
OBJECTIVE: To determine whether poor glycemic control is related to an increased risk of pneumonia in COPD.
METHODS: A historical cohort study conducted in a COPD clinic. The first severe exacerbation after the first visit was analyzed. Exacerbations that presented with pulmonary infiltrates were identified. A Cox proportional hazards analysis was performed including the values of glycosylated hemoglobin (Hb1Ac) in patients with diabetes mellitus (DM) and variables that could plausibly be related to the risk of pneumonia. The best Hb1Ac value to predict pneumonia was assessed using receiver-operating characteristics analysis.
RESULTS: There were 1124 cases included in the study. A total of 411 patients were admitted to the hospital at least once and 87 were diagnosed with pneumonia. Variables associated with the risk of pneumonia were previous admissions due to COPD and Hb1Ac values (HR: 2.33, 95% CI: 1.06 - 5.08, p = 0.03). A higher body mass index (BMI) was associated with a lower risk of pneumonia. The optimal cutoff point for Hb1Ac to predict pneumonia risk was 7.8 %. The patients were classified into 3 groups: (1) no DM, (2) controlled DM (Hb1AC < 7.8 %), (3) uncontrolled DM (Hb1AC ≥ 7.8 %). The risk of pneumonia for group 2 was not different from group 1, while the risk for group 3 was significantly higher than for groups 1 and 2 (HR: 4.52, 95 % CI: 1.57 - 13.02).
CONCLUSIONS: Poor control of DM is a predictor of the risk of pneumonia in COPD. The cutoff point of 7.8 % for this variable seems to be the most useful to identify patients at risk.

BACKGROUND: The integration of machine learning (ML) in predicting asthma-related outcomes in children presents a novel approach in pediatric health care.
OBJECTIVE: This scoping review aims to analyze studies published since 2019, focusing on ML algorithms, their applications, and predictive performances.
METHODS: We searched Ovid MEDLINE ALL and Embase on Ovid, the Cochrane Library (Wiley), CINAHL (EBSCO), and Web of Science (core collection). The search covered the period from January 1, 2019, to July 18, 2023. Studies applying ML models in predicting asthma-related outcomes in children aged <18 years were included. Covidence was used for citation management, and the risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool.
RESULTS: From 1231 initial articles, 15 met our inclusion criteria. The sample size ranged from 74 to 87,413 patients. Most studies used multiple ML techniques, with logistic regression (n=7, 47%) and random forests (n=6, 40%) being the most common. Key outcomes included predicting asthma exacerbations, classifying asthma phenotypes, predicting asthma diagnoses, and identifying potential risk factors. For predicting exacerbations, recurrent neural networks and XGBoost showed high performance, with XGBoost achieving an area under the receiver operating characteristic curve (AUROC) of 0.76. In classifying asthma phenotypes, support vector machines were highly effective, achieving an AUROC of 0.79. For diagnosis prediction, artificial neural networks outperformed logistic regression, with an AUROC of 0.63. To identify risk factors focused on symptom severity and lung function, random forests achieved an AUROC of 0.88. Sound-based studies distinguished wheezing from nonwheezing and asthmatic from normal coughs. The risk of bias assessment revealed that most studies (n=8, 53%) exhibited low to moderate risk, ensuring a reasonable level of confidence in the findings. Common limitations across studies included data quality issues, sample size constraints, and interpretability concerns.
CONCLUSIONS: This review highlights the diverse application of ML in predicting pediatric asthma outcomes, with each model offering unique strengths and challenges. Future research should address data quality, increase sample sizes, and enhance model interpretability to optimize ML utility in clinical settings for pediatric asthma management.

Asthma is a chronic inflammatory airway disease characterized by bronchial hyperresponsiveness and reversibility. Despite considerable advances in asthma treatment based on our understanding of its pathophysiology, asthma exacerbations remain challenging. To reduce asthma exacerbations, it is essential to identify triggers, patients\' risk factors, and underlying mechanisms. While exposure to viruses and environmental stimuli are known common triggers for asthma exacerbations, the key factors involved in asthma exacerbations have been identified as type 2 inflammation. Type 2 inflammatory biomarkers have been demonstrated to be useful in predicting individuals at risk of exacerbations. Furthermore, recent clinical trials of targeted biological therapy, which blocks the type 2 pathway, have supported the critical role of type 2 inflammation in asthma exacerbations. Although the specific mechanisms linking type 2 inflammation to asthma exacerbations have not yet been fully elucidated, increasing evidence shows that reduction/oxidation (redox) imbalance likely plays an important role in this association. Under type 2 inflammatory conditions, human airway epithelial cells activate 15-lipoxygenase-1 in complex with phosphatidylethanolamine binding protein-1, leading to the generation of electrophilic hydroperoxyl-phospholipids. When the accumulation of reactive lipid peroxidation surpasses a specific glutathione-dependent activity, these electrophilic compounds are not neutralized, leading to programmed cell death, ferroptosis. Reduced glutathione levels, caused by type 2 inflammation, may impair its ability to neutralize reactive lipid peroxidation. The accumulation of lipid peroxidation with intracellular redox imbalance may contribute to asthma exacerbations in individuals with type 2 inflammation. Inhibiting the ferroptotic pathway holds promise as a therapeutic strategy to alleviate asthma exacerbations.

Chronic Obstructive Pulmonary Disease (COPD) is characterized by persistent respiratory symptoms and airflow limitation. Acute exacerbation of COPD (AECOPD) is an acute worsening of respiratory symptoms, which needs additional treatment and can result in worsening health status, increasing risks of hospitalization and mortality. Therefore, it is necessary to early recognize and diagnose exacerbations of COPD. This review introduces the updated definition of COPD exacerbations, the current clinical assessment tools, and the current potential biomarkers. The application of mobile health care in COPD management for early identification and diagnosis is also included in this review.