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.

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: 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.

A scoping review methodological framework formed the basis of this review. A search of two electronic databases captured relevant literature published from 2013. 1184 articles were screened, 200 of which met inclusion criteria. Included studies were categorised as tests for either respiratory infections OR pulmonary exacerbations. Data were extracted to ascertain test type, sample type, and indication of use for each test type. For infection, culture is the most common testing method, particularly for bacterial infections, whereas PCR is utilised more for the diagnosis of viral infections. Spirometry tests, indicating lung function, facilitate respiratory infection diagnoses. There is no clear definition of what an exacerbation is in persons with CF. A clinical checklist with risk criteria can determine if a patient is experiencing an exacerbation event, however the diagnosis is clinician-led and will vary between individuals. Fuchs criteria are one of the most frequently used tests to assess signs and symptoms of exacerbation in persons with CF. This scoping review highlights the development of home monitoring tests to facilitate earlier and easier diagnoses, and the identification of novel biomarkers for indication of infections/exacerbations as areas of current research and development. Research is particularly prevalent regarding exhaled breath condensate and volatile organic compounds as an alternative sampling/biomarker respectively for infection diagnosis. Whilst there are a wide range of tests available for diagnosing respiratory infections and/or exacerbations, these are typically used clinically in combination to ensure a rapid, accurate diagnosis which will ultimately benefit both the patient and clinician.

Asthma and chronic obstructive pulmonary disease are chronic respiratory disorders characterized by airways obstruction and chronic inflammation. Exacerbations lead to worsening of symptoms and increased airflow obstruction in both airways diseases, and they are associated with increase in local and systemic inflammation. Exosomes are cell-derived membrane vesicles containing proteins, lipids, and nucleic acids that reflect their cellular origin. Through the transfer of these molecules, exosomes act as mediators of intercellular communication. Via selective delivery of their contents to target cells, exosomes have been proved to be involved in regulation of immunity and inflammation. Although, exosomes have been extensively investigated in different diseases, little is currently known about their role in asthma and COPD pathogenesis, and particularly in exacerbations. This review aims to systemically assess the potential role of exosomes in asthma and COPD exacerbations.

Background: Disorders of mucociliary clearance, such as cystic fibrosis (CF), primary ciliary dyskinesia (PCD) and bronchiectasis of unknown origin, are characterised by periods with increased respiratory symptoms, referred to as pulmonary exacerbations. These exacerbations are hard to predict and associated with lung function decline and the loss of quality of life. To optimise treatment and preserve lung function, there is a need for non-invasive and reliable methods of detection. Breath analysis might be such a method. Methods: We systematically reviewed the existing literature on breath analysis to detect pulmonary exacerbations in mucociliary clearance disorders. Extracted data included the study design, technique of measurement, definition of an exacerbation, identified compounds and diagnostic accuracy. Results: Out of 244 identified articles, 18 were included in the review. All studies included patients with CF and two also with PCD. Age and the definition of exacerbation differed between the studies. There were five that measured volatile organic compounds (VOCs) in exhaled breath using gas chromatography with mass spectrometry, two using an electronic nose and eleven measured organic compounds in exhaled breath condensate. Most studies showed a significant correlation between pulmonary exacerbations and one or multiple compounds, mainly hydrocarbons and cytokines, but the validation of these results in other studies was lacking. Conclusions: The detection of pulmonary exacerbations by the analysis of compounds in exhaled breath seems possible but is not near clinical application due to major differences in results, study design and the definition of an exacerbation. There is a need for larger studies, with a longitudinal design, international accepted definition of an exacerbation and validation of the results in independent cohorts.

The basis of COPD maintenance treatment is the long-acting bronchodilators and the inhaled corticosteroids. Faced with the recent modifications in the clinical practice guidelines, we have carried out a review of studies that contrast the various therapeutic alternatives and pharmacological agents within each category, with the fundamental purpose of shedding light on which of these options prove to be more effective. Triple therapy stands out as essential in poorly controlled patients or with an eosinophilic phenotype, surpassing dual therapy. However, among the combinations of LAMA/LABA or LAMA/LABA/IC, no drug is observed to be superior in the reviewed evidence. Although triple therapies include corticosteroids, there does not appear to be a significant increase in side effects or pneumonia. Regarding monotherapy with LAMA, no significant differences are seen between the drugs, but in dual therapy with LABA/IC, the budesonide/formoterol combination seems to offer better control than fluticasone/salmeterol.

BACKGROUND: An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks.
OBJECTIVE: This study aims to critically evaluate ML-based models derived using EHRs for the prediction of asthma attacks.
METHODS: We systematically searched PubMed and Scopus (the search period was between January 1, 2012, and January 31, 2023) for papers meeting the following inclusion criteria: (1) used EHR data as the main data source, (2) used asthma attack as the outcome, and (3) compared ML-based prediction models\' performance. We excluded non-English papers and nonresearch papers, such as commentary and systematic review papers. In addition, we also excluded papers that did not provide any details about the respective ML approach and its result, including protocol papers. The selected studies were then summarized across multiple dimensions including data preprocessing methods, ML algorithms, model validation, model explainability, and model implementation.
RESULTS: Overall, 17 papers were included at the end of the selection process. There was considerable heterogeneity in how asthma attacks were defined. Of the 17 studies, 8 (47%) studies used routinely collected data both from primary care and secondary care practices together. Extreme imbalanced data was a notable issue in most studies (13/17, 76%), but only 38% (5/13) of them explicitly dealt with it in their data preprocessing pipeline. The gradient boosting-based method was the best ML method in 59% (10/17) of the studies. Of the 17 studies, 14 (82%) studies used a model explanation method to identify the most important predictors. None of the studies followed the standard reporting guidelines, and none were prospectively validated.
CONCLUSIONS: Our review indicates that this research field is still underdeveloped, given the limited body of evidence, heterogeneity of methods, lack of external validation, and suboptimally reported models. We highlighted several technical challenges (class imbalance, external validation, model explanation, and adherence to reporting guidelines to aid reproducibility) that need to be addressed to make progress toward clinical adoption.

UNASSIGNED: According to Global Initiative for Asthma (GINA) guidelines, long-acting muscarinic antagonists (LAMAs) should be considered as add-on therapy in patients with asthma that remains uncontrolled, despite treatment with medium-dose (MD) or high-dose (HD) inhaled corticosteroids (ICS)/long-acting β2-agonist (LABA) combinations. In patients ≥ 18 years, LAMA may be added in triple combination with an ICS and a LABA. To date, the precise efficacy of triple ICS/LABA/LAMA combination remains uncertain concerning the impact on exacerbation risk in patients with uncontrolled asthma. Therefore, an umbrella review was performed to systematically summarize available data on the effect of triple ICS/LABA/LAMA combination on the risk of asthma exacerbation.
UNASSIGNED: An umbrella review has been performed according to the PRIOR statement.
UNASSIGNED: The overall results obtained from 5 systematic reviews and meta-analyses suggest that triple ICS/LABA/LAMA combination reduces the risk of asthma exacerbation. HD-ICS showed a greater effect particularly in reducing severe asthma exacerbation, especially in patients with evidence of type 2 inflammation biomarkers.
UNASSIGNED: The findings of this umbrella review suggest an optimization of ICS dose in triple ICS/LABA/LAMA combination, based on the severity of exacerbation and type 2 biomarkers expression.

BACKGROUND: Although respiratory tract infection is a significant factor that triggers exacerbation of chronic obstructive pulmonary disease (COPD), the benefit of antibiotics for patients with COPD exacerbation remains controversial. It is necessary to evaluate the efficacy and safety of antibiotics versus placebo in such patients.
METHODS: We conducted a systematic review and meta-analysis of randomized controlled trials of antibiotics versus placebo for the treatment of COPD exacerbation, and compared the frequencies of treatment failure, mortality, and adverse events between patients treated with antibiotics and those treated with placebo.
RESULTS: A total of six studies were included in this meta-analysis. The frequency of treatment failure was significantly lower in the antibiotic-treated patients compared to the placebo-treated patients (odds ratios [OR] 0.50, 95% confidence intervals [CI] 0.35-0.71, p = 0.0001). There was no significant difference between the two groups in mortality (OR 0.44, 95% CI 0.05-3.76, p = 0.45) or frequency of adverse events (OR 1.05, 95% CI 0.75-1.48, p = 0.78).
CONCLUSIONS: In the current systematic review and meta-analysis, we found that antibiotics were superior to placebo in patients with exacerbated COPD, as shown by the lower treatment failure rate.