Usefulness of routine blood test-driven clusters for predicting acute exacerbation in patients with asthma

Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases

INTRODUCTION

Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment.

AREAS COVERED

This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation.

EXPERT OPINION

Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.

Dyslipidemia Is Associated With Worse Asthma Clinical Outcomes: A Prospective Cohort Study

BACKGROUND

Dyslipidemia has been widely documented to be associated with cardiovascular disease, and recent studies have found an association with asthma prevalence. However, longitudinal studies investigating the relationships between dyslipidemia, asthma phenotypes, and future asthma exacerbations (AEs) are lacking.

OBJECTIVE

To investigate the relationships between dyslipidemia, asthma phenotypes, and AEs.

METHODS

This study used an observational cohort study design with a 12-month follow-up. All subjects underwent serum lipid measurement, and they were then classified into 2 groups: the normal-lipidemia group and the dyslipidemia group. Demographic and clinical information and details regarding pulmonary function and asthma phenotypes at baseline were collected. All patients were followed up regularly to assess AEs. Associations of dyslipidemia with airway obstruction and asthma phenotypes were assessed at baseline, whereas dyslipidemia and AEs were assessed longitudinally.

RESULTS

A total of 477 patients with asthma were consecutively enrolled in this study. At baseline, the dyslipidemia group (n = 218) had a higher proportion of uncontrolled asthma, defined by the 6-item Asthma Control Questionnaire score (≥1.5). Furthermore, dyslipidemia was associated with severe asthma, nonallergic asthma, asthma with fixed airflow limitation, and older adult asthma phenotypes at baseline. In addition, dyslipidemia was associated with increased frequencies of severe AEs and moderate to severe AEs during the 12-month follow-up. In sensitivity analyses, after excluding the patients who were receiving statins, results did not differ significantly from those of the main analysis.

CONCLUSIONS

We identified the clinical relevance of dyslipidemia, which is associated with specific asthma phenotypes and increased AEs, independent of other components of metabolic syndrome. These findings highlight the importance of considering dyslipidemia as an "extrapulmonary trait" in asthma management.

A Systematic Review of Asthma Phenotypes Derived by Data-Driven Methods

Classification of asthma phenotypes has a potentially relevant impact on the clinical management of the disease. Methods for statistical classification without a priori assumptions (data-driven approaches) may contribute to developing a better comprehension of trait heterogeneity in disease phenotyping. This study aimed to summarize and characterize asthma phenotypes derived by data-driven methods. We performed a systematic review using three scientific databases, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. We included studies reporting adult asthma phenotypes derived by data-driven methods using easily accessible variables in clinical practice. Two independent reviewers assessed studies. The methodological quality of included primary studies was assessed using the ROBINS-I tool. We retrieved 7446 results and included 68 studies of which 65% (n = 44) used data from specialized centers and 53% (n = 36) evaluated the consistency of phenotypes. The most frequent data-driven method was hierarchical cluster analysis (n = 19). Three major asthma-related domains of easily measurable clinical variables used for phenotyping were identified: personal (n = 49), functional (n = 48) and clinical (n = 47). The identified asthma phenotypes varied according to the sample’s characteristics, variables included in the model, and data availability. Overall, the most frequent phenotypes were related to atopy, gender, and severe disease. This review shows a large variability of asthma phenotypes derived from data-driven methods. Further research should include more population-based samples and assess longitudinal consistency of data-driven phenotypes.