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

Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review

INTRODUCTION

Development of asthma and allergies in childhood/adolescence commonly follows a sequential progression termed the 'atopic march'. Recent reports indicate, however, that these diseases are composed of multiple distinct phenotypes, with possibly differential trajectories. We aim to synthesise the current literature in the field of machine learning-based trajectory studies of asthma/allergies in children and adolescents, summarising the frequency, characteristics and associated risk factors and outcomes of identified trajectories and indicating potential directions for subsequent research in replicability, pathophysiology, risk stratification and personalised management. Furthermore, methodological approaches and quality will be critically appraised, highlighting trends, limitations and future perspectives.

METHODS AND ANALYSES

10 databases (CAB Direct, CINAHL, Embase, Google Scholar, PsycInfo, PubMed, Scopus, Web of Science, WHO Global Index Medicus and WorldCat Dissertations and Theses) will be searched for observational studies (including conference abstracts and grey literature) from the last 10 years (2013-2023) without restriction by language. Screening, data extraction and assessment of quality and risk of bias (using a custom-developed tool) will be performed independently in pairs. The characteristics of the derived trajectories will be narratively synthesised, tabulated and visualised in figures. Risk factors and outcomes associated with the trajectories will be summarised and pooled estimates from comparable numerical data produced through random-effects meta-analysis. Methodological approaches will be narratively synthesised and presented in tabulated form and figure to visualise trends.

ETHICS AND DISSEMINATION

Ethical approval is not warranted as no patient-level data will be used. The findings will be published in an international peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42023441691.

Physical Activity and Sedentary Behavior as Treatable Traits for Clinical Control in Moderate-to-Severe Asthma

BACKGROUND

Physical activity and sedentary behavior are treatable traits that may impact asthma control in distinct manners, but this impact remains poorly understood.

OBJECTIVE

To evaluate the influence of physical activity and sedentary behavior on clinical control in adults with moderate-to-severe asthma.

METHODS

This cross-sectional, multicentric study included 426 individuals with moderate-to-severe asthma. Assessments included physical activity and sedentary time (actigraphy), clinical asthma control (Asthma Control Questionnaire [ACQ]), quality of life (Asthma Quality of Life Questionnaire), anxiety and depression symptoms (Hospital Anxiety and Depression Scale), anthropometric data, and lung function. Participants were grouped according to physical activity levels and sedentary behavior.

RESULTS

Participants who walked ≥7500 steps/day presented better ACQ scores than those who walked <7500 steps/day (P < .05), independent of sedentary status. The percentage of patients with controlled asthma was higher in the active/sedentary (43.9%) and active/nonsedentary (43.8%) groups than in the inactive/sedentary (25.4%) and inactive/nonsedentary (23.9%) groups (P < .02). The likelihood of having uncontrolled asthma according to the treatable traits of physical inactivity (odds ratio [95% confidence interval]: 2.36 [1.55-3.59]), higher anxiety (2.26 [1.49-3.42]), and depression symptoms (1.95 [1.28-2.95]) was significant (P ≤ .002). Obesity and sedentary time were not associated with asthma control.

CONCLUSIONS

Our results show that ≥7500 steps/day is associated with better asthma control independent of sedentary time in adults with moderate-to-severe asthma. Physical inactivity, anxiety, and depression symptoms are associated with higher odds of uncontrolled asthma. These results suggest that interventions should mainly focus on increasing physical activity rather than reducing sedentary time.

An update on asthma diagnosis

OBJECTIVE

Asthma imposes a significant health and socioeconomic burden with an average prevalence impacting 5-10% of the global population. The aim of this narrative review is to update the current literature on topics related to asthma diagnosis.

DATA SOURCES

Original research articles were identified from PubMed using the search terms "asthma diagnosis" and "asthma misdiagnosis".

STUDY SELECTIONS

Recently published articles (n = 51) detailing the diagnosis, misdiagnosis of asthma, and the updated recommendations of the European and international asthma guidelines.

RESULTS

Emerging evidence revealed that asthma might represent a rather heterogenous clinical entity with varying underlying molecular mechanisms. Attempts have been made to unravel these traits to better provide accurate diagnosis and a more efficient patient-based management approach. The lack of a gold standard test for asthma diagnosis has contributed to its over- and underdiagnosis. This is problematic, given that overdiagnosis might lead to delay of both diagnosis and prompt treatment of other diseases, while underdiagnosis might substantially impact quality of life due to progression of asthma by increased rate of exacerbations and airway remodeling. In addition to poor asthma control and potential patient harm, asthma misdiagnosis is also associated with excessive costs. As a result, current international guidelines emphasize the need for a standardized approach to diagnosis, including objective measurements prior to treatment.

CONCLUSION

Future research is warranted to define the optimal diagnostic and treatable traits approach especially for patients with severe asthma, as they may benefit from the advent of newly targeted asthma management.

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.

Cluster Analysis of Finnish Population-Based Adult-Onset Asthma Patients

BACKGROUND

Phenotypes of adult asthma have been identified in previous studies but rarely in population-based settings.

OBJECTIVE

To identify clusters of adult-onset asthma in a Finnish population-based study on subjects born before 1967.

METHODS

We used population-based data from 1350 asthmatics with adult-onset asthma (Adult Asthma in Finland) from Finnish national registers. Twenty-eight covariates were selected based on literature. The number of covariates was reduced by using factor analysis before cluster analysis.

RESULTS

Five clusters (CLU1-CLU5) were identified, 3 clusters with late-onset adult asthma (onset ≥40 years) and 2 clusters with onset at earlier adulthood (<40 years). Subjects in CLU1 (n = 666) had late-onset asthma and were nonobese, symptomatic, and predominantly female with few respiratory infections during childhood. CLU2 (n = 36) consisted of subjects who had earlier-onset asthma, were predominantly female, obese with allergic asthma, and had recurrent respiratory infections. Subjects in CLU3 (n = 75) were nonobese, older, and predominantly men with late-onset asthma, smoking history, comorbidities, severe asthma, least allergic diseases, low education, many siblings, and childhood in rural areas. CLU4 (n = 218) was a late-onset cluster consisting of obese females with comorbidities, asthma symptoms, and low education level. Subjects in CLU5 (n = 260) had earlier onset asthma, were nonobese, and predominantly allergic females.

CONCLUSIONS

Our population-based adult-onset asthma clusters take into account several critical factors such as obesity and smoking, and identified clusters that partially overlap with clusters identified in clinical settings. Results give us a more profound understanding of adult-onset asthma phenotypes and support personalized management.

Real-world impact of dupilumab on asthma disease burden in Japan: The CROSSROAD study

BACKGROUND

Dupilumab, a human monoclonal anti-interleukin (IL)-4Ra antibody blocks the shared receptor component of IL-4 and IL-13, drivers of type 2 inflammation. Dupilumab is approved for severe/refractory asthma inadequately controlled by existing therapies, but knowledge of its effect on real-world disease burden is lacking. This study investigates real-world effects of dupilumab on asthma exacerbation risk and oral corticosteroid (OCS) use in Japanese individuals with asthma.

METHODS

This retrospective, cohort study used a Japanese insurance claims database to identify patients who started dupilumab between 26 March 2019-31 May 2020. Patients were followed for ±365 days from dupilumab initiation. The study primarily assessed the annual incidence rate of severe asthma exacerbations occurring simultaneously with hospitalizations or OCS bursts. Secondary and exploratory endpoints assessed OCS dosage and duration, and healthcare resource utilization (HRU), respectively.

RESULTS

At dupilumab initiation (N = 215), mean age was 57.2 years, 41.9% of patients were aged ≥65 years, and 59.5% were female. Dupilumab significantly reduced the annual incidence of severe asthma exacerbations from 1.29 to 0.74 (95% confidence interval, 0.44-0.76) per patient per year. Mean OCS dosage decreased from 10.4 to 7.2 mg/day in chronic OCS users; median frequency of OCS bursts decreased from 3 to 0. Both unscheduled outpatient visits (35.8% vs 29.8%) and hospitalizations (21.9% vs 12.1%) decreased. Mean (standard deviation) duration of hospitalization also decreased from 6.7 (27.6) to 2.2 (8.1) days.

CONCLUSIONS

Japanese patients with asthma who received dupilumab had reduced incidence rates of severe asthma exacerbations, OCS use, and HRU over 12 months.

Typologies of Canadian young adults who drive after cannabis use: A two-step cluster analysis

Young adults that drive after cannabis use (DACU) may not share all the same characteristics. This study aimed to identify typologies of Canadians who engage in DACU. About 910 cannabis users with a driver's license (17-35 years old) who have engaged in DACU completed an online questionnaire. Two-step cluster analysis identified four subgroups, based on driving-related behaviors, cannabis use and related problems, and psychological distress. Complementary comparative analysis among the identified subgroups was performed as external validation. The identified subgroups were: (1) frequent cannabis users who regularly DACU; (2) individuals with generalized deviance with diverse risky road behaviors and high levels of psychological distress; (3) alcohol and drug-impaired drivers who were also heavy frequent drinkers; and (4) well-adjusted youths with mild depressive-anxious symptoms. Individuals who engaged in DACU were not a homogenous group. When required, prevention and treatment need to be tailored according to the different profiles.