Treatable traits, combination inhaler therapy and the future of asthma management

Integrating hot topics and implementation of treatable traits in asthma

People with asthma experience many different problems related to their illness. The number and type of problems differ between patients. This results in asthma being a complex and heterogeneous disorder which mandates a personalised approach to management. These features pose very significant challenges for the effective implementation of evidence-based management. "Treatable traits" is a model of care that has been specifically designed to address these issues. Traits are identified in the pulmonary, extrapulmonary (comorbidity) and behavioural/risk factor domains. Traits are clinically relevant, recognisable with validated trait identification markers and treatable using evidence-based therapies. The clinician and patient agree on a personalised management plan that addresses the relevant traits, and trials show superiority of this approach with significant improvements in asthma control and quality of life. A number of tools have now been developed to assist the clinician in the implementation of this approach. The success of the treatable traits model of care is now being realised in other disease areas.

Factors influencing the initiation of biologic therapy in children with severe asthma: Results of the pediatric asthma noninvasive diagnostic approaches (PANDA) study

BACKGROUND & OBJECTIVES

Despite the availability of biologics for severe pediatric asthma, real-life studies reporting on drivers behind initiating biologics and their alignment with the Global Initiative for Asthma (GINA) recommendations are lacking.

METHODS

We performed analysis within the pediatric asthma noninvasive diagnostic approaches study, a prospective cohort of 6- to 17-year-old children with severe asthma. Information was collected on demographic factors, symptom control, treatment, comorbidities, and diagnostic tests from medical records and questionnaires. We divided patients into "starters" or "nonstarters" based on the clinical decision to initiate biologics and performed multivariate logistic regression analysis to identify drivers behind initiating therapy. Additionally, we assessed patient suitability for biologics according to key factors in the GINA recommendations: Type 2 inflammation, frequency of exacerbations, and optimization of treatment adherence.

RESULTS

In total, 72 children (mean age 11.5 ± 3.0 years, 65.3% male) were included (13 starters). Initiation of biologics was associated with a higher GINA treatment step (adjusted odds ratio's [aOR] = 5.0, 95%CI 1.33-18.76), steroid toxicity (aOR = 21.1, 95%CI 3.73-119.91), frequency of exacerbations (aOR = 1.6, 95%CI 1.10-2.39), improved therapy adherence (aOR = 1.7, 95%CI 1.10-2.46), Caucasian ethnicity (aOR = 0.20, 95%CI 0.05-0.80), ≥1 allergic sensitization (aOR = 0.06, 95%CI 0.004-0.97), and allergic rhinitis (aOR = 0.13, 95%CI 0.03-0.65). Furthermore, steroid toxicity was identified as an important factor for deviation from the current recommendations on biologic prescription.

CONCLUSIONS

We identified multiple drivers and inhibitors for initiating biologics, and showed the clinical need for biologics in severe pediatric asthmatics suffering from steroid toxicity. These findings may help refine asthma management guidelines.

Parental Perceptions and Knowledge towards Asthma Action Plans

INTRODUCTION

Personalised Asthma Action Plans (AAPs) are a cornerstone of paediatric asthma management. We aimed to assess parental knowledge and perceptions of the use of AAPs in managing their child's asthma.

METHODS

In 2023 we conducted a cross-sectional survey to assess knowledge and perceptions of AAPs in parents/caregivers of children aged 0-17 years with asthma who had an AAP and attended respiratory clinics at Sydney Children's Hospital, Randwick. The questionnaire included parental perceptions on the utility of AAPs, parental asthma management self-efficacy (PAMSE), and an adapted version of the Asthma Action Plan Knowledge Interview tool (AAPKI). Descriptive statistics and regression analyses were used to assess associations between AAPKI, PAMSE and other outcomes.

RESULTS

Seventy-one parents completed the survey. Mean PAMSE score was 4.2/5 (SD 0.5). Median AAPKI score was 70.2% out of 100% (IQR 20.4%). Most parents (80.0%, n = 56) were satisfied/very satisfied with using AAPs to manage their child's asthma. Most agreed/strongly agreed that AAPs were helpful in knowing when to seek medical attention for their child's asthma (81.7%, n = 58), helped their understanding of asthma severity (81.7%, n = 58), and increased their confidence in asthma management (76.0%, n = 54). Over half of parents (55.7%, n = 39) would prefer AAPs digitally and majority (78.6%, n = 55) would like colour-coded AAPs.

CONCLUSION

High levels of AAP knowledge and self-efficacy scores were demonstrated. Most parents expressed understanding of the benefits of AAPs. Our findings suggest parents of children with asthma prefer AAPs in colour and in digital version which may help guide standardisation of AAPs across Australia.

Contemporary Concise Review 2023: Asthma

Asthma research and management needs to meet the priorities of the end user-patients, carers and clinicians. A better understanding of the natural history of asthma and the progression of disease has highlighted the importance of early identification of patients with asthma and the potential role of early intervention. Management of mild asthma requires a consistent approach with the same detail and consideration used when managing severe disease. Evidence around treatable traits approaches continues to evolve, supporting the role of a personalized medicine in asthma. Oral corticosteroid (OCS) stewardship continues to be an urgent issue in asthma management. Strategies to taper OCS doses and the implementation of biologic therapies for their steroid sparing benefits will be important steps to address this problem. The concept of remission in asthma provides an ambitious target and treatment outcome.

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient's characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient's ICS response in the next year based on the patient's characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.

Treatable traits models of care

Treatable traits is a personalized approach to the management of respiratory disease. The approach involves a multidimensional assessment to understand the traits present in individual patients. Traits are phenotypic and endotypic characteristics that can be identified, are clinically relevant and can be successfully treated by therapy to improve clinical outcomes. Identification of traits is followed by individualized and targeted treatment to those traits. First proposed for the management of asthma and chronic obstructive pulmonary disease (COPD) the approach is recommended in many other areas of respiratory and now immunology medicine. Models of care for treatable traits have been proposed in different diseases and health care setting. In asthma and COPD traits are identified in three domains including pulmonary, extrapulmonary and behavioural/lifestyle/risk-factors. In bronchiectasis and interstitial lung disease, a fourth domain of aetiological traits has been proposed. As the core of treatable traits is personalized and individualized medicine; there are several key aspects to treatable traits models of care that should be considered in the delivery of care. These include person centredness, consideration of patients' values, needs and preferences, health literacy and engagement. We review the models of care that have been proposed and provide guidance on the engagement of patients in this approach to care.