Original Article
Treatable Traits That Predict Health Status and Treatment Response in Airway Disease

https://doi.org/10.1016/j.jaip.2020.09.046Get rights and content

Background

A strategy based on the assessment and management of treatable traits (TTs) has been proposed as a new paradigm in airway disease. There is a potentially long list of TTs with likely different clinical impact.

Objective

To identify TTs most strongly associated with poorer health-related quality of life (HRQOL) and treatments that most substantially improved HRQOL.

Methods

We pooled data from 2 parallel-group clinical trials of multidimensional assessment and individualized management targeted to TTs versus usual care in patients with chronic obstructive pulmonary disease or severe asthma (intervention N = 45; control N = 46). Following multidimensional assessment, 22 TTs were identified and the intervention group received treatments tailored to their identified TT. We used Bayesian Model Averaging to examine associations between TTs and HRQOL (St George's Respiratory Questionnaire) at baseline, as well as between each TT treatment and the observed change in HRQOL postintervention.

Results

TTs most substantially associated with poorer baseline HRQOL were frequent chest infections, breathing pattern disorder, inadequate inhaler technique, systemic inflammation (C-reactive protein >3 mg/L), and depression. In both trials, TT treatment led to a large, significant improvement in HRQOL compared with usual care (Cohen's d = 1.19; P < .001). Receiving a statin for systemic inflammation and oral corticosteroid for eosinophilic airway inflammation was associated with the largest HRQOL improvements. Treatments for exercise intolerance, anxiety, and obesity were associated with smaller improvements in HRQOL.

Conclusions

This study contributes to identifying clinically impactful TTs by showing that TTs across pulmonary, extrapulmonary, and behavioral domains were associated with HRQOL impairment and treatment response.

Introduction

A strategy based on “treatable traits” (TTs) is proposed as a new paradigm for airway disease management, whereby certain characteristics (TTs) are systematically assessed within the pulmonary, extrapulmonary, and behavioral/risk-factor domains and treatment is targeted to these characteristics.1,2 TTs are defined as phenotypic or endotypic characteristics that are clinically relevant, identifiable, and modifiable with treatment.1,2 There are many potential TTs in patients with chronic airway diseases such as severe asthma and chronic obstructive pulmonary disease (COPD).1,2 Given the heterogeneous and complex clinical presentation of these patients with frequent multimorbidity, as well as the finite time and resources available for their diagnosis and care, identifying the TTs and their treatments that provide “bang for our buck” in terms of health impact is of paramount importance.

Few studies have been conducted assessing a TT approach in the management of chronic airway disease. The first controlled clinical trial in COPD showed a large effect on health-related quality of life (HRQOL) with a 14-unit change in St George's Respiratory Questionnaire (SGRQ) score for the TT intervention group, a change 3.5 times the minimal clinically important difference.3 A randomized controlled trial (RCT) in patients with severe asthma confirmed the efficacy of targeting treatments to assessed TT, improving the SGRQ score by 13 units.4

Because the TT approach requires multidimensional assessment and intervention, a natural question that arises in relation to TTs, multicomponent individualized management interventions, or other similar care bundles is “which aspects lead to the effect.” Although this question could be answered with multiple RCTs that test different components individually and in combination against all other options, this is not feasible with complex models of care, such as TTs. An alternative approach is to use regression analysis to obtain indirect observational evidence of associations between components of management and change in clinically relevant outcomes. This, however, has several computational challenges in a conventional regression approach, including having more predictors than data points for model estimation and collinearity between predictors. Modeling techniques such as Bayesian Model Averaging5 can overcome these challenges.

To gain an understanding of which TTs and targeted treatments have the greatest impact on HRQOL in patients with chronic airway disease, this study sought to apply a novel Bayesian Model Averaging technique to analyze data from 2 trials of TTs that involved multidimensional assessment and individualized management in COPD3 and severe asthma.4 The specific aims were to evaluate: (1) which combination of TTs identified during multidimensional assessment were associated with worse HRQOL impairment at baseline and (2) which combination of treatments delivered during individualized management based on TTs were associated with the greatest HRQOL change over time.

Section snippets

Participants

Participants from 2 previously reported parallel-group clinical trials of multidimensional assessment and individualized management versus a usual care control group for airway diseases were pooled for this analysis: a nonrandomized controlled clinical trial in COPD (N = 17 intervention arm; N = 19 control arm)3 and an RCT in severe asthma (N = 28 intervention arm; N = 27 control arm).4 Participants were recruited from the respiratory ambulatory care clinic at the John Hunter Hospital

Characteristics of participants and prevalence of TTs

Compared with participants in the severe asthma trial, patients with COPD were older (mean, 70.9 years vs 52.2 years), more often males (50% vs 35%), and had more severe airflow limitation (51.2 vs 73.2 postbronchodilator FEV1% predicted) (Table II). Baseline HRQOL was poor in both groups (SGRQ scores 53.5 in COPD and 49.1 in severe asthma). Of the 22 TTs considered in the current study (Table I), participants in the COPD trial expressed a median of 8.5 TTs (range, 4-13) and participants in the

Discussion

This study identifies what specific TTs are associated with HRQOL and treatment response in patients with chronic airway diseases (COPD and severe asthma). By applying Bayesian Model Averaging5 to data from 2 previously reported clinical trials,3,4 our results show that (1) frequent chest infections, dysfunctional breathing/breathing pattern disorder, inadequate inhaler technique, systemic inflammation, and depression had large and reliable associations with HRQOL; (2) treating systemic

Conclusions

This study demonstrated that a combination of TTs across the pulmonary, extrapulmonary, and behavioral domains was associated with HRQOL and, when treated, was associated with HRQOL improvement. Although these findings raise questions regarding why particular TTs were associated with outcomes and not others, they also identify a list of TTs that might be worth examining further in future efficacy, effectiveness, and implementation trials. Likewise, questions remain as to how to implement a TT

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    This work was supported by a grant from GlaxoSmithKline.

    Conflicts of interest: S. A. Hiles' salary was supported by a grant from GlaxoSmithKline paid to her institution (University of Newcastle) during the conduct of the study and AstraZeneca outside the study. P. G. Gibson reports personal fees from AstraZeneca, GlaxoSmithKline, and Novartis, and grants from AstraZeneca and GlaxoSmithKline outside the submitted work. A. Agusti reports grants and personal fees from AstraZeneca, GlaxoSmithKline, and Menarini outside the submitted work and personal fees from Chiesi. V. M. McDonald reports grants from GlaxoSmithKline during the conduct of the study; grants and personal fees from GlaxoSmithKline and AstraZeneca; and personal fees from Menarini outside the submitted work. The COPD clinical trial was originally funded by a National Health and Medical Research Council PhD scholarship and the John Hunter Hospital Charitable Trust Research Scheme. The severe asthma clinical trial was originally funded by the National Health and Medical Research Council, Hunter Medical Research Institute, University of Newcastle, John Hunter Hospital Charitable Trust.

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