Literature DB >> 30846467

Inadequate assessment of adherence to maintenance medication leads to loss of power and increased costs in trials of severe asthma therapy: results from a systematic literature review and modelling study.

Matshediso C Mokoka1, Melissa J McDonnell2, Elaine MacHale1, Breda Cushen1, Fiona Boland3, Sarah Cormican2, Christina Doherty4, Frank Doyle5, Richard W Costello6, Garrett Greene7.   

Abstract

Adherence to inhaled maintenance therapy in severe asthma is rarely adequately assessed, and its influence on trial outcomes is unknown. We systematically determined how adherence to maintenance therapy is assessed in clinical trials of "add-on" therapy for severe asthma. We model the improvement in trial power that could be achieved by accurately assessing adherence.A systematic search of six major databases identified randomised trials of add-on therapy for severe asthma. The relationship between measuring adherence and study outcomes was assessed. An estimate of potential improvements in statistical power and sample size was derived using digitally recorded adherence trial data.87 randomised controlled trials enrolling 22 173 participants were included. Adherence assessment was not reported in 67 trials (n=13 931, 63%). Studies that reported adherence used a range of self-report and subjective methods. None of the studies employed an objective assessment of adherence. Studies that reported adherence had a significantly reduced pooled variance in forced expiratory volume in 1 s (FEV1) compared to those that did not assess adherence: s2=0.144 L2 versus s2=0.168 L2, p<0.0001. Power to detect clinically relevant changes in FEV1 was significantly higher in trials that reported adherence assessment (mean power achieved 59% versus 49%). Modelling suggests that up to 50% of variance in FEV1 outcomes is attributable to undetected variations in adherence. Controlling for such variations could potentially halve the required sample size.Few trials of add-on therapy monitor adherence to maintenance inhaled therapy, resulting in a greater variance in trial outcomes and inadequate power for determining efficacy.
Copyright ©ERS 2019.

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Year:  2019        PMID: 30846467     DOI: 10.1183/13993003.02161-2018

Source DB:  PubMed          Journal:  Eur Respir J        ISSN: 0903-1936            Impact factor:   16.671


  6 in total

1.  Mobile Health and Inhaler-Based Monitoring Devices for Asthma Management.

Authors:  Blanca E Himes; Lena Leszinsky; Ryan Walsh; Hannah Hepner; Ann Chen Wu
Journal:  J Allergy Clin Immunol Pract       Date:  2019 Nov - Dec

Review 2.  Digital Inhalers for Asthma or Chronic Obstructive Pulmonary Disease: A Scientific Perspective.

Authors:  Amy H Y Chan; Roy A Pleasants; Rajiv Dhand; Stephen L Tilley; Stephen A Schworer; Richard W Costello; Rajan Merchant
Journal:  Pulm Ther       Date:  2021-08-11

Review 3.  Targeting TSLP in Asthma.

Authors:  Jane R Parnes; Nestor A Molfino; Gene Colice; Ubaldo Martin; Jonathan Corren; Andrew Menzies-Gow
Journal:  J Asthma Allergy       Date:  2022-06-03

4.  Smart Medication Adherence Monitoring in Clinical Drug Trials: A Prerequisite for Personalised Medicine?

Authors:  Tanja R Zijp; Peter G M Mol; Daan J Touw; Job F M van Boven
Journal:  EClinicalMedicine       Date:  2019-08-27

5.  Severe Adult Asthmas: Integrating Clinical Features, Biology, and Therapeutics to Improve Outcomes.

Authors:  Sally E Wenzel
Journal:  Am J Respir Crit Care Med       Date:  2021-04-01       Impact factor: 21.405

Review 6.  Global burden of medication non-adherence in chronic obstructive pulmonary disease (COPD) and asthma: a narrative review of the clinical and economic case for smart inhalers.

Authors:  Evalyne M Jansen; Susanne J van de Hei; Boudewijn J H Dierick; Huib A M Kerstjens; Janwillem W H Kocks; Job F M van Boven
Journal:  J Thorac Dis       Date:  2021-06       Impact factor: 2.895

  6 in total

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