Asthma control cost-utility randomized trial evaluation (ACCURATE): the goals of asthma treatment

Contains fulltext : 97659.pdf (publisher's version ) (Open Access)ABSTRACT: BACKGROUND: Despite the availability of effective therapies, asthma remains a source of significant morbidity and use of health care resources. The central research question of the ACCURATE trial is whether maximal doses of (combination) therapy should be used for long periods in an attempt to achieve complete control of all features of asthma. An additional question is whether patients and society value the potential incremental benefit, if any, sufficiently to concur with such a treatment approach. We assessed patient preferences and cost-effectiveness of three treatment strategies aimed at achieving different levels of clinical control: 1. sufficiently controlled asthma 2. strictly controlled asthma 3. strictly controlled asthma based on exhaled nitric oxide as an additional disease marker DESIGN: 720 Patients with mild to moderate persistent asthma from general practices with a practice nurse, age 18-50 yr, daily treatment with inhaled corticosteroids (more then 3 months usage of inhaled corticosteroids in the previous year), will be identified via patient registries of general practices in the Leiden, Nijmegen, and Amsterdam areas in The Netherlands. The design is a 12-month cluster-randomised parallel trial with 40 general practices in each of the three arms. The patients will visit the general practice at baseline, 3, 6, 9, and 12 months. At each planned and unplanned visit to the general practice treatment will be adjusted with support of an internet-based asthma monitoring system supervised by a central coordinating specialist nurse. Patient preferences and utilities will be assessed by questionnaire and interview. Data on asthma control, treatment step, adherence to treatment, utilities and costs will be obtained every 3 months and at each unplanned visit. Differences in societal costs (medication, other (health) care and productivity) will be compared to differences in the number of limited activity days and in quality adjusted life years (Dutch EQ5D, SF6D, e-TTO, VAS). This is the first study to assess patient preferences and cost-effectiveness of asthma treatment strategies driven by different target levels of asthma control. Trial registration: Netherlands Trial Registration NTR1756

Identifying patients at risk for severe exacerbations of asthma: development and external validation of a multivariable prediction model

Preventing exacerbations of asthma is a major goal in current guidelines. We aimed to develop a prediction model enabling practitioners to identify patients at risk of severe exacerbations who could potentially benefit from a change in management. We used data from a 12-month primary care pragmatic trial; candidate predictors were identified from GINA 2014 and selected with a multivariable bootstrapping procedure. Three models were constructed, based on: (1) history, (2) history+spirometry and (3) history+spirometry+FeNO. Final models were corrected for overoptimism by shrinking the regression coefficients; predictive performance was assessed by the area under the receiver operating characteristic curve (AUROC) and Hosmer-Lemeshow test. Models were externally validated in a data set including patients with severe asthma (Unbiased BIOmarkers in PREDiction of respiratory disease outcomes). 80/611 (13.1%) participants experienced ≥1 severe exacerbation. Five predictors (Asthma Control Questionnaire score, current smoking, chronic sinusitis, previous hospital admission for asthma and ≥1 severe exacerbation in the previous year) were retained in the history model (AUROC 0.77 (95% CI 0.75 to 0.80); Hosmer-Lemeshow p value 0.35). Adding spirometry and FeNO subsequently improved discrimination slightly (AUROC 0.79 (95% CI 0.77 to 0.81) and 0.80 (95% CI 0.78 to 0.81), respectively). External validation yielded AUROCs of 0.72 (95% CI 0.70 to 0.73; 71 to 0.74 and 0.71 to 0.73) for the three models, respectively; calibration was best for the spirometry model. A simple history-based model extended with spirometry identifies patients who are prone to asthma exacerbations. The additional value of FeNO is modest. These models merit an implementation study in clinical practice to assess their utility. NTR 175

Exacerbations in adults with asthma : A systematic review and external validation of prediction models

BACKGROUND: Several prediction models assessing future risk of exacerbations in adult patients with asthma have been published. Applicability of these models is uncertain because their predictive performance has often not been assessed beyond the population in which they were derived. OBJECTIVE: This study aimed to identify and critically appraise prediction models for asthma exacerbations and validate them in two clinically distinct populations. METHODS: PubMed and EMBASE were searched to April 2017 for reports describing adult asthma populations in which multivariable models were constructed to predict exacerbations during any time frame. After critical appraisal, the models͛ predictive performances were assessed in a primary and a secondary care population for: author-defined exacerbations and for ATS/ERS-defined severe exacerbations. RESULTS: We found 12 reports from which 24 prediction models were evaluated. Three predictors (previous healthcare-utilisation, symptoms, and spirometry values) were retained in most models. Assessment was hampered by sub-optimal methodology and reporting, and by differences in exacerbation outcomes. Discrimination (AUROC) of models for author-defined exacerbations was better in the primary care population (mean 0.71) than in the secondary care population (mean 0.60); and similar (0.65 and 0.62 respectively) for ATS/ERS defined severe exacerbations. Model calibration was generally poor, but consistent between the two populations. CONCLUSION: The preservation of three predictors in models derived from variable populations and the fairly consistent predictive properties of most models in two distinct validation populations suggest the feasibility of a generalizable model predicting severe exacerbations. Nevertheless, improvement of the models is warranted as predictive performances are below the desired level