Persijn J. Honkoop

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

Background 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. Methods 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). Results 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. Conclusions 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. Trial registration number NTR 1756.

Early detection of asthma exacerbations by using action points in self-management plans

Our aim was to validate optimal action points in written action plans for early detection of asthma exacerbations. We analysed daily symptoms and morning peak expiratory flows (PEFs) from two previous studies. Potential action points were based on analysis of symptom scores (standard deviations) percentage of personal best PEF, PEF variability in relation to a run-in period or combinations of these measures. Sensitivity and specificity for predicting exacerbations were obtained for each action point. The numbers needed to treat to prevent one exacerbation and the time interval between reaching action point criteria and the start of the exacerbation were calculated. Based on these parameters, the optimal action points for symptoms, PEF and PEF plus symptoms were determined, and their performance compared with published guidelines’ action points. The optimal action points were, for symptoms, statistical variability (standard deviations) and, for PEF, <70% of personal best. The combination of PEF plus symptoms performed best, with improved specificity and earlier detection. The main benefits associated with using these action points was to reduce false positive rates for detecting exacerbations. Early detection of asthma exacerbations can be improved using a composite action point comprising symptoms and PEF measurements over 1 week.

Perspectives of patients and healthcare professionals on mHealth for asthma self-management

Mobile healthcare (mHealth) has the potential to revolutionise the self-management of long-term medical conditions such as asthma. A user-centred design is integral if mHealth is to be embraced by patients and healthcare professionals. The aim of this study was to determine the perspectives of individuals with asthma and healthcare professionals on the use of mHealth for asthma self-management. We used a sequential exploratory mixed methods design; focus groups informed the development of questionnaires, which were disseminated to individuals with asthma and healthcare professionals. Focus group participants (18 asthma patients and five healthcare professionals) identified 12 potential uses of mHealth. Questionnaire results showed that individuals with asthma (n=186) most frequently requested an mHealth system to monitor asthma over time (72%) and to collect data to present to healthcare teams (70%). In contrast, healthcare professionals (n=63) most frequently selected a system alerting patients to deteriorating asthma control (86%) and advising them when to seek medical attention (87%). Individuals with asthma were less likely than healthcare professionals (p<0.001) to believe that assessing medication adherence and inhaler technique could improve asthma control. Our data provide strong support for mHealth for asthma self-management, but highlight fundamental differences between the perspectives of patients and healthcare professionals. People with asthma and healthcare professionals strongly support the utility of mHealth for asthma self-management http://ow.ly/wyJC3096f4e

MyAirCoach: the use of home-monitoring and mHealth systems to predict deterioration in asthma control and the occurrence of asthma exacerbations; study protocol of an observational study.

Introduction Asthma is a variable lung condition whereby patients experience periods of controlled and uncontrolled asthma symptoms. Patients who experience prolonged periods of uncontrolled asthma have a higher incidence of exacerbations and increased morbidity and mortality rates. The ability to determine and to predict levels of asthma control and the occurrence of exacerbations is crucial in asthma management. Therefore, we aimed to determine to what extent physiological, behavioural and environmental data, obtained by mobile healthcare (mHealth) and home-monitoring sensors, as well as patient characteristics, can be used to predict episodes of uncontrolled asthma and the onset of asthma exacerbations. Methods and analysis In an 1-year observational study, patients will be provided with mHealth and home-monitoring systems to record daily measurements for the first-month (phase I) and weekly measurements during a follow-up period of 11 months (phase II). Our study population consists of 150 patients, aged ≥18 years, with a clinicians diagnosis of asthma, currently on controller medication, with uncontrolled asthma and/or minimally one exacerbation in the past 12 months. They will be enrolled over three participating centres, including Leiden, London and Manchester. Our main outcomes are the association between physiological, behavioural and environmental data and (1) the loss of asthma control and (2) the occurrence of asthma exacerbations. Ethics This study was approved by the Medical Ethics Committee of the Leiden University Medical Center in the Netherlands and by the NHS ethics service in the UK. Trial registration number NCT02774772.

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

BackgroundDespite 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 asthma2. strictly controlled asthma3. strictly controlled asthma based on exhaled nitric oxide as an additional disease markerDesign720 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 registrationNetherlands Trial Register (NTR): NTR1756

Comparison between an online self-administered and an interviewer-administered version of the Asthma Control Questionnaire: a cross-sectional validation study.

Background: Online self-management programmes for asthma have recently become available. International guidelines suggest that the Asthma Control Questionnaire (ACQ) can be used in these programmes. In order to assess the current level of control and guide therapy, the same cut-off values are being used as in conventional asthma management. However, results might differ between different types of administration of the ACQ. Aims: To assess the agreement between an online self-administered version of the ACQ and an interviewer-administered version at a routine visit. Methods: Cross-sectional data from primary care asthma patients in the Asthma Control Cost Utility Randomized Trial Evaluation (ACCURATE) trial aged 18–50 years and prescribed inhaled steroids were analysed. We selected patients who self-administered an ACQ online and subsequently had an ACQ completed by a nurse practitioner within 7 days at a trial-related control visit. ACQ scores were calculated and agreement assessed by paired t-tests, Pearsons correlation coefficient and a Bland-Altman plot. Results: A total of 351 patients were eligible (68% female, mean age 40 years). The time interval between the two versions was 3.2 days. There was a significant difference of 0.14 (95% CI 0.09 to 0.20; p<0.001) between the results of the online self-administered ACQ (mean 1.04±0.04) and the interviewer-administered ACQ results (0.90±0.04). The Pearson correlation coefficient was 0.79. The limits of agreement (−0.86, 1.14) exceeded the predefined minimal clinically important difference between results (±0.5). The Bland-Altman plot therefore showed insufficient agreement. Conclusions: Assessment of asthma control by the ACQ is influenced by the type of administration. Our results suggest that better control of asthma is perceived when interacting with a caregiver than by online self-assessment.

Adaptation of a difficult-to-manage asthma programme for implementation in the Dutch context: a modified e-Delphi

Patients with difficult-to-manage asthma represent a heterogeneous subgroup of asthma patients who require extensive assessment and tailored management. The International Primary Care Respiratory Group approach emphasises the importance of differentiating patients with asthma that is difficult to manage from those with severe disease. Local adaptation of this approach, however, is required to ensure an appropriate strategy for implementation in the Dutch context. We used a modified three-round e-Delphi approach to assess the opinion of all relevant stakeholders (general practitioners, pulmonologists, practice nurses, pulmonary nurses and people with asthma). In the first round, the participants were asked to provide potentially relevant items for a difficult-to-manage asthma programme, which resulted in 67 items. In the second round, we asked participants to rate the relevance of specific items on a seven-point Likert scale, and 46 items were selected as relevant. In the third round, the selected items were categorised and items were ranked within the categories according to relevance. Finally, we created the alphabet acronym for the categories ‘the A–I of difficult-to-manage asthma’ to resonate with an established Dutch ‘A–E acronym for determining asthma control’. This should facilitate implementation of this programme within the existing structure of educational material on asthma and chronic obstructive pulmonary disease (COPD) in primary care, with potential for improving management of difficult-to-manage asthma. Other countries could use a similar approach to create a locally adapted version of such a programme.

Assessing machine learning algorithms for self-management of asthma

Control and monitoring of asthma progress is highly important for patients quality of life and healthcare management. Emerging tools for self-management of various chronic diseases have the potential to support personalized patient guidance. This work presents the design aspects of the myAirCoach decision support system, with focus on the assessment of three machine learning approaches as support tools the first prototype implementation.

The number needed to treat provides additional insight on the performance of detection points of asthma exacerbations in self-management plans

With interest we have read the paper of Kupczyk et al .1 It is an important contribution to the current understanding of the course and early detection of asthma exacerbations, especially with the advent of internet-based self-management programmes using daily or weekly monitoring.2 In the current research the authors found that the combination of a 20% decrease in peak expiratory flow (PEF) on 2 consecutive days or a 20% increase in day symptoms on 2 consecutive days optimally detects an exacerbation with …