Long-Term Cost-Effectiveness of Digital Inhaler Adherence Technologies in Difficult-to-Treat Asthma.

BACKGROUND: Digital inhalers can monitor inhaler usage, support difficult-to-treat asthma management, and inform step-up treatment decisions yet their economic value is unknown, hampering wide-scale implementation. OBJECTIVE: We aimed to assess the long-term cost-effectiveness of digital inhaler-based medication adherence management in difficult-to-treat asthma. METHODS: A model-based cost-utility analysis was performed. The Markov model structure was determined by biological and clinical understanding of asthma and was further informed by guideline-based assessment of model development. Internal and external validation was performed using the Assessment of the Validation Status of Health-Economic (AdViSHE) tool. The INCA (Inhaler Compliance Assessment) Sun randomized clinical trial data were incorporated into the model to evaluate the cost-effectiveness of digital inhalers. Several long-term clinical case scenarios were assessed (reduced number of exacerbations, increased asthma control, introduction of biosimilars [25% price-cut on biologics]). RESULTS: The long-term modelled cost-effectiveness based on a societal perspective indicated 1-year per-patient costs for digital inhalers and usual care (ie, regular inhalers) of €7,546 ($7,946) and €10,752 ($11,322), respectively, reflecting cost savings of €3,207 ($3,377) for digital inhalers. Using a 10-year intervention duration and time horizon resulted in cost savings of €26,309 ($27,703) for digital inhalers. In the first year, add-on biologic therapies accounted for 69% of the total costs in the usual care group and for 49% in the digital inhaler group. Scenario analyses indicated consistent cost savings ranging from €2,287 ($2,408) (introduction biosimilars) to €4,581 ($4,824) (increased control, decreased exacerbations). CONCLUSIONS: In patients with difficult-to-treat asthma, digital inhaler-based interventions can be cost-saving in the long-term by optimizing medication adherence and inhaler technique and reducing add-on biologic prescriptions.

Domiciliary Fractional Exhaled Nitric Oxide and Spirometry in Monitoring Asthma Control and Exacerbations.

BACKGROUND: Domiciliary measurements of airflow obstruction and inflammation may assist healthcare teams and patients in determining asthma control and facilitate self-management. OBJECTIVE: To evaluate parameters derived from domiciliary spirometry and fractional exhaled nitric oxide (Feno) in monitoring asthma exacerbations and control. METHODS: Patients with asthma were provided with hand-held spirometry and Feno devices in addition to their usual asthma care. Patients were instructed to perform twice-daily measurements for 1 month. Daily symptoms and medication change were reported through a mobile health system. The Asthma Control Questionnaire was completed at the end of the monitoring period. RESULTS: One hundred patients had spirometry, of which 60 were given additional Feno devices. Compliance rates for twice-daily measurements were poor (median [interquartile range], 43% [25%-62%] for spirometry; 30% [3%-48%] for Feno); at least 15% of patients took little or no spirometry measurements and 40% rarely measured Feno. The coefficient of variation (CV) values in FEV1 and Feno were higher, and the mean % personal best FEV1 lower in those who had major exacerbations compared with those without (P < .05). Feno CV and FEV1 CV were associated with asthma exacerbation during the monitoring period (area under the receiver-operating characteristic curve, 0.79 and 0.74, respectively). Higher Feno CV also predicted poorer asthma control (area under the receiver-operating characteristic curve, 0.71) at the end of the monitoring period. CONCLUSIONS: Compliance with domiciliary spirometry and Feno varied widely among patients even in the setting of a research study. However, despite significant missing data, Feno and FEV1 were associated with asthma exacerbations and control, making these measurements potentially clinically valuable if used.

Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations.

Early detection of severe asthma exacerbations through home monitoring data in patients with stable mild-to-moderate chronic asthma could help to timely adjust medication. We evaluated the potential of machine learning methods compared to a clinical rule and logistic regression to predict severe exacerbations. We used daily home monitoring data from two studies in asthma patients (development: n = 165 and validation: n = 101 patients). Two ML models (XGBoost, one class SVM) and a logistic regression model provided predictions based on peak expiratory flow and asthma symptoms. These models were compared with an asthma action plan rule. Severe exacerbations occurred in 0.2% of all daily measurements in the development (154/92,787 days) and validation cohorts (94/40,185 days). The AUC of the best performing XGBoost was 0.85 (0.82-0.87) and 0.88 (0.86-0.90) for logistic regression in the validation cohort. The XGBoost model provided overly extreme risk estimates, whereas the logistic regression underestimated predicted risks. Sensitivity and specificity were better overall for XGBoost and logistic regression compared to one class SVM and the clinical rule. We conclude that ML models did not beat logistic regression in predicting short-term severe asthma exacerbations based on home monitoring data. Clinical application remains challenging in settings with low event incidence and high false alarm rates with high sensitivity.

Palliative medicine Master Classes for primary care: an evaluation.

Introduction General practitioners (GP) are key in the delivery of primary palliative care in the community, so appropriate knowledge and skills are needed. Previous research has suggested there may be gaps in training opportunities. Aims To develop and evaluate a Master Class series presented by a hospice palliative care service to their local GPs and other health-care professionals. A secondary aim was to measure the lasting impacts of the educational intervention. Methods The study setting was the Canterbury region of Aotearoa New Zealand (NZ). GPs and other health-care professionals were surveyed using a research team-designed questionnaire rating self confidence in the nominated learning objectives before and after the Master Classes. The participants were also asked to self-rate the application of the knowledge to their practice 3 years after the Master Class sessions were attended. Results There were 56 sets of questionnaires completed. The participants tended to be experienced female GPs with a range of palliative care patients in their practice. There was significant improvement in most learning outcomes across the Master Classes series. The attendees reported the Master Classes were relevant and clear, with useful resources. The improvements in the learning outcomes were largely sustained over 3 years. Discussion The provision of palliative care education that aligns with clinical practice improves knowledge, skills and confidence in GPs who provide most of the end-of-life care in New Zealand.

A Pharmacy-Based eHealth Intervention Promoting Correct Use of Medication in Patients With Asthma and COPD: Nonrandomized Pre-Post Study.

BACKGROUND: Asthma and chronic obstructive pulmonary disease (COPD) affect millions of people worldwide. While medication can control and improve disease symptoms, incorrect use of medication is a common problem. The eHealth intervention SARA (Service Apothecary Respiratory Advice) aims to improve participants' correct use of inhalation medication by providing information and as-needed tailored follow-up support by a pharmacist. OBJECTIVE: The primary aim of this study was to investigate the effect of SARA on exacerbation rates in participants with asthma and COPD. Secondary aims were to investigate its effects in terms of adherence to maintenance medication and antimycotic treatment. METHODS: In this nonrandomized pre-post study, medication dispensing data from 382 Dutch community pharmacies were included. Exacerbation rates were assessed with dispensed short-course oral corticosteroids. Medication adherence between new and chronic users was assessed by calculating the proportion of days covered from dispensed inhalation maintenance medication. Antimycotic treatment was investigated from dispensed oral antimycotics in participants who were also dispensed inhaled corticosteroids (ICS). Outcomes were assessed 1 year before and 1 year after implementation of SARA and were compared between SARA participants and control participants. More specifically, for exacerbation rates and medication adherence, a difference score was calculated (ie, 1 year after SARA minus 1 year before SARA) and was subsequently compared between the study groups with independent-samples t tests. For antimycotics, the relative number of participants who were dispensed antimycotics was calculated and subsequently analyzed with a mixed-effects logistic regression. RESULTS: The study population comprised 9452 participants, of whom 2400 (25.39%) were SARA participants. The mean age of the population was 60.8 (15.0) years, and approximately two-thirds (n=5677, 60.06%) were female. The results showed an increase in mean exacerbation rates over time for both study groups (SARA: 0.05; control: 0.15). However, this increase in exacerbation rates was significantly lower for SARA participants (t9450=3.10, 95% CI 0.04-0.16; P=.002; Cohen d=0.06). Chronic users of inhalation medication in both study groups showed an increase in mean medication adherence over time (SARA: 6.73; control: 4.48); however, this increase was significantly higher for SARA participants (t5886=-2.74, 95% CI -3.86 to -0.84; P=.01; Cohen d=-0.07). Among new users of inhalation medication, results showed no significant difference in medication adherence between SARA and control participants in the year after implementation of SARA (t1434=-1.85, 95% CI -5.60 to 0.16; P=.06; Cohen d=-0.10). Among ICS users, no significant differences between the study groups were found over time in terms of the proportion of participants who were dispensed antimycotics (t5654=0.29, 95% CI -0.40 to 0.54; P=.76; Cohen d=0). CONCLUSIONS: This study provides preliminary evidence that the SARA eHealth intervention might have the potential to decrease exacerbation rates and improve medication adherence among patients with asthma and COPD.

Integrated disease management interventions for patients with chronic obstructive pulmonary disease.

BACKGROUND: People with chronic obstructive pulmonary disease (COPD) show considerable variation in symptoms, limitations, and well-being; this often complicates medical care. A multi-disciplinary and multi-component programme that addresses different elements of care could improve quality of life (QoL) and exercise tolerance, while reducing the number of exacerbations. OBJECTIVES: To compare the effectiveness of integrated disease management (IDM) programmes versus usual care for people with chronic obstructive pulmonary disease (COPD) in terms of health-related quality of life (QoL), exercise tolerance, and exacerbation-related outcomes. SEARCH METHODS: We searched the Cochrane Airways Group Register of Trials, CENTRAL, MEDLINE, Embase, and CINAHL for potentially eligible studies. Searches were current as of September 2020. SELECTION CRITERIA: Randomised controlled trials (RCTs) that compared IDM programmes for COPD versus usual care were included. Interventions consisted of multi-disciplinary (two or more healthcare providers) and multi-treatment (two or more components) IDM programmes of at least three months' duration. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed trial quality and extracted data. If required, we contacted study authors to request additional data. We performed meta-analyses using random-effects modelling. We carried out sensitivity analyses for the quality of included studies and performed subgroup analyses based on setting, study design, dominant intervention components, and region. MAIN RESULTS: Along with 26 studies included in the 2013 Cochrane Review, we added 26 studies for this update, resulting in 52 studies involving 21,086 participants for inclusion in the meta-analysis. Follow-up periods ranged between 3 and 48 months and were classified as short-term (up to 6 months), medium-term (6 to 15 months), and long-term (longer than 15 months) follow-up. Studies were conducted in 19 different countries. The mean age of included participants was 67 years, and 66% were male. Participants were treated in all types of healthcare settings, including primary (n =15), secondary (n = 22), and tertiary care (n = 5), and combined primary and secondary care (n = 10). Overall, the level of certainty of evidence was moderate to high. We found that IDM probably improves health-related QoL as measured by St. George's Respiratory Questionnaire (SGRQ) total score at medium-term follow-up (mean difference (MD) -3.89, 95% confidence interval (CI) -6.16 to -1.63; 18 RCTs, 4321 participants; moderate-certainty evidence). A comparable effect was observed at short-term follow-up (MD -3.78, 95% CI -6.29 to -1.28; 16 RCTs, 1788 participants). However, the common effect did not exceed the minimum clinically important difference (MCID) of 4 points. There was no significant difference between IDM and control for long-term follow-up and for generic QoL. IDM probably also leads to a large improvement in maximum and functional exercise capacity, as measured by six-minute walking distance (6MWD), at medium-term follow-up (MD 44.69, 95% CI 24.01 to 65.37; 13 studies, 2071 participants; moderate-certainty evidence). The effect exceeded the MCID of 35 metres and was even greater at short-term (MD 52.26, 95% CI 32.39 to 72.74; 17 RCTs, 1390 participants) and long-term (MD 48.83, 95% CI 16.37 to 80.49; 6 RCTs, 7288 participants) follow-up. The number of participants with respiratory-related admissions was reduced from 324 per 1000 participants in the control group to 235 per 1000 participants in the IDM group (odds ratio (OR) 0.64, 95% CI 0.50 to 0.81; 15 RCTs, median follow-up 12 months, 4207 participants; high-certainty evidence). Likewise, IDM probably results in a reduction in emergency department (ED) visits (OR 0.69, 95%CI 0.50 to 0.93; 9 RCTs, median follow-up 12 months, 8791 participants; moderate-certainty evidence), a slight reduction in all-cause hospital admissions (OR 0.75, 95%CI 0.57 to 0.98; 10 RCTs, median follow-up 12 months, 9030 participants; moderate-certainty evidence), and fewer hospital days per person admitted (MD -2.27, 95% CI -3.98 to -0.56; 14 RCTs, median follow-up 12 months, 3563 participants; moderate-certainty evidence). Statistically significant improvement was noted on the Medical Research Council (MRC) Dyspnoea Scale at short- and medium-term follow-up but not at long-term follow-up. No differences between groups were reported for mortality, courses of antibiotics/prednisolone, dyspnoea, and depression and anxiety scores. Subgroup analysis of dominant intervention components and regions of study suggested context- and intervention-specific effects. However, some subgroup analyses were marked by considerable heterogeneity or included few studies. These results should therefore be interpreted with caution. AUTHORS' CONCLUSIONS: This review shows that IDM probably results in improvement in disease-specific QoL, exercise capacity, hospital admissions, and hospital days per person. Future research should evaluate which combination of IDM components and which intervention duration are most effective for IDM programmes, and should consider contextual determinants of implementation and treatment effect, including process-related outcomes, long-term follow-up, and cost-effectiveness analyses.

Reorganisation of GP surgeries during the COVID-19 outbreak: analysis of guidelines from 15 countries.

BACKGROUND: General practitioners (GPs) play a key role in managing the COVID-19 outbreak. However, they may encounter difficulties adapting their practices to the pandemic. We provide here an analysis of guidelines for the reorganisation of GP surgeries during the beginning of the pandemic from 15 countries. METHODS: A network of GPs collaborated together in a three-step process: (i) identification of key recommendations of GP surgery reorganisation, according to WHO, CDC and health professional resources from health care facilities; (ii) collection of key recommendations included in the guidelines published in 15 countries; (iii) analysis, comparison and synthesis of the results. RESULTS: Recommendations for the reorganisation of GP surgeries of four types were identified: (i) reorganisation of GP consultations (cancelation of non-urgent consultations, follow-up via e-consultations), (ii) reorganisation of GP surgeries (area partitioning, visual alerts and signs, strict hygiene measures), (iii) reorganisation of medical examinations by GPs (equipment, hygiene, partial clinical examinations, patient education), (iv) reorganisation of GP staff (equipment, management, meetings, collaboration with the local community). CONCLUSIONS: We provide here an analysis of guidelines for the reorganisation of GP surgeries during the beginning of the COVID-19 outbreak from 15 countries. These guidelines focus principally on clinical care, with less attention paid to staff management, and the area of epidemiological surveillance and research is largely neglected. The differences of guidelines between countries and the difficulty to apply them in routine care, highlight the need of advanced research in primary care. Thereby, primary care would be able to provide recommendations adapted to the real-world settings and with stronger evidence, which is especially necessary during pandemics.

SERIES: eHealth in primary care. Part 4: Addressing the challenges of implementation.

BACKGROUND: The implementation of eHealth applications in primary care remains challenging. Enhancing knowledge and awareness of implementation determinants is critical to build evidence-based implementation strategies and optimise uptake and sustainability. OBJECTIVES: We consider how evidence-based implementation strategies can be built to support eHealth implementation. DISCUSSION: What implementation strategies to consider depends on (potential) barriers and facilitators to eHealth implementation in a given situation. Therefore, we first discuss key barriers and facilitators following the five domains of the Consolidated Framework for Implementation Research (CFIR). Cost is identified as a critical barrier to eHealth implementation. Privacy, security problems, and a lack of recognised standards for eHealth applications also hinder implementation. Engagement of key stakeholders in the implementation process, planning the implementation of the intervention, and the availability of training and support are important facilitators. To support care professionals and researchers, we provide a stepwise approach to develop and apply evidence-based implementation strategies for eHealth in primary care. It includes the following steps: (1) specify the eHealth application, (2) define problem, (3) specify desired implementation behaviour, and (4) choose and (5) evaluate the implementation strategy. To improve the fit of the implementation strategy with the setting, the stepwise approach considers the phase of the implementation process and the specific context. CONCLUSION: Applying an approach, as provided here, may help to improve the implementation of eHealth applications in primary care.

Personalised exhaled nitric oxygen fraction (F ENO)-driven asthma management in primary care: a F ENO subgroup analysis of the ACCURATE trial.

BACKGROUND: The aim of this study was to identify patients who benefit most from exhaled nitric oxide fraction (F ENO)-driven asthma management in primary care, based on prespecified subgroups with different levels of F ENO. METHODS: We used data from 179 adults with asthma from a 12-month primary care randomised controlled trial with 3-monthly assessments of F ENO, asthma control, medication usage, costs of medication, severe asthma exacerbations and quality of life. In the original study, patients were randomised to either a symptom-driven treatment strategy (controlled asthma (Ca) strategy) or a F ENO+symptom-driven strategy (FCa). In both groups, patients were categorised by their baseline level of F ENO as low (<25 ppb), intermediate (25-50 ppb) and high (>50 ppb). At 12 months, we compared, for each prespecified F ENO subgroup, asthma control, asthma-related quality of life, medication usage, and costs of medication between the Ca and FCa strategy. RESULTS: We found a difference between the Ca and FCa strategy for the mean dosage of beclomethasone strategy of 223 µg (95% CI 6-439), p=0.04) and for the total costs of asthma medication a mean reduction of US$159 (95% CI US$33-285), p=0.03) in patients with a low baseline F ENO level. No differences were found for asthma control, severe asthma exacerbations and asthma-related quality of life in patients with a low baseline F ENO level. Furthermore, in patients with intermediate or high level of F ENO, no differences were found. CONCLUSIONS: In primary care, F ENO-driven asthma management is effective in patients with a low F ENO level, for whom it is possible to down-titrate medication, while preserving asthma control and quality of life.

Effectiveness of myAirCoach: A mHealth Self-Management System in Asthma.

BACKGROUND: Self-management programs have beneficial effects on asthma control, but their implementation in clinical practice is poor. Mobile health (mHealth) could play an important role in enhancing self-management. OBJECTIVE: To assess the clinical effectiveness and technology acceptance of myAirCoach-supported self-management on top of usual care in patients with asthma using inhalation medication. METHODS: Patients were recruited in 2 separate studies. The myAirCoach system consisted of an inhaler adapter, an indoor air-quality monitor, a physical activity tracker, a portable spirometer, a fraction exhaled nitric oxide device, and an app. The primary outcome was asthma control; secondary outcomes were exacerbations, quality of life, and technology acceptance. In study 1, 30 participants were randomized to either usual care or myAirCoach support for 3 to 6 months; in study 2, 12 participants were provided with the myAirCoach system in a 3-month before-after study. RESULTS: In study 1, asthma control improved in the intervention group compared with controls (Asthma Control Questionnaire difference, 0.70; P = .006). A total of 6 exacerbations occurred in the intervention group compared with 12 in the control group (hazard ratio, 0.31; P = .06). Asthma-related quality of life improved (mini Asthma-related Quality of Life Questionnaire difference, 0.53; P = .04), but forced expiratory volume in 1 second was unchanged. In study 2, asthma control improved by 0.86 compared with baseline (P = .007) and quality of life by 0.16 (P = .64). Participants reported positive attitudes toward the system. DISCUSSION: Using the myAirCoach support system improves asthma control and quality of life, with a reduction in severe asthma exacerbations. Well-validated mHealth technologies should therefore be further studied.

ERS International Congress, Madrid, 2019: highlights from the General Pneumology Assembly.

This article contains highlights and a selection of the scientific advances from the European Respiratory Society's General Pneumology Assembly that were presented at the 2019 European Respiratory Society International Congress in Madrid, Spain. The most relevant topics from the different groups will be discussed, covering a wide range of areas including rehabilitation and chronic care, general practice and primary care and M-health and E-health. In this review, the newest research and actual data as well as award-winning abstracts and highlight sessions will be discussed.

Validation of online Asthma Control Questionnaire and Asthma Quality of Life Questionnaire.

OBJECTIVE: Several newly developed eHealth applications use online questionnaires to monitor asthma control. The Asthma Control Questionnaire (ACQ) and Asthma Quality of Life Questionnaire (AQLQ) are two such commonly used questionnaires. These questionnaires are validated for use on paper. This study aims to validate them by assessing the agreement between online and paper versions of the ACQ and AQLQ. METHODS: Patients (aged 18 years and older) from the Self-Management in Asthma Supported by Hospitals, ICT, Nurses and General Practitioners (SMASHING)-trial and Davos@home study were included in this study. Patients completed both the paper and online Dutch versions of the ACQ and AQLQ in a random order within a 2-week interval. Agreement between the different versions was assessed with paired t-tests, intraclass correlation coefficients and Bland-Altman plots. RESULTS: In total 44 patients were eligible for analysis. The mean difference between the paper and online versions of the ACQ was 0.04 (p=0.40) and for the AQLQ it was 0.08 (p=0.06). The intraclass correlation coefficient scores were 0.94 for the ACQ and 0.95 for the AQLQ. CONCLUSION: The online versions of the ACQ and AQLQ show high levels of agreement with the paper versions and can therefore be safely used in eHealth applications to respectively monitor asthma control and quality of life.

Development and Validation of Personalized Prediction to Estimate Future Risk of Severe Exacerbations and Uncontrolled Asthma in Patients with Asthma, Using Clinical Parameters and Early Treatment Response.

BACKGROUND: Current level of asthma control can be easily assessed by validated instruments, but it is currently difficult to assess individuals' level of future risk. OBJECTIVE: Develop, and validate, a risk prediction score for level of future risk, including patient characteristics and information on early treatment response. METHODS: We used data of 304 adult patients with asthma from a 12-month primary care randomized controlled trial with 3-monthly assessments. With logistic regression we modeled the association between the level of future risk and patient characteristics including early treatment response. Future risk was defined as Asthma Control Questionnaire (ACQ) score of 1.5 or more at 12 months or the experience of at least 1 exacerbation during the final 6 months. We developed a risk prediction score on the basis of regression coefficients. RESULTS: Performance of the risk prediction score improved, taking into account data on early treatment response (area under receiver-operating curve [AUROC] = 0.84) compared with a model containing only baseline characteristics (AUROC = 0.78). The score includes 6 easy-to-obtain predictors: sex, ACQ score and exacerbations in the previous year at baseline and at first follow-up, and smoking status and exacerbations in the previous 3 months (indicating early treatment response). External validation yielded an AUROC of 0.77. The risk prediction score classified patients into 3 risk groups: low (absolute risk, 11.7%), intermediate (47.0%), and high (72.7%). CONCLUSIONS: We developed and externally validated a risk prediction score, quantifying both level of current asthma control and the guideline-defined future risk. Patients' individual risk can now be estimated in an easy way, as proposed but not specified, by asthma management guidelines.

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 2 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 models' predictive performances were assessed in a primary and a secondary care population for author-defined exacerbations and for American Thoracic Society/European Respiratory Society-defined severe exacerbations. RESULTS: We found 12 reports from which 24 prediction models were evaluated. Three predictors (previous health care utilization, symptoms, and spirometry values) were retained in most models. Assessment was hampered by suboptimal methodology and reporting, and by differences in exacerbation outcomes. Discrimination (area under the receiver-operating characteristic curve [c-statistic]) 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 American Thoracic Society/European Respiratory Society-defined severe exacerbations. Model calibration was generally poor, but consistent between the 2 populations. CONCLUSIONS: The preservation of 3 predictors in models derived from variable populations and the fairly consistent predictive properties of most models in 2 distinct validation populations suggest the feasibility of a generalizable model predicting severe exacerbations. Nevertheless, improvement of the models is warranted because predictive performances are below the desired level.

Longitudinal outcomes of different asthma phenotypes in primary care, an observational study.

While asthma presentation is heterogeneous, current asthma management guidelines in primary care are quite homogeneous. In this study we aim to cluster patients together into different phenotypes, that may aid the general practitioner in individualised asthma management. We analysed data from the ACCURATE trial, containing 611 adult asthmatics, 18-50 year-old, treated in primary care, with one year follow-up. Variables obtained at baseline (n = 14), were assessed by cluster analysis. Subsequently, established phenotypes were assessed separately on important asthma outcomes after one year follow-up: asthma control (Asthma Control Questionnaire (ACQ)), quality of life (Asthma Quality of Life Questionnaire (AQLQ)), exacerbation-rate and medication-usage. Five distinct phenotypes were identified. The first phenotype was predominantly defined by their early onset atopic form of asthma. The second phenotype mainly consisted of female patients with a late onset asthma. The third phenotype were patients with high reversibility rates after bronchodilator usage. The fourth phenotype were smokers and the final phenotype were frequent exacerbators. The exacerbators phenotype had the worst outcomes for asthma control and quality of life and experienced the highest exacerbation-rate, despite using the most medication. The early onset phenotype patients were relatively well controlled and their medication dosage was low. ASTHMA: INDIVIDUALIZING TREATMENT BY PHENOTYPE: Asthma patients should be characterised according to their individual asthma type to ensure more targeted treatment. Even though asthma manifests itself in a wide variety of forms with differing degrees of severity, treatment of the disease often takes a broad, one-size-fits-all approach. To determine if asthma can indeed be split into distinct phenotypes, Rishi Khusial at the Leiden University Medical Center and co-workers across the Netherlands analysed data from 611 adult asthmatics treated in primary care, and followed them up after one year. The team identified five phenotypes in the primary care cohort, including one group with early onset asthma, another whose asthma responded well to bronchodilators, and a group classed as frequent exacerbators. Further analysis of long-term asthma outcomes showed clear differences between phenotypes, particularly in terms of asthma control and quality of life.

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.

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.

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 clinician's 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.

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 0.69 (95% CI 0.63 to 0.75; 0.63 to 0.75 and 0.63 to 0.75) 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.