The Global Initiative for Asthma (GINA) guidelines suggests that asthma should not be treated prior to a reversibility test and/or an assessment of peak expiratory flow (PEF) variability over time unless there is a clinical urgency for the patient to be treated. If lung function measurements do not verify an asthma diagnosis, GINA suggests repeating these tests at a later time or applying alternative tests such as bronchial challenges. In spite of clear diagnostic guidelines, current clinical practice relies mostly on evidence of symptoms and assessments of the effect of trial treatments instead of frequently use objective lung function measurements to verify obstructive airway disease. Trial treatments including testing responses to anti-inflammatory medications have lead to increasing numbers of normal spirometry tests and fewer patients with an objective verified asthma diagnosis. Aaron et al. (JAMA 2017) showed that a current diagnosis of asthma could not be established in 33% of patients with a physician-diagnosed asthma within the last five years. Further, Aaron et al. gradually tapered off patients' asthma medication and defined them as mis-diagnosed if they were still symptom-free after 12 months of observation. Altogether, to solve the problem of mis-diagnosed asthma, it is necessary to secure objective evidence of airway obstruction, and if trial treatment is needed due to the clinical urgency of the patient's respiratory condition, reassessment with lung function test is mandatory.
The CE-marked AsthmaTuner provides doctors and nurses with daily automated percentage calculations of diurnal and weekly variability of PEF or forced expiratory volume in one second (FEV1) in close relation with the patient's symptoms, thereby supporting asthma care with information if the patient has variable expiratory airflow limitation and a history of typical asthma symptoms. These collected data contain unexpected amounts of information for correct diagnosis of individuals with asthma and different phenotypes. Hence, AsthmaTuner may improve the quality of asthma care and support more precise diagnosis according to objective diagnostic criteria based on lung function, and if correctly validated and applied, reduce the problem with over- and under-diagnosis in asthma care.
The objectives of this study are to evaluate the sensitivity and specificity to establish objective asthma diagnosis with spirometry including reversibility test and PEF-monitoring with AsthmaTuner, and secondary, assess the number of asthma patients with objective verified asthma diagnosis with use of spirometry including reversibility test and/or periodic variability with PEF/FEV1 between traditional trial treatment and treatment with AsthmaTuner.
Study population At least 146 participants will be included who are at least six years old, with respiratory symptoms that can be signed to asthma last month or with diagnosed asthma last five years, but no regular intake of anti-inflammatory treatment in the last three months. Exclusion criteria are daily intake of asthma control medication, use of oral corticosteroids, pregnancy, breast feeding, inability to perform spirometry, or a contraindication of a severe a medical condition, i.e. heart failure or aorta or cerebral aneurysm or history of myocardial infarction or stroke within three months or a smoking history greater than ten pack-years (possible COPD). Patients will be included at Liljeholmen Healthcare Centre, Ekerö Healthcare Centre in the region of Stockholm County Council, and Närhälsan Färgelanda Healthcare Centre, Västra Götaland County Council in Sweden.
Design This is a randomised controlled trial evaluating a diagnostic algorithm that includes dynamic spirometry with a reversibility test, PEF/FEV1 monitoring with AsthmaTuner and traditional trial treatment with dynamic spirometry with a reversibility test, PEF/FEV1 monitoring with AsthmaTuner and trial treatment with AsthmaTuner in a primary care setting. In the first stage, each patient will perform spirometry with a reversibility test and two weeks of daily monitoring with AsthmaTuner. Patients will be prescribed an inhaler with beta-2 agonist to prevent respiratory symptoms during daily lung function measurements in morning (after awaking) and evening. If any test is positive, the patient will fulfil the study definition of current asthma. If both tests are negative, a trial treatment of at least three months will be performed that is either traditional or with AsthmaTuner. The allocation to either AsthmaTuner or trial treatment will be 1:1 and masked to investigator. After the three months trial treatment, participants will at final clinical visit perform spirometry with reversibility test. If current asthma is still unverified, physician may rule out asthma medication and diagnosis or initiate further investigations including measures in the regular clinical work-up or let patient continue with trial treatment up to 12 months after inclusion.
AsthmaTuner (Medituner AB, Stockholm, Sweden) is a CE-marked cloud computing-based system with a healthcare interface and a downloadable patient app (Android or iOS). Patients download the app from the AppStore or Google Play and create a user account. The app consists of two features: 1) diagnosis based on serial measurements of PEF/FEV1, and 2) self-management with a physician-prescribed treatment recommendation.
The intended use is to improve diagnosis and management of asthma with an automated clinical decision support system called AsthmaTuner by letting patients register symptoms and measure FEV1. The self-management feature consists of a treatment-adjusting algorithm that gives patient immediate feedback on the status of symptom control (i.e. controlled, partly controlled or uncontrolled) and a treatment recommendation with an image of the correct inhaler or other type of medication and the dose. Symptom control is quantified based on lung function, i.e. liter to percentage of personalized best FEV1 using a cut-off of ≤80% and symptoms during the last week based on answers to four questions about the following: 1) the need for rescue medication more than twice due to asthma symptoms, 2) any daytime symptoms, 3) nocturnal symptoms/awakenings, and 4) limitations in physical activities. AsthmaTuner offers patients and health care providers longitudinal data views of assessed symptom control, prescribed treatments and lung function measurements. The back-end data storage of the cloud-based system provides information about participant adherence with AsthmaTuner use.
Dynamic spirometry with reversibility test Lung function will be measured at study enrolment, including analysis of FEV1, forced vital capacity (FVC), FEV1/FVC and forced expiratory flow after 25-75% of vital capacity (FEF 25-75). A reversibility test will be performed after intake of at least four standard doses of salbutamol or terbutaline. During trial treatment, dynamic spirometry will be performed using AsthmaTuner online.
Questionnaires Participants will complete an electronic questionnaire concerning demographic background factors, respiratory symptoms, treatment, and healthcare utilization at study enrollment and again about respiratory symptoms at study end, as well as answer questions about the usability and feasibility of using AsthmaTuner at study end.
Statistical analyses The statistical power analysis is based on Aaron et al., who reported a proportion of positive reversibility tests of 14 percentage points among 613 randomised selected individuals with doctor-diagnosed asthma. An estimated proportion of 28% of participants with asthma will have positive diurnal or weekly PEF/FEV1 variability of at least 10% or more, including 146 participants who would provide a power of 80% at a 5% significance level with adjustment for a 10% drop out rate. A receiver-operating characteristic (ROC) curve will be plotted for spirometry and reversibility tests, and PEF/FEV1 percentage variability given by AsthmaTuner with Spearmen's rank correlations to estimate the predictive values. The optimal cut-off level for sensitivity and specificity will be estimated based on the area under the curve.
