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... Show moreEarly 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. Show less
Vos, M.S. de; Marang-van de Mheen, P.J.; Smith, A.D.; Mou, D.; Whang, E.E.; Hamming, J.F. 2018
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-flows (PEF) from two previous studies.... Show moreOur aim was to validate optimal action points in written action plans for early detection of asthma exacerbations.We analysed daily symptoms and morning peak-flows (PEF) from two previous studies. Potential action points were based on analysis of symptom scores (SDs) percentage of personal best PEF, or 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 to published guidelines action points.The optimal action points were: for symptoms, statistical variability (SDs); 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 one week. Show less
