Identifying asthma patients at high risk of exacerbation in a routine visit: A machine learning model

Abstract

BackgroundTools capable of predicting the risk of asthma exacerbations can facilitate asthma management in clinical practice. However, existing tools require additional data from patients beyond electronic medical records. ObjectiveTo predict asthma exacerbation in an upcoming year using electronically accessible data conditional on past adherence to asthma medications. MethodsThis retrospective cohort study included patients with ≥1 hospitalization or ≥2 medical claims for asthma within 2 consecutive years between 2002 and 2015 in Quebec administrative databases. Cohort entry (CE) was defined as the date of the first asthma-related ambulatory visit on or after meeting the operational definition of asthma. Adherence to each controller medication and use of each rescue medication was measured in the year prior to CE. Elastic-net regularized logistic regression was applied. ResultsAmong 98,823 patients, the mean age was 55.9 years and 36.2% were men. The area under the curve for prediction was 0.708. In the model, the use of long-acting anticholinergic or long-acting β2-agonists in the year prior to CE increased the odds of exacerbation by 24% and 21%, respectively. Among patients who received rescue medication, low and high adherence to controller medications increased the odds by 2%–5% compared with patients with medium adherence. Patients with a predicted risk of ≥0.20 were more likely to develop future exacerbation. ConclusionThis risk prediction indicated that asthma-related medication use increased the risk of asthma exacerbation. A potential U-shaped relationship between adherence to controller medications and the risk of exacerbation was identified among users of rescue medications.