Tools 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.

To predict asthma exacerbation in an upcoming year using electronically accessible data conditional on past adherence to asthma medications.

This 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.

Among 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.

This 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.

Several models have been created to predict the exacerbation of asthma patients. Yet, some of them requested additional measurements from physicians and failed to include the adherence of individual controller medication.

A model predicting asthma exacerbation with electronically accessible data was created using machine learning, which indicated asthma-related medication use increased the risk of asthma exacerbation and a potential U-shaped relationship between adherence to controller medications and the risk of exacerbation.

To facilitate the utilization in asthma management, this model only included electronically accessible data, avoided additional measurement from physicians. It quantifies the risk of disease progression and could be embedded into the electronic medical records to assist real-time decision-making in practice.