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+Fe_NO. 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 Fe_NO 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 0.72 (95% CI 0.70 to 0.73; 71 to 0.74 and 0.71 to 0.73) 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 Fe_NO is modest. These models merit an implementation study in clinical practice to assess their utility.

Trial registration number NTR 1756.