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Identifying patients at risk for severe exacerbations of asthma: development and external validation of a multivariable prediction model
  1. Rik J B Loymans1,
  2. Persijn J Honkoop2,3,
  3. Evelien H Termeer4,
  4. Jiska B Snoeck-Stroband2,
  5. Willem J J Assendelft3,4,
  6. Tjard R J Schermer4,
  7. Kian Fan Chung5,
  8. Ana R Sousa6,
  9. Peter J Sterk7,
  10. Helen K Reddel8,
  11. Jacob K Sont2,
  12. Gerben ter Riet1
  1. 1Department of General Practice, Academic Medical Center-University of Amsterdam (AMC), Amsterdam, The Netherlands
  2. 2LUMC Quality of Care Institute, Leiden University Medical Center (LUMC), Leiden, The Netherlands
  3. 3Department of Public Health and Primary Care, Leiden University Medical Center (LUMC), Leiden, The Netherlands
  4. 4Department of Primary and Community Care, Radboud University Nijmegen Medical Centre (RUNMC), Nijmegen, The Netherlands
  5. 5Experimental Airway Disease, National Heart and Lung Institute, Imperial College London & Royal Brompton NIHR Biomedical Research Unit, London, UK
  6. 6Respiratory Therapeutic Unit, GlaxoSmithKline, Stockley Park, UK
  7. 7Department of Respiratory Medicine, Academic Medical Center-University of Amsterdam (AMC), Amsterdam, The Netherlands
  8. 8Clinical Management Group, Woolcock Institute of Medical Research, University of Sydney, Sydney, Australia
  1. Correspondence to Dr Rik Loymans, AMC-UvA, PO Box 22660, Amsterdam 1100 DD, The Netherlands; r.j.loijmans{at}


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+FeNO. 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 FeNO 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 FeNO is modest. These models merit an implementation study in clinical practice to assess their utility.

Trial registration number NTR 1756.

  • Asthma in primary care
  • Asthma Epidemiology

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