Rik J B Loymans1, Persijn J Honkoop2, Evelien H Termeer3, Jiska B Snoeck-Stroband4, Willem J J Assendelft5, Tjard R J Schermer3, Kian Fan Chung6, Ana R Sousa7, Peter J Sterk8, Helen K Reddel9, Jacob K Sont4, Gerben Ter Riet1. 1. Department of General Practice, Academic Medical Center-University of Amsterdam (AMC), Amsterdam, The Netherlands. 2. LUMC Quality of Care Institute, Leiden University Medical Center (LUMC), Leiden, The Netherlands Department of Public Health and Primary Care, Leiden University Medical Center (LUMC), Leiden, The Netherlands. 3. Department of Primary and Community Care, Radboud University Nijmegen Medical Centre (RUNMC), Nijmegen, The Netherlands. 4. LUMC Quality of Care Institute, Leiden University Medical Center (LUMC), Leiden, The Netherlands. 5. Department of Public Health and Primary Care, Leiden University Medical Center (LUMC), Leiden, The Netherlands Department of Primary and Community Care, Radboud University Nijmegen Medical Centre (RUNMC), Nijmegen, The Netherlands. 6. Experimental Airway Disease, National Heart and Lung Institute, Imperial College London & Royal Brompton NIHR Biomedical Research Unit, London, UK. 7. Respiratory Therapeutic Unit, GlaxoSmithKline, Stockley Park, UK. 8. Department of Respiratory Medicine, Academic Medical Center-University of Amsterdam (AMC), Amsterdam, The Netherlands. 9. Clinical Management Group, Woolcock Institute of Medical Research, University of Sydney, Sydney, Australia.
Abstract
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 0.69 (95% CI 0.63 to 0.75; 0.63 to 0.75 and 0.63 to 0.75) 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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
RCT Entities:
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 0.69 (95% CI 0.63 to 0.75; 0.63 to 0.75 and 0.63 to 0.75) 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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
Entities:
Keywords:
Asthma Epidemiology; Asthma in primary care
Authors: Loren C Denlinger; Brenda R Phillips; Sima Ramratnam; Kristie Ross; Nirav R Bhakta; Juan Carlos Cardet; Mario Castro; Stephen P Peters; Wanda Phipatanakul; Shean Aujla; Leonard B Bacharier; Eugene R Bleecker; Suzy A A Comhair; Andrea Coverstone; Mark DeBoer; Serpil C Erzurum; Sean B Fain; Merritt Fajt; Anne M Fitzpatrick; Jonathan Gaffin; Benjamin Gaston; Annette T Hastie; Gregory A Hawkins; Fernando Holguin; Anne-Marie Irani; Elliot Israel; Bruce D Levy; Ngoc Ly; Deborah A Meyers; Wendy C Moore; Ross Myers; Maria Theresa D Opina; Michael C Peters; Mark L Schiebler; Ronald L Sorkness; W Gerald Teague; Sally E Wenzel; Prescott G Woodruff; David T Mauger; John V Fahy; Nizar N Jarjour Journal: Am J Respir Crit Care Med Date: 2017-02-01 Impact factor: 21.405
Authors: Gene Colice; Alison Chisholm; Alexandra L Dima; Helen K Reddel; Annie Burden; Richard J Martin; Guy Brusselle; Todor A Popov; Julie von Ziegenweidt; David B Price Journal: Pragmat Obs Res Date: 2018-08-10
Authors: Holly Tibble; Athanasios Tsanas; Elsie Horne; Robert Horne; Mehrdad Mizani; Colin R Simpson; Aziz Sheikh Journal: BMJ Open Date: 2019-07-09 Impact factor: 2.692