Identifying Risk of Future Asthma Attacks Using UK Medical Record Data: A Respiratory Effectiveness Group Initiative

John D. Blakey*, David B. Price, Emilio Pizzichini, Todor A. Popov, Borislav D. Dimitrov, Dirkje S. Postma, Lynn K. Josephs, Alan Kaplan, Alberto Papi, Marjan Kerkhof, Elizabeth V. Hillyer, Alison Chisholm, Mike Thomas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

69 Citations (Scopus)


Background: Asthma attacks are common, serious, and costly. Individual factors associated with attacks, such as poor symptom control, are not robust predictors. Objective: We investigated whether the rich data available in UK electronic medical records could identify patients at risk of recurrent attacks. Methods: We analyzed anonymized, longitudinal medical records of 118,981 patients with actively treated asthma (ages 12-80 years) and 3 or more years of data. Potential risk factors during 1 baseline year were evaluated using univariable (simple) logistic regression for outcomes of 2 or more and 4 or more attacks during the following 2-year period. Predictors with significant univariable association (P < .05) were entered into multiple logistic regression analysis with backward stepwise selection of the model including all significant independent predictors. The predictive accuracy of the multivariable models was assessed. Results: Independent predictors associated with future attacks included baseline-year markers of attacks (acute oral corticosteroid courses, emergency visits), more frequent reliever use and health care utilization, worse lung function, current smoking, blood eosinophilia, rhinitis, nasal polyps, eczema, gastroesophageal reflux disease, obesity, older age, and being female. The number of oral corticosteroid courses had the strongest association. The final cross-validated models incorporated 19 and 16 risk factors for 2 or more and 4 or more attacks over 2 years, respectively, with areas under the curve of 0.785 (95% CI, 0.780-0.789) and 0.867 (95% CI, 0.860-0.873), respectively. Conclusions:Routinely collected data could be used proactively via automated searches to identify individuals at risk of recurrent asthma attacks. Further research is needed to assess the impact of such knowledge on clinical prognosis.
Original languageEnglish
Article numbere8
Pages (from-to)1015-1024
Number of pages10
JournalThe Journal of Allergy and Clinical Immunology: In Practice
Issue number4
Early online date22 Dec 2016
Publication statusPublished - Jul 2017

Bibliographical note

We thank Ian D. Pavord, Hilary Pinnock, Gene Colice, Alexandra Dima, Janet Holbrook, Cindy Rand, Iain Small, and Sam Walker for their valuable contributions to discussions about the study design. We thank Anne Burden, Vasilis Nikolaou, Victoria Thomas, and Maria Batsiou for contributions to the data elaboration and statistical analyses.

This work was supported by the Respiratory Effectiveness Group, an international, investigator-led, not-for-profit, real-life respiratory research and advocacy initiative. Access to data from the Optimum Patient Care Research Database was cofunded by Research in Real-Life Ltd, UK, under a subcontract by Observational and Pragmatic Research Institute Pte Ltd, Singapore.


  • Asthma
  • Attack
  • Control
  • Medical record
  • Observational
  • Risk factor


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