Blakey, John D., Price, David B., Pizzichini, Emilio, Popov, Todor A., Dimitrov, Borislav D., Postma, Dirkje S., Josephs, Lynn K., Kaplan, Alan, Papi, Alberto, Kerkhof, Marjan, Hillyer, Elizabeth V., Chisholm, Alison and Thomas, Mike (2016) Identifying risk of future asthma attacks using UK medical record data: a Respiratory Effectiveness Group initiative. The Journal of Allergy and Clinical immunology: In Practice, 1-18. (doi:10.1016/j.jaip.2016.11.007).
Abstract
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 years of data. Potential risk factors during 1 baseline year were evaluated using univariable (simple) logistic regression for outcomes of ?2 and ?4 attacks during the following 2-year period. Predictors with significant univariable association (P<.05) were entered into multiple logistic regression analysis with backwards 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 [OCS] courses, emergency visits), more frequent reliever use and healthcare utilization, worse lung function, current smoking, blood eosinophilia, rhinitis, nasal polyps, eczema, gastroesophageal reflux disease, obesity, older age, and being female. The number of OCS courses had the strongest association. The final cross-validated models incorporated 19 and 16 risk factors for ?2 and ?4 attacks over 2 years, respectively, with areas under the curve of 0.785 (95% CI 0.780-0.789) and 0.867 (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.
Study Registration: ENCePP 4869
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