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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; Thomas, Mike (2016)
Publisher: Elsevier
Languages: English
Types: Article
Subjects: wf_20, wf_140, wa_108
Background\ud \ud Asthma attacks are common, serious, and costly. Individual factors associated with attacks, such as poor symptom control, are not robust predictors.\ud \ud Objective\ud \ud We investigated whether the rich data available in UK electronic medical records could identify patients at risk of recurrent attacks.\ud \ud Methods\ud \ud 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.\ud \ud Results\ud \ud 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.\ud \ud Conclusions\ud \ud 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.
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