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

Blakey, John, 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, Vol 5, Issue 4, 1015-1024.e8.

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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 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.

Item Type:	Article
Subjects:	WA Public Health > Preventive Medicine > WA 108 Preventive health services. Preventive medicine. Travel Medicine. WF Respiratory System > WF 140 Diseases of the respiratory system (General) WF Respiratory System > WF 20 Research (General)
Faculty: Department:	Clinical Sciences & International Health > Clinical Sciences Department
Digital Object Identifer (DOI):	https://doi.org/10.1016/j.jaip.2016.11.007
SWORD Depositor:	JISC Pubrouter
Depositing User:	JISC Pubrouter
Date Deposited:	01 Feb 2017 10:04
Last Modified:	22 Dec 2017 02:02
URI:	https://archive.lstmed.ac.uk/id/eprint/6698

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