John D Blakey1, David B Price2, Emilio Pizzichini3, Todor A Popov4, Borislav D Dimitrov5, Dirkje S Postma6, Lynn K Josephs7, Alan Kaplan8, Alberto Papi9, Marjan Kerkhof10, Elizabeth V Hillyer10, Alison Chisholm11, Mike Thomas5. 1. Clinical Sciences, Liverpool School of Tropical Medicine, and Respiratory Medicine, Royal Liverpool Hospital, Liverpool, United Kingdom. Electronic address: John.Blakey@lstmed.ac.uk. 2. Academic Primary Care, University of Aberdeen, Aberdeen, United Kingdom; Observational and Pragmatic Research Institute Pte Ltd, Singapore. 3. NUPAIVA (Asthma Research Centre), University Hospital, Federal University of Santa Catarina, Florianуpolis, Santa Catarina, Brazil. 4. Medical University Sofia, Sofia, Bulgaria. 5. Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; NIHR Southampton Respiratory Biomedical Research Unit, Southampton, United Kingdom. 6. Department of Pulmonology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. 7. Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom. 8. Department of Family and Community Medicine, University of Toronto, Toronto, Canada. 9. Department of Medicine, University of Ferrara, Ferrara, Italy. 10. Observational and Pragmatic Research Institute Pte Ltd, Singapore. 11. Respiratory Effectiveness Group, Cambridge, United Kingdom.
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.
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.
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