Predicting asthma-related crisis events using routine electronic healthcare data: A quantitative database analysis study

Noble, Michael, Burden, Annie, Stirling, Susan, Clark, Allan B., Musgrave, Stanley, Alsallakh, Mohammad A., Price, David, Davies, Gwyneth A., Pinnock, Hilary, Pond, Martin, Sheikh, Aziz, Sims, Erika J., Walker, Samantha and Wilson, Andrew M. (2021) Predicting asthma-related crisis events using routine electronic healthcare data: A quantitative database analysis study. British Journal of General Practice, 71 (713). e948-e957. ISSN 0960-1643

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Abstract

Background: There is no published algorithm predicting asthma crisis events (accident and emergency [A&E] attendance, hospitalisation, or death) using routinely available electronic health record (EHR) data.  Aim: To develop an algorithm to identify individuals at high risk of an asthma crisis event. Design and setting Database analysis from primary care EHRs of people with asthma across England and Scotland.  Method: Multivariable logistic regression was applied to a dataset of 61 861 people with asthma from England and Scotland using the Clinical Practice Research Datalink. External validation was performed using the Secure Anonymised Information Linkage Databank of 174 240 patients from Wales. Outcomes were ≥1 hospitalisation (development dataset) and asthma-related hospitalisation, A&E attendance, or death (validation dataset) within a 12-month period.  Results: Risk factors for asthma-related crisis events included previous hospitalisation, older age, underweight, smoking, and blood eosinophilia. The prediction algorithm had acceptable predictive ability with a receiver operating characteristic of 0.71 (95% confidence interval [CI] = 0.70 to 0.72) in the validation dataset. Using a cut-point based on the 7% of the population at greatest risk results in a positive predictive value of 5.7% (95% CI = 5.3% to 6.1%) and a negative predictive value of 98.9% (95% CI = 98.9% to 99.0%), with sensitivity of 28.5% (95% CI = 26.7% to 30.3%) and specificity of 93.3% (95% CI = 93.2% to 93.4%); those individuals had an event risk of 6.0% compared with 1.1% for the remaining population. In total, 18 people would need to be followed to identify one admission.  Conclusion: This externally validated algorithm has acceptable predictive ability for identifying patients at high risk of asthma-related crisis events and excluding those not at high risk.

Item Type: Article
Additional Information: Funding Information: The dataset and statistical analyses for the derivation of the algorithm were funded and delivered by the Observational and Pragmatic Research Institute. This article presents independent research funded by the National Institute for Health Research (NIHR) under its Health Technology Assessment programme (grant reference number: 13/34/70). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. Funding Information: The authors would like to thank Derek Skinner of the Observational and Pragmatic Research Institute for his support in analytical dataset generation and statistical analyses. They would also like to acknowledge the support of the Asthma UK Centre for Applied Research for its help with this study. Publisher Copyright: ©The Authors
Uncontrolled Keywords: algorithms,asthma,asthma attack,general practice,prediction,risk,family practice ,/dk/atira/pure/subjectarea/asjc/2700/2714
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
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Depositing User: LivePure Connector
Date Deposited: 17 Jun 2021 00:06
Last Modified: 24 May 2022 14:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/80284
DOI: 10.3399/BJGP.2020.1042

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