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

Michael Noble, Annie Burden, Susan Stirling, Allan B Clark, Stanley Musgrave, Mohammad A. Al Sallakh Orcid Logo, David Price, Gwyneth Davies Orcid Logo, Hilary Pinnock, Martin Pond, Aziz Sheikh, Erika J Sims, Samantha Walker, Andrew M Wilson

British Journal of General Practice, Volume: 71, Issue: 713, Pages: e948 - e957

Swansea University Authors: Mohammad A. Al Sallakh Orcid Logo, Gwyneth Davies Orcid Logo

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DOI (Published version): 10.3399/bjgp.2020.1042

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...

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Published in: British Journal of General Practice
ISSN: 0960-1643 1478-5242
Published: Royal College of General Practitioners 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa70386
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.
Keywords: algorithms; asthma; asthma attack; general practice; prediction; risk
College: Faculty of Medicine, Health and Life Sciences
Funders: Asthma UK Centre for Applied Research
Issue: 713
Start Page: e948
End Page: e957

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