Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Nov 25;71(713):e948-e957.
doi: 10.3399/BJGP.2020.1042. Print 2021 Dec.

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

Michael Noble  1 , Annie Burden  2 , Susan Stirling  3 , Allan B Clark  3 , Stanley Musgrave  3 , Mohammad A Alsallakh  4 , David Price  5 , Gwyneth A Davies  4 , Hilary Pinnock  6 , Martin Pond  3 , Aziz Sheikh  6 , Erika J Sims  3 , Samantha Walker  7 , Andrew M Wilson  8
Affiliations

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

Michael Noble et al. Br J Gen Pract. .

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.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Flow of participants through the study. CPRD = Clinical Practice Research Datalink. HES = Hospital Episode Statistics.
See this image and copyright information in PMC

References

    1. British Thoracic Society and Scottish Intercollegiate Guidelines Network British guideline on the management of asthma: a national clinical guideline. 2019. https://www.brit-thoracic.org.uk/quality-improvement/guidelines/asthma (accessed 8 Oct 2021).
    1. NHS England Report of the Review of the Quality and Outcomes Framework in England. https://www.england.nhs.uk/wp-content/uploads/2018/07/quality-outcome-fr... (accessed 8 Oct 2021).
    1. Asthma UK Asthma facts and statistics. 2021. https://www.asthma.org.uk/about/media/facts-and-statistics (accessed 17 Sep 2021).
    1. Mukherjee M, Stoddart A, Gupta RP, et al. The epidemiology, healthcare and societal burden and costs of asthma in the UK and its member nations: analyses of standalone and linked national databases. BMC Med. 2016;14(1):113. - PMC - PubMed
    1. Hanson JR, Lee BR, Williams DD, et al. Developing a risk stratification model for predicting future health care use in asthmatic children. Ann Allergy Asthma Immunol. 2016;116(1):26–30. - PubMed

Publication types