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J Invest Dermatol. 2017 Aug;137(8):1655-1662. doi: 10.1016/j.jid.2017.03.029. Epub 2017 Apr 18.

Development and Validation of an Algorithm to Accurately Identify Atopic Eczema Patients in Primary Care Electronic Health Records from the UK.

Author information

1
Program for Clinical Research, Department of Dermatology, University of California San Francisco, San Francisco, California, USA. Electronic address: Katrina.abuabara@ucsf.edu.
2
Department of Health Policy & Management, University of California Berkeley School of Public Health, Berkeley, California, USA.
3
Department of Biostatistics, Epidemiology, and Bioinformatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
4
Unit for Population-Based Dermatology Research, St John's Institute of Dermatology, King's College London, London, UK.
5
Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, UK.
6
Centre of Evidence-Based Dermatology, University of Nottingham, Nottingham, UK.
7
Department of Biostatistics, Epidemiology, and Bioinformatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA; Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Abstract

Electronic health records hold great promise for clinical and epidemiologic research. Undertaking atopic eczema (AE) research using such data is challenging because of its episodic and heterogeneous nature. We sought to develop and validate a diagnostic algorithm that identifies AE cases based on codes used for electronic records used in the UK Health Improvement Network. We found that at least one of five diagnosis codes plus two treatment codes for any skin-directed therapy were likely to accurately identify patients with AE. To validate this algorithm, a questionnaire was sent to the physicians of 200 randomly selected children and adults. The primary outcome, positive predictive value for a physician-confirmed diagnosis of AE, was 86% (95% confidence interval = 80-91). Additional criteria increased the PPV up to 95% but would miss up to 89% of individuals with physician-confirmed AE. The first and last entered diagnosis codes for individuals showed good agreement with the physician-confirmed age at onset and last disease activity; the mean difference was 0.8 years (95% confidence interval = -0.3 to 1.9) and -1.3 years (95% confidence interval = -2.5 to -0.1), respectively. A combination of diagnostic and prescription codes can be used to reliably estimate the diagnosis and duration of AE from The Health Improvement Network primary care electronic health records in the UK.

PMID:
28428130
PMCID:
PMC5883318
DOI:
10.1016/j.jid.2017.03.029
[Indexed for MEDLINE]
Free PMC Article

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