Disagreement concerning atopic dermatitis subtypes between an English prospective cohort (ALSPAC) and linked electronic health records.


Journal

Clinical and experimental dermatology
ISSN: 1365-2230
Titre abrégé: Clin Exp Dermatol
Pays: England
ID NLM: 7606847

Informations de publication

Date de publication:
16 May 2024
Historique:
received: 23 02 2024
revised: 12 04 2024
accepted: 14 05 2024
medline: 16 5 2024
pubmed: 16 5 2024
entrez: 16 5 2024
Statut: aheadofprint

Résumé

Subtypes of atopic dermatitis (AD) have been derived from the Avon Longitudinal Study of Parents and Children (ALSPAC) based on presence and severity of symptoms reported in questionnaires (Severe-Frequent, Moderate-Frequent, Moderate-Declining, Mild-Intermittent, Unaffected/Rare). Good agreement between ALSPAC and linked electronic health records (EHRs) would increase trust in the clinical validity of these subtypes and allow inferring subtypes from EHRs alone, which would enable their study in large primary care databases. 1. Explore if presence and number of AD records in EHRs agrees with AD symptom and severity reports from ALSPAC; 2. Explore if EHRs agree with ALSPAC-derived AD subtypes; 3. Construct models to classify ALSPAC-derived AD subtype using EHRs. We used data from the ALSPAC prospective cohort study from 11 timepoints until age 14 years (1991-2008), linked to local general practice EHRs. We assessed how far ALSPAC questionnaire responses and derived subtypes agreed with AD as established in EHRs using different AD definitions (e.g., diagnosis and/or prescription) and other AD-related records. We classified AD subtypes using EHRs, fitting multinomial logistic regression models tuning hyperparameters and evaluating performance in the testing set (ROC AUC, accuracy, sensitivity, and specificity). 8,828 individuals out of a total 13,898 had both been assigned an AD subtype and had linked EHRs. The number of AD-related codes in EHRs generally increased with severity of AD subtype, however not all with the Severe-Frequent subtypes had AD in EHRs, and many with the Unaffected/Rare subtype did have AD in EHRs. When predicting ALSPAC AD subtype using EHRs, the best tuned model had ROC AUC of 0.65, sensitivity of 0.29 and specificity of 0.83 (both macro averaged); when different sets of predictors were used, individuals with missing EHR coverage excluded, and subtypes combined, sensitivity was not considerably improved. ALSPAC and EHRs disagreed not just on AD subtypes, but also on whether children had AD or not. Researchers should be aware that individuals considered as having AD in one source may not be considered as having AD in another.

Sections du résumé

BACKGROUND BACKGROUND
Subtypes of atopic dermatitis (AD) have been derived from the Avon Longitudinal Study of Parents and Children (ALSPAC) based on presence and severity of symptoms reported in questionnaires (Severe-Frequent, Moderate-Frequent, Moderate-Declining, Mild-Intermittent, Unaffected/Rare). Good agreement between ALSPAC and linked electronic health records (EHRs) would increase trust in the clinical validity of these subtypes and allow inferring subtypes from EHRs alone, which would enable their study in large primary care databases.
OBJECTIVES OBJECTIVE
1. Explore if presence and number of AD records in EHRs agrees with AD symptom and severity reports from ALSPAC; 2. Explore if EHRs agree with ALSPAC-derived AD subtypes; 3. Construct models to classify ALSPAC-derived AD subtype using EHRs.
METHODS METHODS
We used data from the ALSPAC prospective cohort study from 11 timepoints until age 14 years (1991-2008), linked to local general practice EHRs. We assessed how far ALSPAC questionnaire responses and derived subtypes agreed with AD as established in EHRs using different AD definitions (e.g., diagnosis and/or prescription) and other AD-related records. We classified AD subtypes using EHRs, fitting multinomial logistic regression models tuning hyperparameters and evaluating performance in the testing set (ROC AUC, accuracy, sensitivity, and specificity).
RESULTS RESULTS
8,828 individuals out of a total 13,898 had both been assigned an AD subtype and had linked EHRs. The number of AD-related codes in EHRs generally increased with severity of AD subtype, however not all with the Severe-Frequent subtypes had AD in EHRs, and many with the Unaffected/Rare subtype did have AD in EHRs. When predicting ALSPAC AD subtype using EHRs, the best tuned model had ROC AUC of 0.65, sensitivity of 0.29 and specificity of 0.83 (both macro averaged); when different sets of predictors were used, individuals with missing EHR coverage excluded, and subtypes combined, sensitivity was not considerably improved.
CONCLUSIONS CONCLUSIONS
ALSPAC and EHRs disagreed not just on AD subtypes, but also on whether children had AD or not. Researchers should be aware that individuals considered as having AD in one source may not be considered as having AD in another.

Identifiants

pubmed: 38751343
pii: 7674976
doi: 10.1093/ced/llae196
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of British Association of Dermatologists.

Auteurs

Julian Matthewman (J)

London School of Hygiene & Tropical Medicine, London, UK.

Amy Mulick (A)

London School of Hygiene & Tropical Medicine, London, UK.

Nick Dand (N)

Department of Medical and Molecular Genetics, School of Basic & Medical Biosciences, King's College London, London, UK.

Daniel Major-Smith (D)

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

Alasdair Henderson (A)

London School of Hygiene & Tropical Medicine, London, UK.

Neil Pearce (N)

London School of Hygiene & Tropical Medicine, London, UK.

Spiros Denaxas (S)

Institute of Health Informatics, UCL, London, UK.
NIHR UCLH BRC, London, UK.
BHF Data Science Centre, HDR UK, London, UK.

Rita Iskandar (R)

London School of Hygiene & Tropical Medicine, London, UK.

Amanda Roberts (A)

Independent Patient Partner.

Rosie P Cornish (RP)

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
MRC Integrative Epidemiology Unit, University of Bristol.

Sara J Brown (SJ)

Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.

Lavinia Paternoster (L)

MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.
NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK.

Sinéad M Langan (SM)

London School of Hygiene & Tropical Medicine, London, UK.

Classifications MeSH