ArticleArchives of dermatological research2022
Untapping the potential of utilizing electronic medical records to identify patients with atopic dermatitis: an algorithm using ICD-10 codes.
Article in Archives of dermatological research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Development of phenotype algorithms for the detection of adverse events in electronic health record data: a multicentre study.BMJ open · 2026Article
- Mindful approaches: Association between atopic dermatitis and autism spectrum disorder and use of visual storyboards.Dermatology online journal · 2026Article
- The Association Between Atopic Dermatitis and Select Disease Events in Adults in the United States: A Retrospective Cohort Study in the Optum Electronic Health Records Database.Dermatology and therapy · 2025Article
- Utilization of Computable Phenotypes in Electronic Health Record Research: A Review and Case Study in Atopic Dermatitis.The Journal of investigative dermatology · 2025Review
- Patient Phenotyping for Atopic Dermatitis With Transformers and Machine Learning: Algorithm Development and Validation Study.JMIR formative research · 2024Article
- Patient Phenotyping for Atopic Dermatitis with Transformers and Machine Learning.medRxiv : the preprint server for health sciences · 2023Article
- Association between yearlong air pollution and moderate-severe atopic dermatitis: A United States cross-sectional claims analysis.JAAD international · 2023Article
- Patterns in the Development of Pediatric Allergy.Pediatrics · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Atopic Dermatitis (AD) is a chronic, inflammatory skin condition that imposes an enormous personal and economic burden in the United States. Due to the ubiquity of the use of electronic medical records (EMR) in the United States, utilizing such data is critically important to studying common dermatologic diseases, such as AD. Our goal was to create a simple-to-use algorithm applied to EMR data to accurately identify AD patients thereby making it possible to efficiently use EMR data to ascertain and then study individuals with AD. Our results suggest that the algorithm that is most likely to accurately identify AD patients from the EMR based on PPV utilizes ICD-10 code for L20.89, L20.9, or L20.84 in conjunction with a diagnosis code for asthma or allergic rhinitis, treatment code, and dermatology consult code. This approach yields a PPV of 95.00% in our training cohort and 100.00% in our validation cohort. Therefore, future studies can use this algorithm to better assure that a subject has AD for studies of the pathogenesis and/or potential treatment targets of AD.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.