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Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases

INTRODUCTION: The use of real-world data offers a possibility to perform large-scale epidemiological studies in actual clinical settings. Despite their many advantages, administrative databases were not designed to be used in research, and the validation of diagnoses and treatments in administrative...

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Autores principales: Ortsäter, Gustaf, De Geer, Anna, Geale, Kirk, Rieem Dun, Alexander, Lindberg, Ingrid, Thyssen, Jacob P., von Kobyletzki, Laura, Ballardini, Natalia, Henrohn, Dan, Neregård, Petra, Cha, Amy, Cappelleri, Joseph C., Neary, Maureen P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Healthcare 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850516/
https://www.ncbi.nlm.nih.gov/pubmed/35041157
http://dx.doi.org/10.1007/s13555-021-00670-1
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author Ortsäter, Gustaf
De Geer, Anna
Geale, Kirk
Rieem Dun, Alexander
Lindberg, Ingrid
Thyssen, Jacob P.
von Kobyletzki, Laura
Ballardini, Natalia
Henrohn, Dan
Neregård, Petra
Cha, Amy
Cappelleri, Joseph C.
Neary, Maureen P.
author_facet Ortsäter, Gustaf
De Geer, Anna
Geale, Kirk
Rieem Dun, Alexander
Lindberg, Ingrid
Thyssen, Jacob P.
von Kobyletzki, Laura
Ballardini, Natalia
Henrohn, Dan
Neregård, Petra
Cha, Amy
Cappelleri, Joseph C.
Neary, Maureen P.
author_sort Ortsäter, Gustaf
collection PubMed
description INTRODUCTION: The use of real-world data offers a possibility to perform large-scale epidemiological studies in actual clinical settings. Despite their many advantages, administrative databases were not designed to be used in research, and the validation of diagnoses and treatments in administrative databases is needed. The primary objective of this study was to validate an existing algorithm based on dispensed prescriptions and diagnoses of skin conditions to identify pediatric patients with atopic dermatitis (AD), using a diagnosis of AD in primary care as a gold standard. METHODS: Retrospective observational data were collected from nation-wide secondary care and pharmacy-dispensed medication databases and two regional primary care databases in Sweden. An existing algorithm and a Modified algorithm, using skin-specific diagnoses from secondary care and/or pharmacy-dispensed prescriptions to identify patients with AD, were assessed. To verify the presence of AD, diagnoses from primary care were used in the base case and complemented with diagnoses from secondary care in a sensitivity analysis. RESULTS: The sensitivity (30.0%) and positive predictive value (PPV) (40.7%) of the existing algorithm were low in the pediatric patient population when using primary care data only but increased when secondary care visits were also included in the Modified algorithm (sensitivity, 62.1%; PPV, 66.3%). The specificity of the two algorithms was high in both the base case and sensitivity analysis (95.1% and 94.1%). In the adult population, sensitivity and PPV were 20.4% and 8.7%, respectively, and increased to 48.3% and 16.9% when secondary care visits were also included in the Modified algorithm. CONCLUSION: The Modified algorithm can be used to identify pediatric AD populations using primary and secondary administrative data with acceptable sensitivity and specificity, but further modifications are needed to accurately identify adult patients with AD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13555-021-00670-1.
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spelling pubmed-88505162022-02-23 Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases Ortsäter, Gustaf De Geer, Anna Geale, Kirk Rieem Dun, Alexander Lindberg, Ingrid Thyssen, Jacob P. von Kobyletzki, Laura Ballardini, Natalia Henrohn, Dan Neregård, Petra Cha, Amy Cappelleri, Joseph C. Neary, Maureen P. Dermatol Ther (Heidelb) Original Research INTRODUCTION: The use of real-world data offers a possibility to perform large-scale epidemiological studies in actual clinical settings. Despite their many advantages, administrative databases were not designed to be used in research, and the validation of diagnoses and treatments in administrative databases is needed. The primary objective of this study was to validate an existing algorithm based on dispensed prescriptions and diagnoses of skin conditions to identify pediatric patients with atopic dermatitis (AD), using a diagnosis of AD in primary care as a gold standard. METHODS: Retrospective observational data were collected from nation-wide secondary care and pharmacy-dispensed medication databases and two regional primary care databases in Sweden. An existing algorithm and a Modified algorithm, using skin-specific diagnoses from secondary care and/or pharmacy-dispensed prescriptions to identify patients with AD, were assessed. To verify the presence of AD, diagnoses from primary care were used in the base case and complemented with diagnoses from secondary care in a sensitivity analysis. RESULTS: The sensitivity (30.0%) and positive predictive value (PPV) (40.7%) of the existing algorithm were low in the pediatric patient population when using primary care data only but increased when secondary care visits were also included in the Modified algorithm (sensitivity, 62.1%; PPV, 66.3%). The specificity of the two algorithms was high in both the base case and sensitivity analysis (95.1% and 94.1%). In the adult population, sensitivity and PPV were 20.4% and 8.7%, respectively, and increased to 48.3% and 16.9% when secondary care visits were also included in the Modified algorithm. CONCLUSION: The Modified algorithm can be used to identify pediatric AD populations using primary and secondary administrative data with acceptable sensitivity and specificity, but further modifications are needed to accurately identify adult patients with AD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13555-021-00670-1. Springer Healthcare 2022-01-18 /pmc/articles/PMC8850516/ /pubmed/35041157 http://dx.doi.org/10.1007/s13555-021-00670-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research
Ortsäter, Gustaf
De Geer, Anna
Geale, Kirk
Rieem Dun, Alexander
Lindberg, Ingrid
Thyssen, Jacob P.
von Kobyletzki, Laura
Ballardini, Natalia
Henrohn, Dan
Neregård, Petra
Cha, Amy
Cappelleri, Joseph C.
Neary, Maureen P.
Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases
title Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases
title_full Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases
title_fullStr Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases
title_full_unstemmed Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases
title_short Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases
title_sort validation of patient identification algorithms for atopic dermatitis using healthcare databases
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850516/
https://www.ncbi.nlm.nih.gov/pubmed/35041157
http://dx.doi.org/10.1007/s13555-021-00670-1
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