Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases
(2022) In Dermatology and Therapy 12(2). p.545-559- Abstract
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... (More)
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.
(Less)
- author
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Atopic dermatitis, Patient identification, Primary care, Validation
- in
- Dermatology and Therapy
- volume
- 12
- issue
- 2
- pages
- 15 pages
- publisher
- Springer
- external identifiers
-
- pmid:35041157
- scopus:85123107229
- ISSN
- 2193-8210
- DOI
- 10.1007/s13555-021-00670-1
- language
- English
- LU publication?
- yes
- id
- 42f78f73-cffa-4b45-bb6c-82af4ecd845e
- date added to LUP
- 2022-03-25 10:42:21
- date last changed
- 2024-05-02 07:27:56
@article{42f78f73-cffa-4b45-bb6c-82af4ecd845e,
abstract = {{<p>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.</p>}},
author = {{Ortsäter, Gustaf and De Geer, Anna and Geale, Kirk and Rieem Dun, Alexander and Lindberg, Ingrid and Thyssen, Jacob P. and von Kobyletzki, Laura and Ballardini, Natalia and Henrohn, Dan and Neregård, Petra and Cha, Amy and Cappelleri, Joseph C. and Neary, Maureen P.}},
issn = {{2193-8210}},
keywords = {{Atopic dermatitis; Patient identification; Primary care; Validation}},
language = {{eng}},
number = {{2}},
pages = {{545--559}},
publisher = {{Springer}},
series = {{Dermatology and Therapy}},
title = {{Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases}},
url = {{http://dx.doi.org/10.1007/s13555-021-00670-1}},
doi = {{10.1007/s13555-021-00670-1}},
volume = {{12}},
year = {{2022}},
}