Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases

Gustaf Ortsäter; Anna De Geer; Kirk Geale; Alexander Rieem Dun; Ingrid Lindberg; Jacob P Thyssen; Laura von Kobyletzki; Natalia Ballardini; Dan Henrohn; Petra Neregård; Amy Cha; Joseph C Cappelleri; Maureen P Neary

doi:10.1007/s13555-021-00670-1

Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases

Dermatol Ther (Heidelb). 2022 Feb;12(2):545-559. doi: 10.1007/s13555-021-00670-1. Epub 2022 Jan 18.

Authors

Gustaf Ortsäter¹, Anna De Geer², Kirk Geale^{3

4}, Alexander Rieem Dun³, Ingrid Lindberg³, Jacob P Thyssen⁵, Laura von Kobyletzki⁶, Natalia Ballardini^{7

8

9}, Dan Henrohn^{2

10}, Petra Neregård², Amy Cha¹¹, Joseph C Cappelleri^{11

12}, Maureen P Neary^{11

13}

Affiliations

¹ Quantify Research, Stockholm, Sweden. gustaf.ortsater@quantifyresearch.com.
² Pfizer AB, Stockholm, Sweden.
³ Quantify Research, Stockholm, Sweden.
⁴ Dermatology and Venerology, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
⁵ Department of Dermatology and Venerology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark.
⁶ Department of Occupational and Environmental Dermatology, Skåne University Hospital, Lund University, Lund, Sweden.
⁷ Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
⁸ Department of Dermatology and Sexual Health, Södersjukhuset, Stockholm, Sweden.
⁹ Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.
¹⁰ Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
¹¹ Pfizer Inc, New York, NY, USA.
¹² Pfizer Inc, Groton, CT, USA.
¹³ Pfizer Inc, Collegeville, PA, USA.

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 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.

Keywords: Atopic dermatitis; Patient identification; Primary care; Validation.