26gaa18 10.3205/26gaa18 urn:nbn:de:0183-26gaa183 Meeting Abstract Peltner Peltner Jonas J

Federal Institute for Drugs and Medical Devices (BfArM), Pharmacoepidemiology, Bonn, Germany

jonas.peltner@bfarm.de author Wicherski Wicherski Julia J

Federal Institute for Drugs and Medical Devices (BfArM), Pharmacoepidemiology, Bonn, Germany

julia.wicherski@bfarm.de author Degen Degen Miriam M

German Cancer Research Center (DKFZ), Division of Clinical Epidemiology of Early Cancer Detection, Heidelberg, Germany

miriam.degen@dkfz-heidelberg.de author Ebert Ebert Nina N

German Diabetes Center (DDZ), Institute for Biometrics and Epidemiology, Düsseldorf, Germany

Nina.Ebert@ddz.de author Haug Haug Ulrike U

Leibniz Institute for Prevention Research and Epidemiology - BIPS, Department of Clinical Epidemiology, Bremen, Germany

haug@leibniz-bips.de author Heier Heier Margit M

University Hospital of Augsburg, KORA Study Centre, Augsburg, Germany

margit.heier@helmholtz-munich.de author Linseisen Linseisen Jakob J

University of Augsburg, Augsburg, Germany

jakob.linseisen@med.uni-augsburg.de author Keil Keil Thomas T

Charité – University Medical Center Berlin, Berlin, Germany

thomas.keil@charite.de author Haenisch Haenisch Britta B

Federal Institute for Drugs and Medical Devices (BfArM), Pharmacoepidemiology, Bonn, Germany

britta.haenisch@bfarm.de author German Medical Science GMS Publishing House

Düsseldorf

610 20261021 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0661 18 Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie e.V. (GAA) 33. Jahrestagung der Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie Abstracts München 20261129 20261130 26gaa18 TextBackground/Aim: Polypharmacy is commonly characterised by the number of medications, leaving clinically relevant information such as distinct medication patterns insufficiently understood. Therefore, the objective of this analysis was to identify medication patterns using an unsupervised clustering approach.Methods: Latent Dirichlet Allocation (LDA) was applied to baseline assessment data from the German National Cohort (NAKO). In the NAKO all medications reported to be taken regularly during the preceding 7 days are included. For the main analysis, corresponding ATC codes were aggregated at level 4. The analysis was restricted to ATC codes reported by ≥ 1.0% of participants and participants taking ≥ 2 medications regularly with complete information on ATC codes and frequency of use. Tuning metrics, model stability, and clinical interpretability were used to determine the number of the clustering topics. For visualisation at topic level, Hellinger-based Multidimensional Scaling plots were used. At participant level, Uniform Manifold Approximation and Projection (UMAP) projection was applied.Results: 55,052 participants (mean age 57 years, 55% female) fulfilled the eligibility criteria. Highest clinically interpretability was achieved by a 6-topic model which comprised a gynaecological-endocrine hormone pattern, a cardiovascular secondary prevention pattern, a diabetic-cardiorenal pattern, a respiratory/allergy pattern, a male-dominated cardiovascular-urological pattern, and a pattern of chronic pain with psychotropic and neurological co-medication. At the topic level, clinically related cardiometabolic patterns showed a sufficient separation despite their close relationship. At the participant level, compact clusters with dominant topic membership and overlapping regions were visible, aligning with the mixed-membership structure of LDA.Discussion/Conclusion: In this cohort study, the LDA approach appears appropriate for identifying clinically plausible medication patterns. Based on the main analysis, a 6-topic model is indicated. Sensitivity analyses, profiling of topic-dominant participants, and regression analyses of associated features will follow. 0 0 0 0