AB0207 AI-ENABLED REAL-WORLD DATA MINING ACROSS RHEUMATOID ARTHRITIS, GOUT, AND SPONDYLARTROPATHIES: UNRAVELING COMORBIDITIES AND LIFESTYLE FACTORS FROM UNSTRUCTURED PATIENT DATA
Authors: R. Simons, P. Van Riel, M. Merkelbach, D. Hens, N. Cornelis, T. Jansen
Journal: Annals of the Rheumatic Diseases (2024)

<h3>Background:</h3> Patients with inflammatory rheumatic diseases often have a number of (chronic) co-morbidities which has a negative influence on treatment options and on outcomes. In daily clinical practice it is therefore important to be informed about hereabout and to be enables to manage these inflammatory rheumatic patients with a less stringent, more personalized treatment algorithm. <h3>Objectives:</h3> In this study we explored the feasibility of analyzing structured and unstructured data from anonymized electronic health records (EHRs) with the help of Artificial Intelligence (AI), particularly Natural Language Processing (NLP) and machine learning. Healthcare professionals would visualize these data through dashboards that include information about the comorbidities next to the most important treatments and outcomes of patients with inflammatory rheumatic diseases. <h3>Methods:</h3> Anonymized EHRs from VieCuri Medical Center (Venlo, the Netherlands) were processed using NLP and machine learning to generate an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) database that was validated in-house, as previously described [1-3]. Data of patients from the Rheumatology department with a diagnosis of RA, SpA and Gout from 2019-2023 were analyzed regarding variables such as demographics, clinical characteristics, comorbidities, procedures, disease-specific scores, complications, laboratory parameters, or medications. Dashboards for result visualization were generated using PowerBI software (Microsoft). <h3>Results:</h3> A total of 2190 patients with RA, 1062 with SpA and 1592 with Gout were analyzed, with a mean follow up of respectively 3.4, 3.0 and 2.3 years. The mean age was respectively 65.5, 51.0 and 65.4 years and % females was respectively 64, 51 and 17.5. The five most frequent comorbidities in patients with RA, SpA and Gout were Hypertension (23, 27 and 23 %), Cancer (21, 23 and 13 %), COPD (10, 6 and 7 %), Diabetes Mellitus (12, 14 and 13 %), MACE (19, 14 and 37 %), and Obesity (18, 16 and 25 %). <h3>Conclusion:</h3> By using AI methodologies we were able to collect structured and unstructured data about co-morbiditeis of three inflammatory rheumatic disease categories. For this study the collection of the unstructured data was of utmost importance as almost 100 % of these data were unstructured. Differences beween the three disease categories with respect of gender, age and frequency of comorbidities were found. This information is important for the management of patients with inflammatory rheumatic diseases as it has a, negative, impact on the course of the disease and the treatment possibilities. The main advange of the our approach lies in iets seamless integration with existing workflows. The health care professionals in this study were not burdened with the need to record addiutional data beyond their routine electronic medical record entries. <h3>REFERENCES:</h3> [1] Filipowicz-Sosnowska A. Comorbidities and multimorbidity in rheumatic diseases. Reumatologia.2019;57(1):1-2. [2] Van de Meulebroucke C et al. What Can We Expect Following Anterior Total Hip Arthroplasty on a Regular Operating Table? A Validation Study of an Artificial Intelligence Algorithm to Monitor Adverse Events in a High-Volume, Nonacademic Setting. J Arthroplasty. 2019 Oct;34(10):2260-2266. [3] Hens D et al. Validation of an Artificial Intelligence driven framework to automatically detect red flag symptoms in screening for rare diseases in electronic health records: hereditary transthyretin amyloidosis polyneuropathy as a key example. J Peripher Nerv Syst. 2023 Mar;28(1):79-85. [4] Moya A, et al. Detection of transthyretin amyloid cardiomyopathy by automated data extraction from electronic health records. ESC Heart Fail. 2023 Dec;10(6):3483-3492. <h3>Acknowledgements:</h3> <b>NIL.</b> <h3>Disclosure of Interests:</h3> Ruud Simons: None declared, Piet van Riel Pfizer, Netherlands, Manon Merkelbach: None declared, Dries Hens: None declared, Nils Cornelis: None declared, Tim Jansen: None declared.