Identification and prediction of difficult-to-treat rheumatoid arthritis patients in structured and unstructured routine care data: results from a hackathon.

Autor: Messelink MA; Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands. M.A.Messelink@umcutrecht.nl., Roodenrijs NMT; Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands., van Es B; Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands.; MedxAI, Theophile de Bockstraat 77-1, 1058VA, Amsterdam, The Netherlands., Hulsbergen-Veelken CAR; Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands., Jong S; MedxAI, Theophile de Bockstraat 77-1, 1058VA, Amsterdam, The Netherlands., Overmars LM; Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands.; MedxAI, Theophile de Bockstraat 77-1, 1058VA, Amsterdam, The Netherlands., Reteig LC; Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands.; MedxAI, Theophile de Bockstraat 77-1, 1058VA, Amsterdam, The Netherlands., Tan SC; MedxAI, Theophile de Bockstraat 77-1, 1058VA, Amsterdam, The Netherlands.; Department of Information Technology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands., Tauber T; MedxAI, Theophile de Bockstraat 77-1, 1058VA, Amsterdam, The Netherlands., van Laar JM; Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands., Welsing PMJ; Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands., Haitjema S; Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands.
Jazyk: angličtina
Zdroj: Arthritis research & therapy [Arthritis Res Ther] 2021 Jul 08; Vol. 23 (1), pp. 184. Date of Electronic Publication: 2021 Jul 08.
DOI: 10.1186/s13075-021-02560-5
Abstrakt: Background: The new concept of difficult-to-treat rheumatoid arthritis (D2T RA) refers to RA patients who remain symptomatic after several lines of treatment, resulting in a high patient and economic burden. During a hackathon, we aimed to identify and predict D2T RA patients in structured and unstructured routine care data.
Methods: Routine care data of 1873 RA patients were extracted from the Utrecht Patient Oriented Database. Data from a previous cross-sectional study, in which 152 RA patients were clinically classified as either D2T or non-D2T, served as a validation set. Machine learning techniques, text mining, and feature importance analyses were performed to identify and predict D2T RA patients based on structured and unstructured routine care data.
Results: We identified 123 potentially new D2T RA patients by applying the D2T RA definition in structured and unstructured routine care data. Additionally, we developed a D2T RA identification model derived from a feature importance analysis of all available structured data (AUC-ROC 0.88 (95% CI 0.82-0.94)), and we demonstrated the potential of longitudinal hematological data to differentiate D2T from non-D2T RA patients using supervised dimension reduction. Lastly, using data up to the time of starting the first biological treatment, we predicted future development of D2TRA (AUC-ROC 0.73 (95% CI 0.71-0.75)).
Conclusions: During this hackathon, we have demonstrated the potential of different techniques for the identification and prediction of D2T RA patients in structured as well as unstructured routine care data. The results are promising and should be optimized and validated in future research.
Databáze: MEDLINE
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