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Pain. 2019 Aug 30. doi: 10.1097/j.pain.0000000000001693. [Epub ahead of print]

Machine-learning based knowledge discovery in rheumatoid arthritis related registry data to identify predictors of persistent pain.

Author information

1
Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany.
2
Fraunhofer Institute of Molecular Biology and Applied Ecology - Project Group Translational Medicine and Pharmacology (IME-TMP), Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany.
3
Department of Environmental Medicine, Karolinska Institute, Stockholm, Sweden.
4
Department of Medicine, Rheumatology Unit, Center of Molecular Medicine (CMM), Karolinska Institute, Stockholm, Sweden.

Abstract

Early detection of patients with chronic diseases at risk of developing persistent pain is clinically desirable for timely initiation of multimodal therapies. Quality follow-up registries may provide the necessary clinical data; however, their design is not focused on a specific research aim, which poses challenges on the data-analysis strategy. Here, machine-learning was used to identify early parameters that provide information about a future development of persistent pain in rheumatoid arthritis (RA). Data of 288 patients were queried from a registry based on the Swedish Epidemiological Investigation of RA (EIRA). Unsupervised machine-learning identified three distinct patient subgroups (low, median and high) persistent pain intensities. Next, supervised machine learning, implemented as random forests followed by computed ABC analysis-based item categorization, was used to select predictive parameters among 21 different demographic, patient rated and objective clinical factors. The selected parameters were used to train machine-learned algorithms to assign patients pain-related subgroups (1,000 random resamplings, 2/3 training, 1/3 test data). Algorithms trained with three-month data of patient global assessment and health assessment questionnaire provided pain group assignment at a balanced accuracy of 70 %. When restricting the predictors to objective clinical parameters of disease severity, swollen joint count and tender joint count acquired at three months provided a balanced accuracy of rheumatoid arthritis of 59 %. Results indicate that machine-learning is suited to extract knowledge from data queried from pain and disease related registries. Early functional parameters of RA are informative for the development and degree of persistent pain.

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