Development of a Machine Learning Model to Predict the Use of Surgery in Patients With Rheumatoid Arthritis

Natalie B. Baxter, Ching Heng Lin, Beth I. Wallace, Jung Sheng Chen, Chang Fu Kuo*, Kevin C. Chung*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review


Objective: One in five patients with rheumatoid arthritis (RA) rely on surgery to restore joint function. However, variable response to disease-modifying antirheumatic drugs (DMARDs) complicates surgical planning, and it is difficult to predict which patients may ultimately require surgery. We used machine learning to develop predictive models for the likelihood of undergoing an operation related to RA and which type of operation patients who require surgery undergo. Methods: We used electronic health record data to train two extreme gradient boosting machine learning models. The first model predicted patients’ probabilities of undergoing surgery ≥5 years after their initial clinic visit. The second model predicted whether patients who underwent surgery would undergo a major joint replacement versus a less intensive procedure. Predictors included demographics, comorbidities, and medication data. The primary outcome was model discrimination, measured by area under the receiver operating characteristic curve (AUC). Results: We identified 5,481 patients, of whom 278 (5.1%) underwent surgery. There was no significant difference in the frequency of DMARD or steroid prescriptions between patients who did and did not have surgery, though nonsteroidal anti-inflammatory drug prescriptions were more common among patients who did have surgery (P = 0.03). The model predicting use of surgery had an AUC of 0.90 ± 0.02. The model predicting type of surgery had an AUC of 0.58 ± 0.10. Conclusions: Predictive models using clinical data have the potential to facilitate identification of patients who may undergo rheumatoid-related surgery, but not what type of procedure they will need. Integrating similar models into practice has the potential to improve surgical planning.

Original languageEnglish
Pages (from-to)636-643
Number of pages8
JournalArthritis Care and Research
Issue number5
StatePublished - 05 2024
Externally publishedYes

Bibliographical note

© 2023 American College of Rheumatology.


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