Patient characteristics affecting knowledge of the possibility of surgical reconstruction for rheumatoid hand and wrist deformities

Motomiya, Makoto; Nishimoto, Norihiro; Urita, Atsushi; Kawamura, Daisuke; Shimizu, Yasuhiko; Iwasaki, Norimasa

doi:10.1080/14397595.2020.1790779

Cited by 1 publication

(2 citation statements)

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“…2,27 It is also possible that patients do not seek out surgical care owing to a lack of knowledge or physician bias. [28][29][30] In addition, trends in the use of surgery are changing in response to the development of bDMARDs and tsDMARDs, which can be highly effective in retarding RA disease progression. 19,[31][32][33] Despite these advancements, a portion of patients go on to develop structural change, joint pain and loss of function that may warrant surgery.…”

Section: Discussionmentioning

confidence: 99%

“…Some variation in the use of surgery has been attributed to disagreement among rheumatologists and surgeons on the timing and outcomes of certain procedures 2,27 . It is also possible that patients do not seek out surgical care owing to a lack of knowledge or physician bias 28–30 . In addition, trends in the use of surgery are changing in response to the development of bDMARDs and tsDMARDs, which can be highly effective in retarding RA disease progression 19,31–33 .…”

Section: Discussionmentioning

confidence: 99%

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

Baxter,

Lin,

Wallace

et al. 2024

Arthritis Care & Research

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ObjectiveOne in five patients with rheumatoid arthritis (RA) rely on surgery to restore joint function. However, variable response to disease‐modifying anti‐rheumatic 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 a) likelihood of undergoing an operation related to RA, b) which type of operation surgical patients undergo.MethodsWe 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 surgical patients 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).ResultsWe identified 5,481 patients, of which 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 non‐steroidal 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.ConclusionsPredictive 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.This article is protected by copyright. All rights reserved.

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