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Machine learning models can predict patients unlikely to achieve meaningful gains after total joint arthroplasty (TJA). These models, using registry data, offer fair-to-good predictive ability for 2-year outcomes, aiding patient monitoring and decision support.

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Area of Science:

  • Orthopedic Surgery
  • Data Science
  • Predictive Analytics

Background:

  • Identifying patients at risk of poor long-term outcomes after total joint arthroplasty (TJA) is crucial for personalized care.
  • Machine learning (ML) offers potential for predicting these outcomes by analyzing complex datasets.
  • Current ML applications in TJA outcome prediction are limited, especially concerning patient-reported outcome measures (PROMs).

Purpose of the Study:

  • To evaluate ML algorithms for predicting patients who will not achieve a minimally clinically important difference (MCID) in PROMs two years post-TJA.
  • To assess how predictive accuracy changes with the inclusion of different data time points.
  • To identify key variables driving the predictive performance of these ML models.

Main Methods:

  • Utilized TJA registry data from a single institution, including hip and knee replacements (2007-2012).
  • Trained three supervised ML algorithms (logistic LASSO, random forest, linear SVM) to predict 2-year MCIDs for SF-36 PCS/MCS, HOOS JR, and KOOS JR.
  • Evaluated models using AUROC statistics, considering predictors from four time points: pre-decision, pre-surgery, pre-discharge, and post-discharge.

Main Results:

  • ML models demonstrated poor-to-good predictive performance (AUROCs 0.60-0.89) for 2-year MCIDs across PROMs.
  • Predictive ability improved significantly when including pre-surgery data, with logistic LASSO models achieving AUROCs up to 0.89 for SF-36 MCS.
  • Baseline PROMs were consistently identified as key predictors in pre-surgery models.

Conclusions:

  • ML models applied to presurgical registry data show potential for predicting 2-year post-TJA MCIDs with fair-to-good accuracy.
  • These models can enhance clinical decision-making, patient monitoring, and presurgical outcome discussions.
  • Further validation is necessary before widespread clinical application of these promising predictive tools.