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Journal of Shoulder and Elbow Surgery

Using Machine Learning to Predict Internal Rotation after Anatomic and Reverse Total Shoulder Arthroplasty

Published:November 19, 2021DOI:https://doi.org/10.1016/j.jse.2021.10.032
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      Abstract

      Background

      Improvement in internal rotation (IR) after anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty is difficult to predict, with rTSA patients experiencing greater variability and more limited IR improvements than aTSA patients. The purpose of this study is to quantify and compare the IR score for aTSA and rTSA patients and create supervised machine learning that predicts IR after aTSA and rTSA at multiple postoperative timepoints.

      Methods

      Clinical data from 2,270 aTSA and 4,198 rTSA patients were analyzed using 3 supervised machine learning techniques to create predictive models for internal rotation as measured by the IR score at 6 postoperative timepoints. Predictions were performed using the full input feature set and 2 minimal input feature sets. The mean absolute error (MAE) quantified the difference between actual and predicted IR scores for each model at each timepoint. The predictive accuracy of the XGBoost algorithm was also quantified by its ability to distinguish which patients would achieve clinical improvement greater than the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) patient satisfaction thresholds for IR score at 2-3 years after surgery.

      Results

      RTSA patients had significantly lower mean IR scores and significantly less mean IR score improvement than aTSA patients at each postoperative timepoint. Both aTSA and rTSA patients experienced significant improvements in their ability to perform ADLs; however, aTSA patients were significantly more likely to perform these ADLs. Using a minimal feature set of preoperative inputs, our machine learning algorithms had equivalent accuracy when predicting IR score for both aTSA (0.92-1.18 MAE) and rTSA (1.03-1.25 MAE) from 3 months to >5 years after surgery. Furthermore, these predictive algorithms identified with 90% accuracy for aTSA and 85% accuracy for rTSA which patients will achieve MCID IR score improvement and predicted with 85% accuracy for aTSA patients and 77% accuracy for rTSA which patients will achieve SCB IR score improvement at 2-3 years after surgery.

      Discussion

      Our machine learning study demonstrates that active internal rotation can be accurately predicted after aTSA and rTSA at multiple postoperative timepoints using a minimal feature set of preoperative inputs. These predictive algorithms accurately identified which patients will, and will not achieve clinical improvement in IR score that exceeds the MCID and SCB patient satisfaction thresholds.

      Keywords

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