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- W3205656918 abstract "We use machine learning to create predictive models from preoperative data to predict the Shoulder Arthroplasty Smart (SAS) score, the American Shoulder and Elbow Surgeons (ASES) score, and the Constant score at multiple postoperative time points and compare the accuracy of each algorithm for anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA). Clinical data from 2270 patients who underwent aTSA and 4198 patients who underwent rTSA were analyzed using 3 supervised machine learning techniques to create predictive models for the SAS, ASES, and Constant scores at 6 different postoperative time points using a full input feature set and the 2 different minimal feature sets. Mean absolute errors (MAEs) quantified the difference between actual and predicted outcome scores for each model at each postoperative time point. The performance of each model was also quantified by its ability to predict improvement greater than the minimal clinically important difference (MCID) and the substantial clinical benefit (SCB) patient satisfaction thresholds for each outcome measure at 2-3 years after surgery. All 3 machine learning techniques were more accurate at predicting aTSA and rTSA outcomes using the SAS score (aTSA: ±7.41 MAE; rTSA: ±7.79 MAE), followed by the Constant score (aTSA: ±8.32 MAE; rTSA: ±8.30 MAE) and finally the ASES score (aTSA: ±10.86 MAE; rTSA: ±10.60 MAE). These prediction accuracy trends were maintained across the 3 different model input categories for each of the SAS, ASES, and Constant models at each postoperative time point. For patients who underwent aTSA, the XGBoost predictive models achieved 94%-97% accuracy in MCID with an area under the receiver operating curve (AUROC) between 0.90-0.97 and 89%-94% accuracy in SCB with an AUROC between 0.89-0.92 for the 3 clinical scores using the full feature set of inputs. For patients who underwent rTSA, the XGBoost predictive models achieved 95%-99% accuracy in MCID with an AUROC between 0.88-0.96 and 88%-92% accuracy in SCB with an AUROC between 0.81-0.89 for the 3 clinical scores using the full feature set of inputs. Our study demonstrated that the SAS score predictions are more accurate than the ASES and Constant predictions for multiple supervised machine learning techniques, despite requiring fewer input data for the SAS model. In addition, we predicted which patients will and will not achieve clinical improvement that exceeds the MCID and SCB thresholds for each score; this highly accurate predictive capability effectively risk-stratifies patients for a variety of outcome measures using only preoperative data. Level III; Retrospective Comparative Study" @default.
- W3205656918 created "2021-10-25" @default.
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- W3205656918 date "2022-06-01" @default.
- W3205656918 modified "2023-09-30" @default.
- W3205656918 title "Development of a predictive model for a machine learning–derived shoulder arthroplasty clinical outcome score" @default.
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- W3205656918 doi "https://doi.org/10.1053/j.sart.2021.09.005" @default.
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