Kumar Vikas, Schoch Bradley S, Allen Christine, Overman Steve, Teredesai Ankur, Aibinder William, Parsons Moby, Watling Jonathan, Ko Jiawei Kevin, Gobbato Bruno, Throckmorton Thomas, Routman Howard, Roche Christopher
KenSci, Seattle, WA, USA.
Department of Orthopedic Surgery, Mayo Clinic, Jacksonville, FL, USA.
J Shoulder Elbow Surg. 2022 May;31(5):e234-e245. doi: 10.1016/j.jse.2021.10.032. Epub 2021 Nov 20.
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 time points.
Clinical data from 2270 aTSA and 4198 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 time points. 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 time point. 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.
rTSA patients had significantly lower mean IR scores and significantly less mean IR score improvement than aTSA patients at each postoperative time point. Both aTSA and rTSA patients experienced significant improvements in their ability to perform activities of daily living (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.
Our machine learning study demonstrates that active internal rotation can be accurately predicted after aTSA and rTSA at multiple postoperative time points 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.
解剖型全肩关节置换术（aTSA）和反置型全肩关节置换术（rTSA）后内旋（IR）的改善情况难以预测，与aTSA患者相比，rTSA患者的内旋改善情况变异性更大且更有限。本研究的目的是量化并比较aTSA和rTSA患者的IR评分，并创建监督式机器学习模型，以预测aTSA和rTSA术后多个时间点的IR情况。
使用3种监督式机器学习技术分析了2270例aTSA患者和4198例rTSA患者的临床数据，以创建术后6个时间点通过IR评分测量的内旋预测模型。使用完整输入特征集和2个最小输入特征集进行预测。平均绝对误差（MAE）量化了每个模型在每个时间点实际IR评分与预测IR评分之间的差异。XGBoost算法的预测准确性还通过其区分哪些患者在术后2至3年将实现大于最小临床重要差异（MCID）和实质性临床获益（SCB）患者满意度阈值的IR评分临床改善的能力来量化。
在每个术后时间点，rTSA患者的平均IR评分均显著低于aTSA患者，且平均IR评分改善程度也显著低于aTSA患者。aTSA和rTSA患者在日常生活活动（ADL）能力方面均有显著改善；然而，aTSA患者进行这些ADL的可能性显著更高。使用术前输入的最小特征集，我们的机器学习算法在预测术后3个月至5年以上aTSA（MAE为0.92 - 1.18）和rTSA（MAE为1.03 - 1.25）的IR评分时具有同等准确性。此外，这些预测算法对aTSA患者预测哪些患者将实现MCID IR评分改善的准确率为90%，对rTSA患者为85%；对aTSA患者预测哪些患者将实现SCB IR评分改善的准确率为85%，对rTSA患者为77%，预测时间为术后2至3年。
我们的机器学习研究表明，使用术前输入的最小特征集，可以在术后多个时间点准确预测aTSA和rTSA后的主动内旋情况。这些预测算法准确地识别出哪些患者将实现以及哪些患者不会实现超过MCID和SCB患者满意度阈值的IR评分临床改善。
