Schneller Tim, Cina Andrea, Moroder Philipp, Scheibel Markus, Lazaridou Asimina
Department of Shoulder and Elbow Surgery, Schulthess Clinic, Zurich, Switzerland.
Spine Center, Schulthess Clinic, Zurich, Switzerland.
JSES Int. 2024 Dec 19;9(3):748-755. doi: 10.1016/j.jseint.2024.11.020. eCollection 2025 May.
Most research on shoulder arthroplasty has predominantly concentrated on optimizing treatment to enhance shoulder function with comparatively less emphasis on postsurgical pain. Yet, pain is an equally significant or even more important outcome in orthopedic surgery. The aim of this study was to develop a deep learning algorithm for predicting postsurgical pain after reverse total shoulder arthroplasty (rTSA).
Clinical data of rTSA patients were extracted from a local shoulder arthroplasty registry and used to build an artificial neural network, which was set up with input from 34 preoperative features including demographics, disease-related information, clinical, and self-report assessments. The target variable was a binary classification derived from a numeric pain rating scale (0-10): if the pain scored 3 or higher, it was classified as positive; if the pain score was 2 or lower, it was classified as negative. The model was internally validated with a test dataset that was comprised of 20% of the whole dataset. Model performance was evaluated on the testset using the metrics accuracy, precision, recall, and f1-score.
Our model, including data from 1707 patients (pain: n = 705, no pain: n = 1002), achieved a 63% accuracy rate in predicting postsurgical pain 2 years following rTSA. Identification of the most critical factors indicating low postsurgical pain was performed by SHapley Additive exPlanations analysis, which included a low American Society of Anesthesiologists physical status classification, a low Quick Disability of the Arm, Shoulder and Hand questionnaire score, private insurance status, primary OA, being admitted due to illness as opposed to due to an accident, low pain levels, occasional alcohol consumption, low shoulder pain and disability index and functional scores.
We successfully developed an artificial neural network to predict postsurgical pain after rTSA. Additional efforts are still required to refine the models' performance, such as including further parameters predictive of pain and considering other machine learning algorithms. In a clinical setting, the implementation of such a prediction model could optimize surgical indications and help manage patient expectations more effectively.
大多数关于肩关节置换术的研究主要集中在优化治疗以增强肩部功能，而相对较少关注术后疼痛。然而，疼痛在骨科手术中是同样重要甚至更为重要的结果。本研究的目的是开发一种深度学习算法，用于预测反式全肩关节置换术（rTSA）后的术后疼痛。
从当地肩关节置换术登记处提取rTSA患者的临床数据，并用于构建人工神经网络，该网络的输入包括34个术前特征，如人口统计学、疾病相关信息、临床和自我报告评估。目标变量是从数字疼痛评分量表（0-10）得出的二元分类：如果疼痛评分3或更高，则分类为阳性；如果疼痛评分2或更低，则分类为阴性。该模型使用由整个数据集的20%组成的测试数据集进行内部验证。在测试集上使用准确率、精确率、召回率和F1分数等指标评估模型性能。
我们的模型纳入了1707例患者的数据（疼痛：n = 705，无疼痛：n = 1002），在预测rTSA术后2年的术后疼痛方面达到了63%的准确率。通过SHapley加性解释分析确定了表明术后疼痛较低的最关键因素，包括美国麻醉医师协会身体状况分类较低、手臂、肩部和手部快速残疾问卷得分较低、私人保险状况、原发性骨关节炎、因疾病而非事故入院、疼痛水平较低、偶尔饮酒、肩部疼痛和残疾指数及功能评分较低。
我们成功开发了一种人工神经网络来预测rTSA后的术后疼痛。仍需要进一步努力来优化模型性能，例如纳入更多预测疼痛的参数并考虑其他机器学习算法。在临床环境中，实施这样的预测模型可以优化手术指征并更有效地帮助管理患者期望。
