Using deep learning to predict postoperative pain in reverse shoulder arthroplasty patients

Background: 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). Methods: 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. Results: 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. Conclusion: 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.