A machine learning and nomogram-based study: effect of applying biologically formulated platelet-rich plasma (PRP) on the degree of pain relief after rotator cuff repair and prediction modeling, integrating biomedicine and artificial intelligence.

Introduction: Rotator cuff repair, a common orthopedic surgery, often leads to considerable postoperative pain that delays functional recovery. Platelet-rich plasma (PRP) has been increasingly used as a biologically active autologous therapy to promote tendon healing and reduce inflammation, but its analgesic effects remain inconsistent across individuals. Conventional linear models may fail to account for complex patient-specific interactions such as age, body mass index (BMI), and preexisting inflammatory status. Methods: We developed a machine learning-based prediction model combined with a nomogram to assess the analgesic efficacy of PRP following rotator cuff repair. Clinical and demographic variables were incorporated to capture nonlinear relationships influencing pain reduction. Results: The machine learning framework demonstrated improved predictive accuracy compared with traditional models. The nomogram provided an interpretable and clinically applicable visualization of individualized pain-relief trajectories. Discussion: This study highlights the potential of integrating machine learning and nomogram approaches to enhance personalized prediction of PRP analgesic response. Such individualized forecasting tools may support tailored postoperative management strategies and optimize rehabilitation outcomes.