Purpose: Chronic pain management continues to present a significant challenge following arthroscopic shoulder surgery. Our purpose was to detect chronic postsurgical pain (CPSP) in patients who had undergone arthroscopic rotator cuff repair (ARCR) and develop a nomogram capable of predicting the associated risk.
Patients and Methods: We collected the demographic and clinical data of 240 patients undergoing ARCR in our hospital from January 2021 to May 2022. The pain level was monitored and evaluated three months after ARCR. LASSO regression was used to screen out pain-predicting factors, which were subsequently used to construct a nomogram. Internal validation was carried out using Bootstrap resampling. The data of 78 patients who underwent ARCR in our hospital from August 2022 to December 2022 were also collected for external verification of the nomogram. The predictive model was evaluated using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).
Results: Age, duration of preoperative shoulder pain (DPSP), C-reactive protein (CRP), number of tear tendons, and American Shoulder and Elbow Surgical Score (ASES) were screened by LASSO regression as predictive factors for CPSP. These factors were then used to construct a chronic pain risk nomogram. The area under the curve (AUC) of the predictive and validation models were 0.756 (95% CI: 0.6386– 0.8731) and 0.806 (95% CI: 0.6825– 0.9291), respectively. Furthermore, the calibration curves and decision curve analysis (DCA) for both models indicated strong performance, affirming the reliability of this predictive model.
Conclusion: The CPSP risk model that has been developed exhibits strong predictive capabilities and practical utility. It offers valuable support to clinical healthcare professionals in making informed treatment decisions, reducing the unnecessary use of analgesic drugs, and optimizing the allocation of medical resources.
Keywords: arthroscopic rotator cuff repair, chronic postoperative pain, nomogram, predictive model, risk factors