Development and Validation of a Predictive Model for Chronic Postsurgical Pain After Arthroscopic Rotator Cuff Repair: A Prospective Cohort Study

Abstract

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

Data Sharing Statement

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

This study was approved by the Yangzhou University, China (YZUHL20210112), and written or verbal consent was obtained from the patients for their anonymized information to be published in this article. The patients collected from January to September 2021 were included for a retrospective cohort study using data extracted from the outcomes of patient visits to the hospital, and these patients provided verbal consent statements. Due to the epidemic situation of COVID-19, the discharged patients were followed up by telephone, and the purpose, steps, benefits, risks and other details of the study were informed to the patients so that patients and their families can cooperate. Verbal consent from these patients has been approved by the Institutional Review Board.

Patients after October 2021 were included for a prospective cohort study; therefore, this subset of patients provided written consent statements.

Acknowledgments

We would like to thank the researchers and study participants for their contributions.

Author Contributions

All authors made a significant contribution to the work reported, in the conception, study design, execution, acquisition of data, analysis, and interpretation; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.

Additional information

Funding

This work was supported by the National Orthopaedic and sports Rehabilitation Clinical Medical Research Center (2021-NCRC-CXJJ-PY-07).