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<p>Re-tear remains a frequent complication after arthroscopic rotator cuff repair (ARCR), adversely affecting functional recovery. This study aimed to develop and validate a clinical prediction model for re-tear risk following ARCR. This retrospective study included 570 patients undergoing ARCR from 2018 to 2023, randomly assigned to training (n = 399) and validation (n = 171) cohorts. Demographic, imaging, and intraoperative variables were analyzed. Independent predictors from multivariable logistic regression were used to construct a nomogram. Model performance was assessed by discrimination, calibration, and decision curve analysis (DCA). Six independent predictors were identified: age, maximal tear diameter, Patte grade III tendon retraction, high-grade fatty infiltration (Goutallier ≥ 3), positive tangent sign, and intraoperative repair tension. The nomogram showed good discrimination (AUC = 0.76 training; 0.74 validation) and calibration (Hosmer–Lemeshow <i>P</i> = 0.735 and 0.721, respectively). DCA demonstrated clinical utility across wide threshold probabilities, outperforming “treat-all” and “treat-none” strategies. This nomogram, integrating preoperative imaging and intraoperative findings, provides individualized re-tear risk estimates after ARCR, offering a practical tool for surgical planning and patient counseling.</p>

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Development and validation of a risk prediction model for re-tear after arthroscopic rotator cuff repair

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月5日

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  • Yiwen Hu,
  • Shaojian Chen,
  • Heng Zhang,
  • Changzhao Huang,
  • Zixi Hu

摘要

Re-tear remains a frequent complication after arthroscopic rotator cuff repair (ARCR), adversely affecting functional recovery. This study aimed to develop and validate a clinical prediction model for re-tear risk following ARCR. This retrospective study included 570 patients undergoing ARCR from 2018 to 2023, randomly assigned to training (n = 399) and validation (n = 171) cohorts. Demographic, imaging, and intraoperative variables were analyzed. Independent predictors from multivariable logistic regression were used to construct a nomogram. Model performance was assessed by discrimination, calibration, and decision curve analysis (DCA). Six independent predictors were identified: age, maximal tear diameter, Patte grade III tendon retraction, high-grade fatty infiltration (Goutallier ≥ 3), positive tangent sign, and intraoperative repair tension. The nomogram showed good discrimination (AUC = 0.76 training; 0.74 validation) and calibration (Hosmer–Lemeshow P = 0.735 and 0.721, respectively). DCA demonstrated clinical utility across wide threshold probabilities, outperforming “treat-all” and “treat-none” strategies. This nomogram, integrating preoperative imaging and intraoperative findings, provides individualized re-tear risk estimates after ARCR, offering a practical tool for surgical planning and patient counseling.