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Purpose <p>To establish a risk prediction model for postoperative outcomes in patients with ossification of the posterior longitudinal ligament (OPLL), identify key risk factors, and provide a theoretical basis for personalized treatment.</p> Methods <p>Clinical data of 384 OPLL patients undergoing cervical spine surgery were retrospectively analyzed. Potential predictors were screened using univariate analysis, and independent risk factors were determined through multivariate logistic regression. A dynamic nomogram was constructed, and its performance was evaluated with the concordance index (C-index), receiver operating characteristic (ROC) curve, Hosmer–Lemeshow test, and decision curve analysis (DCA).</p> Results <p>Four independent risk factors were identified and incorporated into the prediction model. The model demonstrated high accuracy and stability, with C-index values of 0.880 (training cohort) and 0.915 (validation cohort). ROC curve analysis confirmed excellent discrimination, while calibration and DCA showed good clinical applicability.</p> Conclusion <p>Advanced age, history of trauma, surgical approach, and spinal cord T2 hyperintensity on imaging are independent predictors of postoperative outcomes in OPLL patients. The developed nomogram provides a reliable tool for individualized risk assessment and clinical decision-making. These findings warrant further validation in multicenter cohorts.</p>

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Analysis of related factors affecting the postoperative clinical outcome of cervical ossification of the posterior longitudinal ligament

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Shuqing Chen,
  • Changlin Lv,
  • Xuanyu Dong,
  • Ziang Zhang,
  • Jianyi Li,
  • Tianyu Bai,
  • Xiaofan Du,
  • Guodong Zhang,
  • Jianwei Guo,
  • Jiale Shao,
  • Jiayan Li,
  • Yukun Du,
  • Jun Dong,
  • Yongming Xi

摘要

Purpose

To establish a risk prediction model for postoperative outcomes in patients with ossification of the posterior longitudinal ligament (OPLL), identify key risk factors, and provide a theoretical basis for personalized treatment.

Methods

Clinical data of 384 OPLL patients undergoing cervical spine surgery were retrospectively analyzed. Potential predictors were screened using univariate analysis, and independent risk factors were determined through multivariate logistic regression. A dynamic nomogram was constructed, and its performance was evaluated with the concordance index (C-index), receiver operating characteristic (ROC) curve, Hosmer–Lemeshow test, and decision curve analysis (DCA).

Results

Four independent risk factors were identified and incorporated into the prediction model. The model demonstrated high accuracy and stability, with C-index values of 0.880 (training cohort) and 0.915 (validation cohort). ROC curve analysis confirmed excellent discrimination, while calibration and DCA showed good clinical applicability.

Conclusion

Advanced age, history of trauma, surgical approach, and spinal cord T2 hyperintensity on imaging are independent predictors of postoperative outcomes in OPLL patients. The developed nomogram provides a reliable tool for individualized risk assessment and clinical decision-making. These findings warrant further validation in multicenter cohorts.