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Background <p>Vertebral recompression after percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fractures (OVCFs) may lead to recurrent pain, deformity, and neurological impairment, compromising prognosis and quality of life.</p> Objective <p>To identify independent risk factors for postoperative recompression and develop predictive models for risk assessment.</p> Methods <p>We retrospectively analyzed 284 OVCF patients treated with PKP, grouped by recompression status. Predictors were screened using univariate and correlation analyses. Multicollinearity was assessed using variance inflation factor (VIF). A multivariable logistic regression model was constructed and validated via 10-fold cross-validation and temporal validation.</p> Results <p>Five independent predictors were identified: incomplete anterior cortex (odds ratio [OR] = 9.38), high paravertebral muscle fat infiltration (OR = 218.68), low vertebral CT value (OR = 0.87), large Cobb change (OR = 1.45), and high vertebral height recovery rate (OR = 22.64). The logistic regression model achieved strong performance: accuracy 97.67%, precision 97.06%, recall 97.06%, F1 score 97.06%, specificity 98.08%, area under the receiver operating characteristic curve (AUC) 0.998. Machine learning models (e.g., random forest) were also evaluated but did not outperform logistic regression in accuracy or interpretability.</p> Conclusion <p>Five imaging-based predictors of vertebral recompression were identified. The logistic regression model showed excellent predictive accuracy and generalizability, supporting its clinical utility for early risk stratification and personalized decision-making in OVCF patients undergoing PKP.</p>

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Evaluation and analysis of risk factors for fractured vertebral recompression post-percutaneous kyphoplasty: a retrospective cohort study based on logistic regression analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月29日

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  • YingLun Zhao,
  • Bo Li,
  • Lu Qian,
  • XueMing Chen,
  • YanHui Wang,
  • LiBin Cui,
  • Yuan Xin,
  • Liang Liu

摘要

Background

Vertebral recompression after percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fractures (OVCFs) may lead to recurrent pain, deformity, and neurological impairment, compromising prognosis and quality of life.

Objective

To identify independent risk factors for postoperative recompression and develop predictive models for risk assessment.

Methods

We retrospectively analyzed 284 OVCF patients treated with PKP, grouped by recompression status. Predictors were screened using univariate and correlation analyses. Multicollinearity was assessed using variance inflation factor (VIF). A multivariable logistic regression model was constructed and validated via 10-fold cross-validation and temporal validation.

Results

Five independent predictors were identified: incomplete anterior cortex (odds ratio [OR] = 9.38), high paravertebral muscle fat infiltration (OR = 218.68), low vertebral CT value (OR = 0.87), large Cobb change (OR = 1.45), and high vertebral height recovery rate (OR = 22.64). The logistic regression model achieved strong performance: accuracy 97.67%, precision 97.06%, recall 97.06%, F1 score 97.06%, specificity 98.08%, area under the receiver operating characteristic curve (AUC) 0.998. Machine learning models (e.g., random forest) were also evaluated but did not outperform logistic regression in accuracy or interpretability.

Conclusion

Five imaging-based predictors of vertebral recompression were identified. The logistic regression model showed excellent predictive accuracy and generalizability, supporting its clinical utility for early risk stratification and personalized decision-making in OVCF patients undergoing PKP.