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Background <p>Thoracolumbar fascia injury (TLFI) is common in Parkinson’s disease (PD) patients; however, the related risk factors are still controversial, and few studies have focused on clinical prediction models for TLFI. The&#xa0;aim of this study was to investigate the risk factors for TLFI in patients with PD and construct a clinical nomogram prediction model.</p> Methods <p>The clinical data of 351 patients with PD from October 2019 to September 2022 were retrospectively analyzed. MRI images were used to evaluate the presence or absence of TLFI. Binary logistic regression analysis was used to determine the independent risk factors for TLFI in patients with PD. The independent predictors were used as predictors to construct a nomogram model, and the predictive efficacy of the model was evaluated by receiver operating characteristic (ROC) curves and calibration curves (CCs). Decision curve analysis (DCA) was used to evaluate the clinical application value of the model.</p> Results <p>A higher UPDRS-III score, a lack of regular exercise, sagittal imbalance, sarcopenia and a low level of albumin were independent risk factors for TLFI in patients with PD. The area under the ROC curve was 0.974, and the CC and the ideal curve fit were good; the DCA. results showed that the model had high net benefits within all thresholds and had high clinical application value.</p> Conclusion <p>TLFI is closely related to higher a UPDRS-III score, a lack of daily exercise, sagittal imbalance, sarcopenia, and lower albumin levels in patients with PD. Patients with these factors should pay more attention to TLFI prevention and treatment.</p>

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Risk factors of thoracolumbar fascia injury for patients with Parkinson's disease and construction of a nomogram model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月27日

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  • Yitong Xiong,
  • Chengfang Chen,
  • Luyao Shi,
  • Yiyao Yang,
  • Chenjun Zhai,
  • Tao Jiang,
  • Zhanyun Ren,
  • Lei Shen

摘要

Background

Thoracolumbar fascia injury (TLFI) is common in Parkinson’s disease (PD) patients; however, the related risk factors are still controversial, and few studies have focused on clinical prediction models for TLFI. The aim of this study was to investigate the risk factors for TLFI in patients with PD and construct a clinical nomogram prediction model.

Methods

The clinical data of 351 patients with PD from October 2019 to September 2022 were retrospectively analyzed. MRI images were used to evaluate the presence or absence of TLFI. Binary logistic regression analysis was used to determine the independent risk factors for TLFI in patients with PD. The independent predictors were used as predictors to construct a nomogram model, and the predictive efficacy of the model was evaluated by receiver operating characteristic (ROC) curves and calibration curves (CCs). Decision curve analysis (DCA) was used to evaluate the clinical application value of the model.

Results

A higher UPDRS-III score, a lack of regular exercise, sagittal imbalance, sarcopenia and a low level of albumin were independent risk factors for TLFI in patients with PD. The area under the ROC curve was 0.974, and the CC and the ideal curve fit were good; the DCA. results showed that the model had high net benefits within all thresholds and had high clinical application value.

Conclusion

TLFI is closely related to higher a UPDRS-III score, a lack of daily exercise, sagittal imbalance, sarcopenia, and lower albumin levels in patients with PD. Patients with these factors should pay more attention to TLFI prevention and treatment.