Scholarlink

Objective <p>To develop a nomogram that incorporating clinical data and transcranial sonography (TCS) markers for predicting Parkinson’s disease patients with cognitive impairment (PD-CI).</p> Methods <p>149 PD with normal cognition (PD-NCI), 117 PD with mild CI (PD-MCI), and 79 PD with dementia (PDD) were included as the training set, 145 PD patients and 154 age- and gender-matched volunteers were enrolled as the test set and control group, respectively. Logistic regression was utilized to screen risk factors for predicting PD-CI, and a nomogram was generated.</p> Results <p>A predictive model was developed using age, education level, homocysteine, substantia nigra hyperechogenicity (SNH), and third ventricle (V3) width. Receiver operating characteristic curves indicated that V3 width, homocysteine, and the model effectively distinguished between PD-NCI and PD-CI, PDD and non-PDD, as well as PDD and PD-MCI, with the predictive model yielding the highest area under the curve. Calibration curves showed that the predictions of the final model in both the training and test sets closely matched the actual probabilities, while clinical decision curves suggested that the nomogram provided a substantial clinical net benefit.</p> Conclusion <p>A predictive model for PD-CI that incorporates age, education level, plasma homocysteine levels, SNH, and V3 width has been developed and validated.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A nomogram combined substantia nigra hyperechogenicity with third ventricular width assessed by transcranial sonography in prediction Parkinson’s disease-related cognitive impairment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月9日

查看全文
  • Qi-hui Yang,
  • Chao Hou,
  • Ming-xing Li

摘要

Objective

To develop a nomogram that incorporating clinical data and transcranial sonography (TCS) markers for predicting Parkinson’s disease patients with cognitive impairment (PD-CI).

Methods

149 PD with normal cognition (PD-NCI), 117 PD with mild CI (PD-MCI), and 79 PD with dementia (PDD) were included as the training set, 145 PD patients and 154 age- and gender-matched volunteers were enrolled as the test set and control group, respectively. Logistic regression was utilized to screen risk factors for predicting PD-CI, and a nomogram was generated.

Results

A predictive model was developed using age, education level, homocysteine, substantia nigra hyperechogenicity (SNH), and third ventricle (V3) width. Receiver operating characteristic curves indicated that V3 width, homocysteine, and the model effectively distinguished between PD-NCI and PD-CI, PDD and non-PDD, as well as PDD and PD-MCI, with the predictive model yielding the highest area under the curve. Calibration curves showed that the predictions of the final model in both the training and test sets closely matched the actual probabilities, while clinical decision curves suggested that the nomogram provided a substantial clinical net benefit.

Conclusion

A predictive model for PD-CI that incorporates age, education level, plasma homocysteine levels, SNH, and V3 width has been developed and validated.