基于MRI定量参数构建老年帕金森患者认知功能障碍的风险预测模型

doi:10.3969/j.issn.1003-9198.2026.04.007

Abstract

Abstract: Objective To explore the risk factors of cognitive dysfunction in the elderly patients with Parkinson’s disease (PD) based on quantitative parameters of magnetic resonance imaging (MRI). Methods A retrospective study was conducted, enrolling 150 elderly PD patients admitted to Xi’an International Medical Center Hospital from September 2021 to January 2025. These patients were divided into an observation group (n=52) and a control group (n=98) based on the occurrence of cognitive dysfunction. The MRI parameters, general clinical data, and laboratory indicators of the two groups were collected and compared. Logistic regression analysis was used to investigate the factors influencing the occurrence of cognitive dysfunction in elderly PD patients. ROC curve and calibration curve were used to verify the model. Results Compared with the control group, the observation group showed increased apparent diffusion coefficient (ADC) and H-Y grading, prolonged disease course, decreased gray matter fractional anisotropy (FA) and uric acid (UA) levels (P<0.05). Logistic regression analysis revealed that substantia nigra ADC (OR=2.192), substantia nigra FA (OR=0.464), disease duration (OR=1.893), H-Y grading (OR=1.923), and UA (OR=0.488) were independent predictors of cognitive dysfunction in elderly PD patients (P<0.05). A risk prediction model for cognitive dysfunction in PD patients was constructed based on the influencing factors. The ROC curve results showed that the area under the curve (AUC) of this prediction model for cognitive dysfunction in PD patients was 0.910 (95%CI: 0.852-0.950), with a sensitivity of 90.54% and a specificity of 83.65%, and the Youden index was 0.729. The calibration curve demonstrated that the predictive model exhibited good fit (Hosmer-Lemeshow χ²=0.83, P=0.437). Conclusions The substantia nigra ADC, FA, disease duration, H-Y grading, and UA levels are influencing factors for cognitive dysfunction in elderly PD patients. The risk prediction model for cognitive dysfunction constructed based on these factors in elderly PD patients, demonstrates significant assessment value.

Key words: Parkinson’s disease, aged, magnetic resonance imaging, cognitive dysfunction, risk prediction model

CLC Number:

R 741

LU Shuang, LI Xia, REN Qianmeng. Risk prediction model of cognitive dysfunction in elderly patients with Parkinson’s disease based on quantitative MRI parameters[J]. Practical Geriatrics, 2026, 40(4): 362-366.

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Risk prediction model of cognitive dysfunction in elderly patients with Parkinson’s disease based on quantitative MRI parameters

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