老年帕金森病患者脑深部电刺激术后神经认知延迟恢复预测模型列线图构建与验证

doi:10.3969/j.issn.1003-9198.2026.04.016

摘要/Abstract

摘要： 目的构建行丘脑底核(subthalamic nucleus,STN)脑深部电刺激术(deep brain stimulation,DBS)的老年PD患者术后神经认知延迟恢复(delayed neurocognitive recovery,dNCR)的列线图风险预测模型。方法回顾性分析2016—2024年在南京脑科医院行STN-DBS治疗的老年PD患者291例,分析STN-DBS术后发生dNCR与未发生dNCR患者的数据特征,将差异有统计学意义的指标包括重复经颅磁刺激(rMST)治疗史、患病年限、受教育年限、左旋多巴每日等效剂量(L-dopa equivalent daily dose,LEDD)、汉密尔顿焦虑量表(Hamilton Anxiety Scale,HAMA)、汉密尔顿抑郁量表(Hamilton Depression Scale,HAMD)、 MMSE、MoCA、总胆固醇、高血压史、糖尿病史、脑梗死史、脑萎缩史等,纳入多因素logistic回归分析,构建列线图预测模型并绘制校准曲线评价模型的准确性。结果 89例(30.58%)STN-DBS患者发生dNCR。多因素logistic回归筛选出LEDD、MMSE评分、受教育年限、rMST治疗史、脑萎缩史是STN-DBS术后dNCR的影响因素(P<0.05)。采用bootstrap法进行500次重抽样,对模型进行校正后绘制校准曲线,内部验证集中实际概率与预测概率之间具有一致性。结论 MMSE评分、受教育年限、rMST治疗史是老年PD患者STN-DBS术后dNCR的保护因素,LEDD、脑萎缩史是危险因素,构建的列线图模型在临床中应用有较好的预测价值。

关键词: 脑深部电刺激术, 神经认知延迟恢复, 帕金森病, 预测模型, 老年人

Abstract: Objective To construct a nomogram risk prediction model for delayed neurocognitive recovery (dNCR) in elderly patients with Parkinson’s disease (PD) after subthalamic nucleus (STN) deep brain stimulation (DBS). Methods A retrospective analysis was conducted on 291 elderly PD patients who underwent STN-DBS treatment at Nanjing Brain Hospital from 2016 to 2024. The characteristic variables were selected based on the data of patients with or without dNCR after STN-DBS. Fifteen characteristics, including prior repetitive transcranial magnetic stimulation(rTMS), disease duration, years of education, levodopa-equivalent daily dose (LEDD), scores of Hamilton Anxiety Scale(HAMA), Hamilton Depression Scale(HAMD), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment(MoCA), total cholesterol, and histories of hypertension, diabetes, cerebral infarction, and brain atrophy were enrolled in multivariable logistic regression analysis to construct a prediction nomogram model, and a calibration curve was drawn to evaluate the accuracy of the model. Results There were 89(30.58%) patients presenting with dNCR after STN-DBS. Multivariable logistic regression model identified LEDD, baseline MMSE score, years of education, rMST history, and brain atrophy as the influencing factors for dNCR after STN-DBS (all P<0.05). The model demonstrated good discriminative ability and calibration upon bootstrap validation (500 replicates), and showed consistency between the actual probability and the predicted probability. Conclusions Higher MMSE score, longer education duration, and previous rTMS treatment independently reduce the risk of post-operative dNCR, whereas elevated LEDD and brain atrophy are the risk factors. The constructed nomogram model showed robust discrimination and calibration, providing a clinically useful tool for individualized risk assessment before STN-DBS in elderly PD patients.

Key words: deep brain stimulation, delayed neurocognitive recovery, Parkinson’s disease, prediction model, aged

中图分类号:

R 749.1

杨瑶, 万美萍, 章文斌, 罗媛榕. 老年帕金森病患者脑深部电刺激术后神经认知延迟恢复预测模型列线图构建与验证[J]. 实用老年医学, 2026, 40(4): 411-416.

YANG Yao, WAN Meiping, ZHANG Wenbin, LUO Yuanrong. Construction and validation of a nomogram for predicting delayed recovery of neurocognitive function after deep brain stimulation in elderly patients with Parkinson’s disease[J]. Practical Geriatrics, 2026, 40(4): 411-416.

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