| 蒲阳，母其文，郭志伟，唐雨露.老年患者阿尔兹海默病风险预测模型的建立和验证[J].神经损伤功能重建,2024,(7):392-396 |
| 老年患者阿尔兹海默病风险预测模型的建立和验证 |
| Development and Validation of a Risk Prediction Model for Alzheimer's Disease in ElderlyPatients |
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| DOI： |
| 中文关键词: 老年人群 阿尔茨海默病 多因素Logistic回归分析 列线图 |
| 英文关键词: the elderly Alzheimer's disease multivariate Logistic regression nomogram |
| 基金项目:川 北 医 学 院 2022
年度四川省基层卫
生事业发展研究中
心资助项目（基层
老年人群阿尔兹海
默病高风险因素及
预防策略研究，No.
SWFZ22-C-88） |
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| 摘要点击次数: 361 |
| 全文下载次数: 626 |
| 中文摘要: |
| 目的：建立老年患者阿尔兹海默病（AD）风险预测模型，并对模型进行验证。方法：选择2020年1月
至2022年12月在我院神经内科就诊的382例老年患者进行研究。以7∶3比例将患者分为模型组267例与
验证组115例。收集患者一般资料、临床认知相关指标、影像学资料及实验室指标。根据有无AD将模型组
患者分为2亚组，比较2亚组一般资料、临床认知相关指标、影像学资料及实验室指标，使用LASSO回归筛
选变量后行多因素Logistic回归分析，根据多因素分析结果建立列线图模型并进行验证。结果：模型组267
例患者中有67例（25.09%）患有AD，LASSO回归筛选出10个潜在的预测因素，分别为年龄、高血压病史、
AD家族史、RAVLT、FAQ、海马沟回比、大脑外侧裂比、载脂蛋白A1、载脂蛋白E、C反应蛋白。多因素Logistic回归分析结果显示：年龄、高血压病史、RAVLT、FAQ、海马沟回比、载脂蛋白A1、载脂蛋白E及C反应
蛋白为独立性影响因素（P＜0.05）。以模型组构建老年人群AD风险预测模型预测值的曲线下面积（AUC）
为0.968，95%CI为0.946～0.990。再以验证组数据进行外部验证，由验证组构建模型的AUC为0.957，95%
CI为0.932～0.983，与内部验证结果相接近。校准曲线结果显示，预测曲线与标准曲线基本拟合。决策曲
线分析结果显示：当该列线图模型预测神经内科老年患者AD概率阈值为0.15～0.88时，患者的净受益率
大于0。结论：神经内科老年患者AD患病主要受年龄、高血压病史、RAVLT等因素的影响，本研究建立的
列线图模型对预测AD患病风险具有较高的准确度与区分度。 |
| 英文摘要: |
| To develop and validate a risk prediction model for Alzheimer's disease (AD) in elderly
patients. Methods: A total of 382 elderly patients who visited the Department of Neurology in our hospital
from January 2020 to December 2022 were included in this study. The patients were divided into a model group
(267 cases) and a validation group (115 cases) at a ratio of 7 ∶ 3. Demographics, clinical cognition indicators,
imaging data and laboratory indicators were collected. The model group was further divided into AD and
non-AD subgroups. General information, clinical cognitive indicators, imaging data and laboratory indicators
were compared between the two subgroups. Variables were screened using LASSO regression, followed by
multivariate logistic regression. A nomogram model was developed and validated according to the results of
multivariate analysis. Results: In the model group, 67 out of 267 patients (25.09%) had AD. LASSO regression
identified 10 potential predictors, including age, history of hypertension, family history of AD, RAVLT, FAQ,
hippocampal sulcus ratio, lateral cerebral fissure ratio, apolipoprotein A1, apolipoprotein E, and C-reactive
protein. Multivariate logistic regression analysis showed that age, history of hypertension, RAVLT, FAQ,
hippocampal sulcus ratio, apolipoprotein A1, apolipoprotein E and C-reactive protein were independent
predictors (P<0.05). The area under the curve (AUC) of the AD risk prediction model for the elderly based on
the model group was 0.968 (95% CI 0.946～0.990). External validation using the validation group showed an
AUC of 0.957 (95% CI 0.932～0.983), which closely aligned with the internal validation results. The calibration
curve indicated a close fit to the standard curve. The decision curve analysis showed that the net benefit rate was
greater than 0 when the probability threshold of the nomograph model for predicting AD in elderly neurology
patients ranged from 0.15 to 0.88. Conclusion: The prevalence of AD in elderly neurology patients is
influenced mainly by factors such as age, history of hypertension, and RAVLT. The nomogram model developed
in this study exhibits high accuracy and discrimination in predicting the risk of AD. |
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