文章摘要
徐晶,袁满琼,方亚.基于联合模型的老年人轻度认知功能障碍发病风险预测研究[J].中华流行病学杂志,2022,43(2):269-276
基于联合模型的老年人轻度认知功能障碍发病风险预测研究
Research on predicting the risk of mild cognitive impairment in the elderly based on the joint model
收稿日期:2021-06-20  出版日期:2022-02-16
DOI:10.3760/cma.j.cn112338-20210620-00484
中文关键词: 轻度认知功能障碍  风险预测  联合模型  老年人
英文关键词: Mild cognitive impairment  Risk prediction  Joint model  Elderly
基金项目:国家自然科学基金面上项目(81973144)
作者单位E-mail
徐晶 厦门大学公共卫生学院/福建省高校卫生技术评估重点实验室, 厦门 361102  
袁满琼 厦门大学公共卫生学院/福建省高校卫生技术评估重点实验室, 厦门 361102  
方亚 厦门大学公共卫生学院/福建省高校卫生技术评估重点实验室, 厦门 361102 fangya@xmu.edu.cn 
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中文摘要:
      目的 构建并比较基于6种不同认知功能测量量表的老年人轻度认知功能障碍(MCI)发病风险动态预测模型。方法 基于2005-2020年阿尔茨海默病神经影像学倡议的纵向数据,以简易精神状态量表(MMSE)、社会功能活动调查表(FAQ)、阿尔茨海默病评定量表认知分量表(ADAS-Cog11、ADAS-Cog13)、阿尔茨海默病评定量表延迟词语回忆(ADASQ4)和Rey听觉词语学习即刻测验(RAVLT_immediate)作为纵向认知功能评估指标评估认知功能的纵向变化,利用联合模型分析纵向认知功能评估指标变化轨迹与生存结局MCI之间的关系,构建老年人MCI发病风险预测模型,以线性混合模型对纵向评估指标的变化轨迹建模,以比例风险模型对生存过程建模,通过关联参数(α)将两个子模型联系起来。采用受试者工作特征曲线下面积(AUC)评价模型在(ttt)随访时间段的预测效能,其中t选取第30、42、54个月,Δt选取15和21个月。基于预测模型,选取1名研究对象示例进行MCI发病风险个体动态预测。结果 最终纳入544名基线认知状态正常的老年人(≥60岁),其中119名(21.9%)在随访过程中发生MCI视为病例组,425名始终保持正常视为对照组。联合模型提示6种评估指标的纵向轨迹与MCI发生风险均相关(P<0.001),其中MMSE、RAVLT_immediate评分每纵向增加1分,MCI发生风险对应降低32.3%(HR=0.677,95%CI:0.541~0.846)和10.8%(HR=0.892,95%CI:0.865~0.919);FAQ、ADAS-Cog11、ADAS-Cog13、ADASQ4评分每纵向增加1分,MCI发生风险对应增加53.2%(HR=1.532,95%CI:1.393~1.686)、36.2%(HR=1.362,95%CI:1.268~1.462)、23.2%(HR=1.232,95%CI:1.181~1.285)、85.1%(HR=1.851,95%CI:1.629~2.104)。AUC结果显示RAVLT_immediate(0.760 2)和ADASQ4(0.755 8)的平均预测效能较高,之后依次为ADAS-Cog13(0.743 7)、ADAS-Cog11(0.715 3)、FAQ(0.700 8)和MMSE(0.629 5)。基于ADASQ4的联合模型进行MCI发病风险个体预测,示例个体随访5年后、10年后发生MCI的平均概率分别为8%、40%。结论 仅针对记忆测验的RAVLT_immediate与ADASQ4在预测MCI发生风险上具有较高的准确性,以其作为纵向认知功能评估指标构建联合模型,结果可为实现老年人MCI风险预测提供依据。
英文摘要:
      Objective To construct and compare the dynamic prediction models of the risk of mild cognitive impairment (MCI) in the elderly based on six different cognitive function scales.Methods Based on longitudinal data from the Alzheimer's Disease Neuroimaging Initiative from 2005 to 2020, Mini-mental state examination (MMSE), functional activities questionnaire (FAQ), Alzheimer's disease assessment scale-cognitive (ADAS-Cog) 11, ADAS-Cog13, ADAS delayed word recall (ADASQ4), and Rey auditory verbal learning test (RAVLT)_immediate were used as longitudinal cognitive function evaluation indicators to assess the longitudinal changes in cognitive function. The joint model was used to analyze association between indicators variation trajectory and survival outcome MCI, and construct the risk prediction model of MCI in the elderly, the linear mixed model was constructed the longitudinal sub-model which described the evolution of a repeated measure over time, a proportional hazards model was constructed the survival sub-model, and the two sub-models were connected through the correlation parameter (α). The areas under the receiver operator characteristic curve (AUC) were used to evaluate the predictive efficacy of the model in the follow-up period of (t, tt). The starting point t was selected at the 30th, 42nd, and 54th month, and the Δt was selected as 15 and 21 months. Based on the prediction model, an example of the research object was selected for dynamic individual predictions of the risk of MCI. Results Finally, 544 older adults (aged 60 years and above) with normal baseline cognitive status were included, of which 119 cases (21.9%) had MCI during the follow-up process were regarded as the case group, and 425 cases remained normal as the control group. The joint model suggests that the longitudinal trajectories of the six evaluation indicators are all related to the risk of MCI (P<0.001). The risk of MCI decreased by 32.3% (HR=0.677, 95%CI: 0.541-0.846) and 10.8% (HR=0.892, 95%CI: 0.865-0.919) for each one-point increase of MMSE and RAVLT_immediate longitudinal scores. The risk of MCI increased by 53.2% (HR=1.532, 95%CI: 1.393-1.686), 36.2% (HR=1.362, 95%CI: 1.268-1.462), 23.2% (HR=1.232, 95%CI: 1.181-1.285), and 85.1% (HR=1.851, 95%CI:1.629-2.104) for each one-point increase of FAQ, ADAS-Cog11, ADAS-Cog13, and ADASQ4 longitudinal scores. AUC results show that RAVLT_immediate (0.760 2) and ADASQ4 (0.755 8) have higher average prediction efficiency, followed by ADAS-Cog13 (0.743 7), ADAS-Cog11 (0.715 3), FAQ (0.700 8) and MMSE (0.629 5). ADASQ4 joint model was used to provide a dynamic individual prediction of the risk of MCI. The average probability of MCI after five years of follow-up and ten years of follow-up in the example individuals were 8% and 40%, respectively. Conclusions The RAVLT_immediate and ADASQ4 scales, which are only for memory tests, have high accuracy in predicting the risk of MCI. Using the RAVLT_immediate and ADASQ4 scales as longitudinal cognitive function evaluation indicators to construct a joint model, the results can provide a basis for realizing MCI risk prediction for the elderly.
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