Jing Cui, Durong Chen, Jiajia Zhang, Yao Qin, Wenlin Bai, Yifei Ma, Rong Zhang, Hongmei Yu
{"title":"基于里程碑模型的认知筛查从轻度认知障碍到阿尔茨海默病转化的个体动态预测","authors":"Jing Cui, Durong Chen, Jiajia Zhang, Yao Qin, Wenlin Bai, Yifei Ma, Rong Zhang, Hongmei Yu","doi":"10.2174/1567205020666230526101524","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Identifying individuals with mild cognitive impairment (MCI) who are at increased risk of Alzheimer's Disease (AD) in cognitive screening is important for early diagnosis and prevention of AD.</p><p><strong>Objective: </strong>This study aimed at proposing a screening strategy based on landmark models to provide dynamic predictive probabilities of MCI-to-AD conversion according to longitudinal neurocognitive tests.</p><p><strong>Methods: </strong>Participants were 312 individuals who had MCI at baseline. The longitudinal neurocognitive tests were the Mini-Mental State Examination, Alzheimer Disease Assessment Scale-Cognitive 13 items, Rey Auditory Verbal Learning Test immediate, learning, and forgetting, and Functional Assessment Questionnaire. We constructed three types of landmark models and selected the optimal landmark model to dynamically predict 2-year probabilities of conversion. The dataset was randomly divided into training set and validation set at a ratio of 7:3.</p><p><strong>Results: </strong>The FAQ, RAVLT-immediate, and RAVLT-forgetting were significant longitudinal neurocognitive tests for MCI-to-AD conversion in all three landmark models. We considered Model 3 as the final landmark model (C-index = 0.894, Brier score = 0.040) and selected Model 3c (FAQ and RAVLT-forgetting as neurocognitive tests) as the optimal landmark model (C-index = 0.898, Brier score = 0.027).</p><p><strong>Conclusion: </strong>Our study shows that the optimal landmark model with a combination FAQ and RAVLTforgetting is feasible to identify the risk of MCI-to-AD conversion, which can be implemented in cognitive screening.</p>","PeriodicalId":10810,"journal":{"name":"Current Alzheimer research","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Landmark Model-based Individual Dynamic Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Cognitive Screening.\",\"authors\":\"Jing Cui, Durong Chen, Jiajia Zhang, Yao Qin, Wenlin Bai, Yifei Ma, Rong Zhang, Hongmei Yu\",\"doi\":\"10.2174/1567205020666230526101524\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Identifying individuals with mild cognitive impairment (MCI) who are at increased risk of Alzheimer's Disease (AD) in cognitive screening is important for early diagnosis and prevention of AD.</p><p><strong>Objective: </strong>This study aimed at proposing a screening strategy based on landmark models to provide dynamic predictive probabilities of MCI-to-AD conversion according to longitudinal neurocognitive tests.</p><p><strong>Methods: </strong>Participants were 312 individuals who had MCI at baseline. The longitudinal neurocognitive tests were the Mini-Mental State Examination, Alzheimer Disease Assessment Scale-Cognitive 13 items, Rey Auditory Verbal Learning Test immediate, learning, and forgetting, and Functional Assessment Questionnaire. We constructed three types of landmark models and selected the optimal landmark model to dynamically predict 2-year probabilities of conversion. The dataset was randomly divided into training set and validation set at a ratio of 7:3.</p><p><strong>Results: </strong>The FAQ, RAVLT-immediate, and RAVLT-forgetting were significant longitudinal neurocognitive tests for MCI-to-AD conversion in all three landmark models. We considered Model 3 as the final landmark model (C-index = 0.894, Brier score = 0.040) and selected Model 3c (FAQ and RAVLT-forgetting as neurocognitive tests) as the optimal landmark model (C-index = 0.898, Brier score = 0.027).</p><p><strong>Conclusion: </strong>Our study shows that the optimal landmark model with a combination FAQ and RAVLTforgetting is feasible to identify the risk of MCI-to-AD conversion, which can be implemented in cognitive screening.</p>\",\"PeriodicalId\":10810,\"journal\":{\"name\":\"Current Alzheimer research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Alzheimer research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2174/1567205020666230526101524\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Alzheimer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/1567205020666230526101524","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Landmark Model-based Individual Dynamic Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Cognitive Screening.
Background: Identifying individuals with mild cognitive impairment (MCI) who are at increased risk of Alzheimer's Disease (AD) in cognitive screening is important for early diagnosis and prevention of AD.
Objective: This study aimed at proposing a screening strategy based on landmark models to provide dynamic predictive probabilities of MCI-to-AD conversion according to longitudinal neurocognitive tests.
Methods: Participants were 312 individuals who had MCI at baseline. The longitudinal neurocognitive tests were the Mini-Mental State Examination, Alzheimer Disease Assessment Scale-Cognitive 13 items, Rey Auditory Verbal Learning Test immediate, learning, and forgetting, and Functional Assessment Questionnaire. We constructed three types of landmark models and selected the optimal landmark model to dynamically predict 2-year probabilities of conversion. The dataset was randomly divided into training set and validation set at a ratio of 7:3.
Results: The FAQ, RAVLT-immediate, and RAVLT-forgetting were significant longitudinal neurocognitive tests for MCI-to-AD conversion in all three landmark models. We considered Model 3 as the final landmark model (C-index = 0.894, Brier score = 0.040) and selected Model 3c (FAQ and RAVLT-forgetting as neurocognitive tests) as the optimal landmark model (C-index = 0.898, Brier score = 0.027).
Conclusion: Our study shows that the optimal landmark model with a combination FAQ and RAVLTforgetting is feasible to identify the risk of MCI-to-AD conversion, which can be implemented in cognitive screening.
期刊介绍:
Current Alzheimer Research publishes peer-reviewed frontier review, research, drug clinical trial studies and letter articles on all areas of Alzheimer’s disease. This multidisciplinary journal will help in understanding the neurobiology, genetics, pathogenesis, and treatment strategies of Alzheimer’s disease. The journal publishes objective reviews written by experts and leaders actively engaged in research using cellular, molecular, and animal models. The journal also covers original articles on recent research in fast emerging areas of molecular diagnostics, brain imaging, drug development and discovery, and clinical aspects of Alzheimer’s disease. Manuscripts are encouraged that relate to the synergistic mechanism of Alzheimer''s disease with other dementia and neurodegenerative disorders. Book reviews, meeting reports and letters-to-the-editor are also published. The journal is essential reading for researchers, educators and physicians with interest in age-related dementia and Alzheimer’s disease. Current Alzheimer Research provides a comprehensive ''bird''s-eye view'' of the current state of Alzheimer''s research for neuroscientists, clinicians, health science planners, granting, caregivers and families of this devastating disease.