Title:Landmark Model-based Individual Dynamic Prediction of Conversion from Mild Cognitive Impairment to Alzheimer’s Disease using Cognitive
Screening
Volume: 20
Issue: 2
Author(s): Jing Cui, Durong Chen, Jiajia Zhang, Yao Qin, Wenlin Bai, Yifei Ma, Rong Zhang and Hongmei Yu*
Affiliation:
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 Xin Jian South Road, Taiyuan,
China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, 56 XinJian South Road, Taiyuan, China
Keywords:
Alzheimer’s Disease, mild cognitive impairment, cognitive screening, neurocognitive tests, dynamic prediction, diagnosis and prevention.
Abstract:
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.
