Evaluating the Performance of Different Criteria in Diagnosing AD and Preclinical AD with the Bayesian Latent Class Model

期刊论文
发表日期：

2024年4月2日

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Xiaonan Wang,
Guanyu Niu,
J. Zhao,
H. Zhu,
F. Li,
J. Tian,
Z. Zhang,
G. Chen,
Y. He,
Qi Gao

摘要

Background

The diagnostic criteria for Alzheimer’s disease (AD) should be highly sensitive and specific. Clinicians have varying opinions on the different criteria, including the International Working Group-1 (IWG-1), International Working Group-2 (IWG-2), and AT(N) criteria. Few studies had evaluated the performance of these criteria in diagnosing AD and preclinical AD when the gold standard was absent.

Methods

We estimated and compared the performance of these criteria in diagnosing AD using data from 908 subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Additionally, 622 subjects were selected to evaluate and compare the performance of IWG-2 and AT(N) criteria in diagnosing preclinical AD. A novel approach, Bayesian latent class models with fixed effect dependent, was utilized to estimate the diagnostic accuracy of these criteria in detecting different AD statuses simultaneously.

Results

The sensitivity of the IWG-1, IWG-2, and AT(N) criteria in diagnosing AD was 0.850, 0.836, and 0.665. The specificity of these criteria was 0.788, 0.746, and 0.747. The IWG-1 criteria had the highest Youden Index in detecting AD. When diagnosing preclinical AD, the sensitivity of the IWG-2 and AT(N) criteria was 0.797 and 0.955. The specificity of these criteria was 0.922 and 0.720. The IWG-2 criteria had the highest Youden Index.

Conclusion

IWG-1 was more suitable than the IWG-2 and AT(N) criteria in detecting AD. IWG-2 criteria was more suitable than AT(N) criteria in detecting preclinical AD.