A six-month longitudinal evaluation significantly improves accuracy of predicting incipient Alzheimer's disease in mild cognitive impairment
| Autor: | Ali Asaei, Alzheimer's Disease Neuroimaging Initiative, Alvin H. Bachman, Asim M. Mubeen, Babak A. Ardekani, John J. Sidtis |
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| Rok vydání: | 2017 |
| Předmět: |
Male
0301 basic medicine medicine.medical_specialty Disease Neuropsychological Tests Longitudinal model 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Alzheimer Disease Predictive Value of Tests Early prediction medicine Humans Dementia Cognitive Dysfunction Radiology Nuclear Medicine and imaging Longitudinal Studies Psychiatry Cognitive impairment Aged Radiological and Ultrasound Technology business.industry medicine.disease Magnetic Resonance Imaging Clinical trial 030104 developmental biology Female Neurology (clinical) business Neurocognitive Algorithms Biomarkers 030217 neurology & neurosurgery Predictive modelling |
| Zdroj: | Journal of Neuroradiology. 44:381-387 |
| ISSN: | 0150-9861 |
| DOI: | 10.1016/j.neurad.2017.05.008 |
| Popis: | Rationale and objectives Early prediction of incipient Alzheimer's disease (AD) dementia in individuals with mild cognitive impairment (MCI) is important for timely therapeutic intervention and identifying participants for clinical trials at greater risk of developing AD. Methods to predict incipient AD in MCI have mostly utilized cross-sectional data. Longitudinal data enables estimation of the rate of change of variables, which along with the variable levels have been shown to improve prediction power. While some efforts have already been made in this direction, all previous longitudinal studies have been based on observation periods longer than one year, hence limiting their practical utility. It remains to be seen if follow-up evaluations within shorter intervals can significantly improve the accuracy of prediction in this problem. Our aim was to determine the added value of incorporating 6-month longitudinal data for predicting progression from MCI to AD. Materials and methods Using 6-months longitudinal data from 247 participants with MCI, we trained two Random Forest classifiers to distinguish between progressive MCI (n = 162) and stable MCI (n = 85) cases. These models utilized structural MRI, neurocognitive assessments, and demographic information. The first model (cross-sectional) only used baseline data. The second model (longitudinal) used data from both baseline and a 6-month follow-up evaluation allowing the model to additionally incorporate biomarkers’ rate of change. Results The longitudinal model (AUC = 0.87; accuracy = 80.2%) performed significantly better (P Conclusion Short-term longitudinal assessments significantly enhance the performance of AD prediction models. |
| Databáze: | OpenAIRE |
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