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PLoS One. 2014 Aug 20;9(8):e105542. doi: 10.1371/journal.pone.0105542. eCollection 2014.

Predicting progression of Alzheimer's disease using ordinal regression.

Author information

1
Department of Neuroimaging, Institute of Psychiatry, King's College London, London, United Kingdom.
2
Department of Neuroimaging, Institute of Psychiatry, King's College London, London, United Kingdom; Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
3
Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy.
4
INSERM U 558, University of Toulouse, Toulouse, France.
5
Aristotle University of Thessaloniki, Thessaloniki, Greece.
6
Medical University of Lodz, Lodz, Poland.
7
University of Eastern Finland and University Hospital of Kuopio, Kuopio, Finland.
8
NIHR Biomedical Research Centre for Mental Health at South London, London, United Kingdom.
9
Department of Neuroimaging, Institute of Psychiatry, King's College London, London, United Kingdom; NIHR Biomedical Research Centre for Mental Health at South London, London, United Kingdom; Maudsley NHS Foundation Trust, London, United Kingdom; Institute of Psychiatry, King's College London, London, United Kingdom.
10
Department of Neuroimaging, Institute of Psychiatry, King's College London, London, United Kingdom; NIHR Biomedical Research Centre for Mental Health at South London, London, United Kingdom; NIHR Biomedical Research Unit for Dementia at South London, London, United Kingdom; Maudsley NHS Foundation Trust, London, United Kingdom; Institute of Psychiatry, King's College London, London, United Kingdom.

Abstract

We propose a novel approach to predicting disease progression in Alzheimer's disease (AD)--multivariate ordinal regression--which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression--the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer's Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. The ORCHID score was computed for all subjects. We showed that this measure significantly correlated with MMSE at 12 months (ρ =  -0.64, ADNI and ρ =  -0.59, AddNeuroMed). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. reverters and healthy controls who later progressed to MCI), moderately late converters (12-24 months) and late converters (24-36 months). A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive.

PMID:
25141298
PMCID:
PMC4139338
DOI:
10.1371/journal.pone.0105542
[Indexed for MEDLINE]
Free PMC Article

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