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Predicting progression from cognitive impairment to Alzheimer's disease with the Disease State Index
Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland.
VTT Technical Research Centre of Finland, Tampere, Finland.
VTT Technical Research Centre of Finland, Tampere, Finland.
VTT Technical Research Centre of Finland, Tampere, Finland.
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2015 (English)In: Current Alzheimer Research, ISSN 1567-2050, E-ISSN 1875-5828, Vol. 12, no 1, p. 69-79Article in journal (Refereed) Published
Abstract [en]

We evaluated the performance of the Disease State Index (DSI) method when predicting progression to Alzheimer's disease (AD) in patients with subjective cognitive impairment (SCI), amnestic or non-amnestic mild cognitive impairment (aMCI, naMCI). The DSI model measures patients' similarity to diagnosed cases based on available data, such as cognitive tests, the APOE genotype, CSF biomarkers and MRI. We applied the DSI model to data from the DESCRIPA cohort, where non-demented patients (N=775) with different subtypes of cognitive impairment were followed for 1 to 5 years. Classification accuracies for the subgroups were calculated with the DSI using leave-one-out crossvalidation. The DSI's classification accuracy in predicting progression to AD was 0.75 (AUC=0.83) in the total population, 0.70 (AUC=0.77) for aMCI and 0.71 (AUC=0.76) for naMCI. For a subset of approximately half of the patients with high or low DSI values, accuracy reached 0.86 (all), 0.78 (aMCI), and 0.85 (naMCI). For patients with MRI or CSF biomarker data available, theywere 0.78 (all), 0.76 (aMCI) and 0.76 (naMCI), while for clear cases the accuracies rose to 0.90 (all), 0.83 (aMCI) and 0.91 (naMCI). The results show that the DSI model can distinguish between clear and ambiguous cases, assess the severity of the disease and also provide information on the effectiveness of different biomarkers. While a specific test or biomarker may confound analysis for an individual patient, combining several different types of tests and biomarkers could be able to reveal the trajectory of the disease and improve the prediction of AD progression.

Place, publisher, year, edition, pages
Bentham Science Publishers , 2015. Vol. 12, no 1, p. 69-79
Keywords [en]
Alzheimer's disease, cerebrospinal fluid (CSF), computer-assisted diagnosis, dementia, DESCRIPA, magnetic resonance imaging (MRI), mild cognitive impairment (MCI)
National Category
Neurology
Identifiers
URN: urn:nbn:se:oru:diva-74050DOI: 10.2174/1567205012666141218123829ISI: 000347299500008PubMedID: 25523428Scopus ID: 2-s2.0-84921850733OAI: oai:DiVA.org:oru-74050DiVA, id: diva2:1313960
Available from: 2019-05-07 Created: 2019-05-07 Last updated: 2019-05-20Bibliographically approved

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Freund-Levi, Yvonne

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