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Predicting Progression from Cognitive Impairment to Alzheimer's Disease with the Disease State Index

Hall, Anette; Mattila, Jussi; Koikkalainen, Juha; Loejonen, Jyrki; Wolz, Robin; Scheltens, Philip; Frisoni, Giovanni; Tsolaki, Magdalini; Nobili, Flavio and Freund-Levi, Yvonne, et al. (2015) In Current Alzheimer Research 12(1). p.69-79
Abstract
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 DE-SCRIPA 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 cross-validation. The DSI's classification accuracy in predicting progression... (More)
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 DE-SCRIPA 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 cross-validation. 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, they were 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. (Less)
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published
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keywords
Alzheimer's disease, cerebrospinal fluid (CSF), computer-assisted, diagnosis, dementia, DESCRIPA, magnetic resonance imaging (MRI), mild, cognitive impairment (MCI)
in
Current Alzheimer Research
volume
12
issue
1
pages
69 - 79
publisher
Bentham Science Publishers
external identifiers
  • wos:000347299500008
ISSN
1875-5828
DOI
10.2174/1567205012666141218123829
language
English
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yes
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14542551-dfb2-497f-836a-28e7215857d7 (old id 5076042)
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2015-03-02 07:08:53
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2016-04-15 13:52:28
@article{14542551-dfb2-497f-836a-28e7215857d7,
  abstract     = {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 DE-SCRIPA 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 cross-validation. 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, they were 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.},
  author       = {Hall, Anette and Mattila, Jussi and Koikkalainen, Juha and Loejonen, Jyrki and Wolz, Robin and Scheltens, Philip and Frisoni, Giovanni and Tsolaki, Magdalini and Nobili, Flavio and Freund-Levi, Yvonne and Minthon, Lennart and Froelich, Lutz and Hampel, Harald and Visser, Pieter Jelle and Soininen, Hilkka},
  issn         = {1875-5828},
  keyword      = {Alzheimer's disease,cerebrospinal fluid (CSF),computer-assisted,diagnosis,dementia,DESCRIPA,magnetic resonance imaging (MRI),mild,cognitive impairment (MCI)},
  language     = {eng},
  number       = {1},
  pages        = {69--79},
  publisher    = {Bentham Science Publishers},
  series       = {Current Alzheimer Research},
  title        = {Predicting Progression from Cognitive Impairment to Alzheimer's Disease with the Disease State Index},
  url          = {http://dx.doi.org/10.2174/1567205012666141218123829},
  volume       = {12},
  year         = {2015},
}