Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 69-79 |
| Journal | Current Alzheimer Research |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2015 |
| MoE publication type | A1 Journal article-refereed |
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Keywords
- Alzheimer's disease
- cerebrospinal fluid (CSF)
- computer-assisted diagnosis
- dementia
- DESCRIPA
- magnetic resonance imaging (MRI)
- mild cognitive impairment (MCI)
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Prediction progression from cognitive impairment to alzheimer's disease with the disease state index. / Hall, Anette; Mattila, Jussi; Koikkalainen, Juha; Lötjonen, Jyrki; Wolz, Robin; Scheltens, Philip; Frisoni, Giovanni; Tsolaki, Magdalini; Nobili, Flavio; Freund-Levi, Yvonne; Minthon, Lennart; Frölich, Lutz; Hampel, Harald; Visser, Pieter Jelle; Soininen, Hilkka.
In: Current Alzheimer Research, Vol. 12, No. 1, 2015, p. 69-79.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Prediction progression from cognitive impairment to alzheimer's disease with the disease state index
AU - Hall, Anette
AU - Mattila, Jussi
AU - Koikkalainen, Juha
AU - Lötjonen, Jyrki
AU - Wolz, Robin
AU - Scheltens, Philip
AU - Frisoni, Giovanni
AU - Tsolaki, Magdalini
AU - Nobili, Flavio
AU - Freund-Levi, Yvonne
AU - Minthon, Lennart
AU - Frölich, Lutz
AU - Hampel, Harald
AU - Visser, Pieter Jelle
AU - Soininen, Hilkka
N1 - LIS: Romeo yellow - but article is open Project code: 100765
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - cerebrospinal fluid (CSF)
KW - computer-assisted diagnosis
KW - dementia
KW - DESCRIPA
KW - magnetic resonance imaging (MRI)
KW - mild cognitive impairment (MCI)
U2 - 10.2174/1567205012666141218123829
DO - 10.2174/1567205012666141218123829
M3 - Article
VL - 12
SP - 69
EP - 79
JO - Current Alzheimer Research
JF - Current Alzheimer Research
SN - 1567-2050
IS - 1
ER -