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 -