title: Predicting progression from cognitive impairment to Alzheimer's disease with the Disease State Index.
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 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.
authors.forename: Anette
authors.initials: A
authors.lastname: Hall
authors.forename: Jussi
authors.initials: J
authors.lastname: Mattila
authors.forename: Juha
authors.initials: J
authors.lastname: Koikkalainen
authors.forename: Jyrki
authors.initials: J
authors.lastname: Lötjonen
authors.forename: Robin
authors.initials: R
authors.lastname: Wolz
authors.forename: Philip
authors.initials: P
authors.lastname: Scheltens
authors.forename: Giovanni
authors.initials: G
authors.lastname: Frisoni
authors.forename: Magdalini
authors.initials: M
authors.lastname: Tsolaki
authors.forename: Flavio
authors.initials: F
authors.lastname: Nobili
authors.forename: Yvonne
authors.initials: Y
authors.lastname: Freund-Levi
authors.forename: Lennart
authors.initials: L
authors.lastname: Minthon
authors.forename: Lutz
authors.initials: L
authors.lastname: Frölich
authors.forename: Harald
authors.initials: H
authors.lastname: Hampel
authors.forename: Pieter Jelle
authors.initials: PJ
authors.lastname: Visser
authors.affiliation: University of Eastern Finland, Institute of Clinical Medicine/Neurology, P.O.Box 1627, 70211 Kuopio, Finland. anette.hall@uef.fi.
authors.forename: Hilkka
authors.initials: H
authors.lastname: Soininen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000503:physiopathology*; D001057:Apolipoproteins E / Q000235:genetics; D019540:Area Under Curve; D015415:Biomarkers / Q000134:cerebrospinal fluid; D060825:Cognitive Dysfunction / Q000134:cerebrospinal fluid / Q000235:genetics / Q000503:physiopathology*; D018450:Disease Progression; D005060:Europe; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009483:Neuropsychological Tests; D011237:Predictive Value of Tests; D011569:Psychiatric Status Rating Scales; D012680:Sensitivity and Specificity
chemical_list: D001057:Apolipoproteins E; D015415:Biomarkers