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Sökning: onr:"swepub:oai:lup.lub.lu.se:14542551-dfb2-497f-836a-28e7215857d7" > Predicting Progress...

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

Hall, Anette (författare)
Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
Mattila, Jussi (författare)
VTT Technical Research Centre of Finland, Tampere, Finland
Koikkalainen, Juha (författare)
VTT Technical Research Centre of Finland, Tampere, Finland
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Loejonen, Jyrki (författare)
VTT Technical Research Centre of Finland, Tampere, Finland
Wolz, Robin (författare)
Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
Scheltens, Philip (författare)
Department of Neurology, Alzheimer Centre, VU University Medical Center, Amsterdam, Netherlands
Frisoni, Giovanni (författare)
IRCCS San Giovanni, Laboratory of Epidemiology and Neuroimaging, Brescia, Italy
Tsolaki, Magdalini (författare)
Aristotle University of Thessaloniki, Memory and Dementia Centre, 3rd Department of Neurology, "G Papanicolaou" General Hospital, Thessaloniki, Greece
Nobili, Flavio (författare)
Clinical Neurology, Department of Neuroscience, Ophthalmology and Genetics, University of Genoa, Genoa, Italy
Freund-Levi, Yvonne, 1956- (författare)
Institution of NVS, Department of Geriatrics, Section of Clinical Geriatrics, Karolinska Institutet, Department of Geriatrics, Karolinska University Hospital, Huddinge, Stockholm, Sweden
Minthon, Lennart (författare)
Lund University,Lunds universitet,Klinisk minnesforskning,Forskargrupper vid Lunds universitet,Clinical Memory Research,Lund University Research Groups,Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Malmö, Sweden
Froelich, Lutz (författare)
Department of Geriatric Psychiatry, Central Institute of Mental Health, Mannheim, University of Heidelberg, Mannheim, Germany
Hampel, Harald (författare)
Sorbonne Universités, Université Pierre et Marie Curie, Institut de la Mémoire et de la Maladie d’Alzheimer & Institut du Cerveau et de la Moelle épinière (ICM), Département de Neurologie, Hôpital de la Pitié-Salpétrière, Paris, France
Visser, Pieter Jelle (författare)
Department of Neurology, Alzheimer Centre, VU University Medical Center, Amsterdam, Netherlands; Department of Psychiatry and Neuropsychology, University of Maastricht, Maastricht, Netherlands
Soininen, Hilkka (författare)
Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland; Department of Neurology, Kuopio University Hospital, Kuopio, Finland
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 (creator_code:org_t)
Bentham Science Publishers Ltd. 2015
2015
Engelska.
Ingår i: Current Alzheimer Research. - : Bentham Science Publishers Ltd.. - 1875-5828 .- 1567-2050. ; 12:1, s. 69-79
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Neurologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Neurology (hsv//eng)

Nyckelord

Alzheimer's disease
cerebrospinal fluid (CSF)
computer-assisted
diagnosis
dementia
DESCRIPA
magnetic resonance imaging (MRI)
mild
cognitive impairment (MCI)
Alzheimer's disease

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art (ämneskategori)
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