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Predicting Progress...
Predicting Progression from Cognitive Impairment to Alzheimer's Disease with the Disease State Index
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- Hall, Anette (author)
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
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- Mattila, Jussi (author)
- VTT Technical Research Centre of Finland, Tampere, Finland
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- Koikkalainen, Juha (author)
- VTT Technical Research Centre of Finland, Tampere, Finland
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- Loejonen, Jyrki (author)
- VTT Technical Research Centre of Finland, Tampere, Finland
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- Wolz, Robin (author)
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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- Scheltens, Philip (author)
- Department of Neurology, Alzheimer Centre, VU University Medical Center, Amsterdam, Netherlands
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- Frisoni, Giovanni (author)
- IRCCS San Giovanni, Laboratory of Epidemiology and Neuroimaging, Brescia, Italy
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- Tsolaki, Magdalini (author)
- Aristotle University of Thessaloniki, Memory and Dementia Centre, 3rd Department of Neurology, "G Papanicolaou" General Hospital, Thessaloniki, Greece
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- Nobili, Flavio (author)
- Clinical Neurology, Department of Neuroscience, Ophthalmology and Genetics, University of Genoa, Genoa, Italy
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- Freund-Levi, Yvonne, 1956- (author)
- Institution of NVS, Department of Geriatrics, Section of Clinical Geriatrics, Karolinska Institutet, Department of Geriatrics, Karolinska University Hospital, Huddinge, Stockholm, Sweden
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- Minthon, Lennart (author)
- 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
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- Froelich, Lutz (author)
- Department of Geriatric Psychiatry, Central Institute of Mental Health, Mannheim, University of Heidelberg, Mannheim, Germany
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- Hampel, Harald (author)
- 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
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- Visser, Pieter Jelle (author)
- Department of Neurology, Alzheimer Centre, VU University Medical Center, Amsterdam, Netherlands; Department of Psychiatry and Neuropsychology, University of Maastricht, Maastricht, Netherlands
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- Soininen, Hilkka (author)
- 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
- English.
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In: Current Alzheimer Research. - : Bentham Science Publishers Ltd.. - 1875-5828 .- 1567-2050. ; 12:1, s. 69-79
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Abstract
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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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Neurologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Neurology (hsv//eng)
Keyword
- Alzheimer's disease
- cerebrospinal fluid (CSF)
- computer-assisted
- diagnosis
- dementia
- DESCRIPA
- magnetic resonance imaging (MRI)
- mild
- cognitive impairment (MCI)
- Alzheimer's disease
Publication and Content Type
- art (subject category)
- ref (subject category)
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- By the author/editor
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Hall, Anette
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Mattila, Jussi
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Koikkalainen, Ju ...
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Loejonen, Jyrki
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Wolz, Robin
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Scheltens, Phili ...
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show more...
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Frisoni, Giovann ...
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Tsolaki, Magdali ...
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Nobili, Flavio
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Freund-Levi, Yvo ...
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Minthon, Lennart
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Froelich, Lutz
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Hampel, Harald
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Visser, Pieter J ...
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Soininen, Hilkka
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- About the subject
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Neurology
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- By the university
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Lund University
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Örebro University
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Karolinska Institutet