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.
Keywords: Alzheimer’s disease, cerebrospinal fluid (CSF), computer-assisted diagnosis, dementia, DESCRIPA, magnetic resonance imaging (MRI), mild cognitive impairment (MCI).
Current Alzheimer Research
Title:Predicting Progression from Cognitive Impairment to Alzheimer’s Disease with the Disease State Index
Volume: 12 Issue: 1
Author(s): Anette Hall, Jussi Mattila, Juha Koikkalainen, Jyrki Lotjonen, Robin Wolz, Philip Scheltens, Giovanni Frisoni, Magdalini Tsolaki, Flavio Nobili, Yvonne Freund-Levi, Lennart Minthon, Lutz Frolich, Harald Hampel, Pieter Jelle Visser and Hilkka Soininen
Affiliation:
Keywords: Alzheimer’s disease, cerebrospinal fluid (CSF), computer-assisted diagnosis, dementia, DESCRIPA, magnetic resonance imaging (MRI), mild cognitive impairment (MCI).
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.
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Hall Anette, Mattila Jussi, Koikkalainen Juha, Lotjonen Jyrki, Wolz Robin, Scheltens Philip, Frisoni Giovanni, Tsolaki Magdalini, Nobili Flavio, Freund-Levi Yvonne, Minthon Lennart, Frolich Lutz, Hampel Harald, Jelle Visser Pieter and Soininen Hilkka, Predicting Progression from Cognitive Impairment to Alzheimer’s Disease with the Disease State Index, Current Alzheimer Research 2015; 12 (1) . https://dx.doi.org/10.2174/1567205012666141218123829
DOI https://dx.doi.org/10.2174/1567205012666141218123829 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
Call for Papers in Thematic Issues
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Aims and Scope: Introduction: Alzheimer's disease (AD) poses a significant global health challenge, with an increasing prevalence that demands concerted efforts to advance our understanding and strategies for prevention, diagnosis, treatment, and rehabilitation. This thematic issue aims to bring together cutting-edge research and innovative approaches from multidisciplinary perspectives to address ...read more
Current updates on the Role of Neuroinflammation in Neurodegenerative Disorders
Neuroinflammation is an invariable hallmark of chronic and acute neurodegenerative disorders and has long been considered a potential drug target for Alzheimer?s disease (AD) and dementia. Significant evidence of inflammatory processes as a feature of AD is provided by the presence of inflammatory markers in plasma, CSF and postmortem brain ...read more
Deep Learning for Advancing Alzheimer's Disease Research
Alzheimer's disease (AD) poses a significant global health challenge, with an increasing number of individuals affected yearly. Deep learning, a subfield of artificial intelligence, has shown immense potential in various domains, including healthcare. This thematic issue of Current Alzheimer Research explores the application of deep learning techniques in advancing our ...read more
Diagnostic and therapeutic biomarkers of dementia
Dementia affects 18 million people worldwide. Dementia is a syndrome of symptoms caused by brain disease, usually chronic or progressive, clinically characterized by multiple impairments of higher cortical functions such as memory, thinking, orientation, and learning. In addition, in the course of dementia, cognitive deficits are observed, which often hinder ...read more
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