Generalizability of the Disease State Index Prediction Model for Identifying Patients Progressing from Mild Cognitive Impairment to Alzheimer's Disease

Article type: Research Article

Authors: Hall, Anette^{a; *; 1} | Muñoz-Ruiz, Miguel^{a; b; 1} | Mattila, Jussi^c | Koikkalainen, Juha^c | Tsolaki, Magda^d | Mecocci, Patrizia^e | Kloszewska, Iwona^f | Vellas, Bruno^g | Lovestone, Simon^{h; i} | Visser, Pieter Jelle^{j; k} | Lötjonen, Jyrki^c | Soininen, Hilkka^{a; b} | for the Alzheimer Disease Neuroimaging Initiative² | the AddNeuroMed consortium, DESCRIPA and Kuopio L-MCI

Affiliations: [a] Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland | [b] Department of Neurology, Kuopio University Hospital, Kuopio, Finland | [c] VTT Technical Research Centre of Finland, Tampere, Finland | [d] Aristotle University of Thessaloniki, Memory and Dementia Centre, “G Papanicolaou” General Hospital, Thessaloniki, Greece | [e] Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy | [f] Medical University of Lodz, Lodz, Poland | [g] UMR INSERM, University of Toulouse, France | [h] National Institute for Health Research (NIHR), London, UK | [i] King's College London, Institute of Psychiatry, London, UK | [j] VU University Medical Center, Amsterdam, The Netherlands | [k] Maastricht University, Maastricht, The Netherlands

Correspondence: [*] Correspondence to: Anette Hall, University of Eastern Finland, Institute of Clinical Medicine/Neurology, P.O. Box 1627, 70211 Kuopio, Finland. Tel.: +358 50 5392167; Fax: +358 17 162048; E-mail: [email protected].

Note: [1] These authors contributed equally to this work.

Note: [2] Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: Background:The Disease State Index (DSI) prediction model measures the similarity of patient data to diagnosed stable and progressive mild cognitive impairment (MCI) cases to identify patients who are progressing to Alzheimer's disease. Objectives:We evaluated how well the DSI generalizes across four different cohorts: DESCRIPA, ADNI, AddNeuroMed, and the Kuopio MCI study. Methods:The accuracy of the DSI in predicting progression was examined for each cohort separately using 10 × 10-fold cross-validation and for inter-cohort validation using each cohort as a test set for the model built from the other independent cohorts using bootstrapping with 10 repetitions. Altogether 875 subjects were included in the analysis. The analyzed data included a comprehensive set of age and gender corrected magnetic resonance imaging (MRI) features from hippocampal volumetry, multi-template tensor-based morphometry, and voxel-based morphometry as well as Mini-Mental State Examination (MMSE), APOE genotype, and additional cohort specific data from neuropsychological tests and cerebrospinal fluid measurements (CSF). Results:The DSI model was used to classify the patients into stable and progressive MCI cases. AddNeuroMed had the highest classification results of the cohorts, while ADNI and Kuopio MCI exhibited the lowest values. The MRI features alone achieved a good classification performance for all cohorts. For ADNI and DESCRIPA, adding MMSE, APOE genotype, CSF, and neuropsychological data improved the results. Conclusions:The results reveal that the prediction performance of the combined cohort is close to the average of the individual cohorts. It is feasible to use different cohorts as training sets for the DSI, if they are sufficiently similar.

Keywords: Alzheimer's disease, computer-assisted diagnosis, dementia, magnetic resonance imaging (MRI), mild cognitive impairment

DOI: 10.3233/JAD-140942

Journal: Journal of Alzheimer's Disease, vol. 44, no. 1, pp. 79-92, 2015