%0 Journal Article
%A van Maurik, Ingrid S
%A Vos, Stephanie J
%A Bos, Isabelle
%A Bouwman, Femke H
%A Teunissen, Charlotte E
%A Scheltens, Philip
%A Barkhof, Frederik
%A Frolich, Lutz
%A Kornhuber, Johannes
%A Wiltfang, Jens
%A Maier, Wolfgang
%A Peters, Oliver
%A Rüther, Eckart
%A Nobili, Flavio
%A Frisoni, Giovanni B
%A Spiru, Luiza
%A Freund-Levi, Yvonne
%A Wallin, Asa K
%A Hampel, Harald
%A Soininen, Hilkka
%A Tsolaki, Magda
%A Verhey, Frans
%A Kłoszewska, Iwona
%A Mecocci, Patrizia
%A Vellas, Bruno
%A Lovestone, Simon
%A Galluzzi, Samantha
%A Herukka, Sanna-Kaisa
%A Santana, Isabel
%A Baldeiras, Ines
%A de Mendonça, Alexandre
%A Silva, Dina
%A Chetelat, Gael
%A Egret, Stephanie
%A Palmqvist, Sebastian
%A Hansson, Oskar
%A Visser, Pieter Jelle
%A Berkhof, Johannes
%A van der Flier, Wiesje M
%A Initiative, Alzheimer's Disease Neuroimaging
%T Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study.
%J The lancet  / Neurology
%V 18
%N 11
%@ 1474-4422
%C London
%I Lancet Publ. Group
%M DZNE-2020-07881
%P 1034-1044
%D 2019
%X Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia.In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models-a demographics model, a hippocampal volume model, and a CSF biomarkers model-by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework.We included all 2611 individuals with MCI in the four cohorts, 1007 (39
%K Aged
%K Aged, 80 and over
%K Alzheimer Disease: cerebrospinal fluid
%K Alzheimer Disease: epidemiology
%K Alzheimer Disease: pathology
%K Amyloid beta-Peptides: cerebrospinal fluid
%K Biomarkers: cerebrospinal fluid
%K Cognitive Dysfunction: cerebrospinal fluid
%K Cognitive Dysfunction: pathology
%K Disease Progression
%K Europe: epidemiology
%K Female
%K Follow-Up Studies
%K Hippocampus: pathology
%K Humans
%K Kaplan-Meier Estimate
%K Magnetic Resonance Imaging
%K Male
%K Middle Aged
%K Multicenter Studies as Topic: statistics & numerical data
%K Nerve Degeneration
%K Neuroimaging
%K North America: epidemiology
%K Organ Size
%K Peptide Fragments: cerebrospinal fluid
%K Phosphorylation
%K Prognosis
%K Proportional Hazards Models
%K Protein Processing, Post-Translational
%K tau Proteins: cerebrospinal fluid
%K tau Proteins: chemistry
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:31526625
%R 10.1016/S1474-4422(19)30283-2
%U https://pub.dzne.de/record/141557