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000141557 0247_ $$2doi$$a10.1016/S1474-4422(19)30283-2
000141557 0247_ $$2pmid$$apmid:31526625
000141557 0247_ $$2ISSN$$a1474-4422
000141557 0247_ $$2ISSN$$a1474-4465
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000141557 037__ $$aDZNE-2020-07881
000141557 041__ $$aEnglish
000141557 082__ $$a610
000141557 1001_ $$0P:(DE-HGF)0$$avan Maurik, Ingrid S$$b0$$eCorresponding author
000141557 245__ $$aBiomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study.
000141557 260__ $$aLondon$$bLancet Publ. Group$$c2019
000141557 264_1 $$2Crossref$$3print$$bElsevier BV$$c2019-11-01
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000141557 520__ $$aBiomarker-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%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59-0·65), validated hippocampal volume model (0·67, 0·62-0·72), and updated CSF biomarkers model (0·72, 0·68-0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71-0·76).We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour.ZonMW-Memorabel.
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000141557 536__ $$0G:(DE-HGF)POF3-344$$a344 - Clinical and Health Care Research (POF3-344)$$cPOF3-344$$fPOF III$$x1
000141557 542__ $$2Crossref$$i2019-11-01$$uhttps://www.elsevier.com/tdm/userlicense/1.0/
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000141557 650_2 $$2MeSH$$aAged
000141557 650_2 $$2MeSH$$aAged, 80 and over
000141557 650_2 $$2MeSH$$aAlzheimer Disease: cerebrospinal fluid
000141557 650_2 $$2MeSH$$aAlzheimer Disease: epidemiology
000141557 650_2 $$2MeSH$$aAlzheimer Disease: pathology
000141557 650_2 $$2MeSH$$aAmyloid beta-Peptides: cerebrospinal fluid
000141557 650_2 $$2MeSH$$aBiomarkers: cerebrospinal fluid
000141557 650_2 $$2MeSH$$aCognitive Dysfunction: cerebrospinal fluid
000141557 650_2 $$2MeSH$$aCognitive Dysfunction: pathology
000141557 650_2 $$2MeSH$$aDisease Progression
000141557 650_2 $$2MeSH$$aEurope: epidemiology
000141557 650_2 $$2MeSH$$aFemale
000141557 650_2 $$2MeSH$$aFollow-Up Studies
000141557 650_2 $$2MeSH$$aHippocampus: pathology
000141557 650_2 $$2MeSH$$aHumans
000141557 650_2 $$2MeSH$$aKaplan-Meier Estimate
000141557 650_2 $$2MeSH$$aMagnetic Resonance Imaging
000141557 650_2 $$2MeSH$$aMale
000141557 650_2 $$2MeSH$$aMiddle Aged
000141557 650_2 $$2MeSH$$aMulticenter Studies as Topic: statistics & numerical data
000141557 650_2 $$2MeSH$$aNerve Degeneration
000141557 650_2 $$2MeSH$$aNeuroimaging
000141557 650_2 $$2MeSH$$aNorth America: epidemiology
000141557 650_2 $$2MeSH$$aOrgan Size
000141557 650_2 $$2MeSH$$aPeptide Fragments: cerebrospinal fluid
000141557 650_2 $$2MeSH$$aPhosphorylation
000141557 650_2 $$2MeSH$$aPrognosis
000141557 650_2 $$2MeSH$$aProportional Hazards Models
000141557 650_2 $$2MeSH$$aProtein Processing, Post-Translational
000141557 650_2 $$2MeSH$$atau Proteins: cerebrospinal fluid
000141557 650_2 $$2MeSH$$atau Proteins: chemistry
000141557 7001_ $$aVos, Stephanie J$$b1
000141557 7001_ $$aBos, Isabelle$$b2
000141557 7001_ $$aBouwman, Femke H$$b3
000141557 7001_ $$aTeunissen, Charlotte E$$b4
000141557 7001_ $$aScheltens, Philip$$b5
000141557 7001_ $$aBarkhof, Frederik$$b6
000141557 7001_ $$aFrolich, Lutz$$b7
000141557 7001_ $$aKornhuber, Johannes$$b8
000141557 7001_ $$0P:(DE-2719)2811317$$aWiltfang, Jens$$b9$$udzne
000141557 7001_ $$0P:(DE-2719)2000015$$aMaier, Wolfgang$$b10$$udzne
000141557 7001_ $$0P:(DE-2719)2811024$$aPeters, Oliver$$b11$$udzne
000141557 7001_ $$0P:(DE-HGF)0$$aRüther, Eckart$$b12
000141557 7001_ $$aNobili, Flavio$$b13
000141557 7001_ $$aFrisoni, Giovanni B$$b14
000141557 7001_ $$aSpiru, Luiza$$b15
000141557 7001_ $$aFreund-Levi, Yvonne$$b16
000141557 7001_ $$aWallin, Asa K$$b17
000141557 7001_ $$aHampel, Harald$$b18
000141557 7001_ $$aSoininen, Hilkka$$b19
000141557 7001_ $$aTsolaki, Magda$$b20
000141557 7001_ $$aVerhey, Frans$$b21
000141557 7001_ $$aKłoszewska, Iwona$$b22
000141557 7001_ $$aMecocci, Patrizia$$b23
000141557 7001_ $$aVellas, Bruno$$b24
000141557 7001_ $$aLovestone, Simon$$b25
000141557 7001_ $$aGalluzzi, Samantha$$b26
000141557 7001_ $$aHerukka, Sanna-Kaisa$$b27
000141557 7001_ $$aSantana, Isabel$$b28
000141557 7001_ $$aBaldeiras, Ines$$b29
000141557 7001_ $$ade Mendonça, Alexandre$$b30
000141557 7001_ $$aSilva, Dina$$b31
000141557 7001_ $$aChetelat, Gael$$b32
000141557 7001_ $$aEgret, Stephanie$$b33
000141557 7001_ $$aPalmqvist, Sebastian$$b34
000141557 7001_ $$aHansson, Oskar$$b35
000141557 7001_ $$aVisser, Pieter Jelle$$b36
000141557 7001_ $$aBerkhof, Johannes$$b37
000141557 7001_ $$avan der Flier, Wiesje M$$b38
000141557 7001_ $$aInitiative, Alzheimer's Disease Neuroimaging$$b39
000141557 77318 $$2Crossref$$3journal-article$$a10.1016/s1474-4422(19)30283-2$$b : Elsevier BV, 2019-11-01$$n11$$p1034-1044$$tThe Lancet Neurology$$v18$$x1474-4422$$y2019
000141557 773__ $$0PERI:(DE-600)2079704-7$$a10.1016/S1474-4422(19)30283-2$$gVol. 18, no. 11, p. 1034 - 1044$$n11$$p1034-1044$$q18:11<1034 - 1044$$tThe lancet <London> / Neurology$$v18$$x1474-4422$$y2019
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