Literature DB >> 8681001

Application of a growth curve approach to modeling the progression of Alzheimer's disease.

Y Stern1, X Liu, M Albert, J Brandt, D M Jacobs, C Del Castillo-Castaneda, K Marder, K Bell, M Sano, F Bylsma, G Lafleche, W Y Tsai.   

Abstract

BACKGROUND: Studies using clinical measures to track AD progression often assume linear declines over the entire course of the disease, which may not be justified. The objective of this study was to model change in measures of the clinical severity of Alzheimer's disease (AD) over time.
METHODS: We developed a method to apply growth curve models to prospective data and characterize AD patients' functional change over time. Data from the modified Mini-Mental State Examination (mMMSE) and measures of basic and instrumental ADL, administered semiannually for up to 5 years to 236 patients with probable AD, were modeled.
RESULTS: The rate of decline in mMMS scores per 6-month interval gradually increased as scores dropped from the maximum of 57 to 20. The rate of decline then decreased as scores approached 0, resulting in an inverse "S" curve. The rate of increase in instrumental ADL scores per interval attenuated as the scores increased, while that for basic ADL scores across intervals was constant.
CONCLUSIONS: Differences in the pattern of progression of the three measures is in part a function of their psychometric properties. The progression curves may also reflect content-specific features of the instruments. Superimposition of the modeled decline in these three content areas suggests a hypothetical model of the relative timing of cognitive and functional changes in AD.

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Year:  1996        PMID: 8681001     DOI: 10.1093/gerona/51a.4.m179

Source DB:  PubMed          Journal:  J Gerontol A Biol Sci Med Sci        ISSN: 1079-5006            Impact factor:   6.053


  13 in total

1.  Disease progression model in subjects with mild cognitive impairment from the Alzheimer's disease neuroimaging initiative: CSF biomarkers predict population subtypes.

Authors:  Mahesh N Samtani; Nandini Raghavan; Yingqi Shi; Gerald Novak; Michael Farnum; Victor Lobanov; Tim Schultz; Eric Yang; Allitia DiBernardo; Vaibhav A Narayan
Journal:  Br J Clin Pharmacol       Date:  2013-01       Impact factor: 4.335

2.  Estimation and validation of a multiattribute model of Alzheimer disease progression.

Authors:  Eric Stallard; Bruce Kinosian; Arthur S Zbrozek; Anatoliy I Yashin; Henry A Glick; Yaakov Stern
Journal:  Med Decis Making       Date:  2010 Nov-Dec       Impact factor: 2.583

3.  The dynamics of cortical and hippocampal atrophy in Alzheimer disease.

Authors:  Mert R Sabuncu; Rahul S Desikan; Jorge Sepulcre; Boon Thye T Yeo; Hesheng Liu; Nicholas J Schmansky; Martin Reuter; Michael W Weiner; Randy L Buckner; Reisa A Sperling; Bruce Fischl
Journal:  Arch Neurol       Date:  2011-08

4.  Gene Expressions, Hippocampal Volume Loss, and MMSE Scores in Computation of Progression and Pharmacologic Therapy Effects for Alzheimer's Disease.

Authors:  Aydin Saribudak; Adarsha A Subick; Na Hyun Kim; Joshua A Rutta; M Umit Uyar
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2018-09-14       Impact factor: 3.710

Review 5.  Modeling the time-course of Alzheimer dementia.

Authors:  J W Ashford; F A Schmitt
Journal:  Curr Psychiatry Rep       Date:  2001-02       Impact factor: 5.285

6.  Independent contributions of neural and "higher-order" deficits to symptoms in Alzheimer's disease: a latent variable modeling approach.

Authors:  Rochelle E Tractenberg; Paul S Aisen; Myron F Weiner; Jeffrey L Cummings; Gregory R Hancock
Journal:  Alzheimers Dement       Date:  2006-10       Impact factor: 21.566

7.  The effects of patient function and dependence on costs of care in Alzheimer's disease.

Authors:  Carolyn W Zhu; Christopher Leibman; Trent McLaughlin; Nikolaos Scarmeas; Marilyn Albert; Jason Brandt; Deborah Blacker; Mary Sano; Yaakov Stern
Journal:  J Am Geriatr Soc       Date:  2008-07-24       Impact factor: 5.562

Review 8.  Measuring decision-making capacity in cognitively impaired individuals.

Authors:  Jason Karlawish
Journal:  Neurosignals       Date:  2007-12-05

9.  A new algorithm for predicting time to disease endpoints in Alzheimer's disease patients.

Authors:  Qolamreza R Razlighi; Eric Stallard; Jason Brandt; Deborah Blacker; Marilyn Albert; Nikolaos Scarmeas; Bruce Kinosian; Anatoliy I Yashin; Yaakov Stern
Journal:  J Alzheimers Dis       Date:  2014       Impact factor: 4.472

10.  Measuring cerebral atrophy and white matter hyperintensity burden to predict the rate of cognitive decline in Alzheimer disease.

Authors:  Adam M Brickman; Lawrence S Honig; Nikolaos Scarmeas; Oksana Tatarina; Linda Sanders; Marilyn S Albert; Jason Brandt; Deborah Blacker; Yaakov Stern
Journal:  Arch Neurol       Date:  2008-09
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