Cited by 576Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsNeurological Disease Mechanisms and Treatments

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The Natural History of Alzheimer's Disease: Description of Study Cohort and Accuracy of Diagnosis

Becker, James T.·1994·Archives of Neurology

DOI 10.1001/archneur.1994.00540180063015

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Abstract

OBJECTIVE: We describe the sampling, initial evaluation, and final diagnostic classification of subjects enrolled in a natural history study of Alzheimer's disease (AD). DESIGN: Volunteer cohort study. SETTING: Multidisciplinary behavioral neurology research clinic. PATIENTS OR OTHER PARTICIPANTS: Three-hundred nineteen individuals were enrolled in the Alzheimer Research Program between March 1983 and March 1988. Of these, 204 were originally classified with AD, 102 were normal elderly control subjects, and 13 were considered special cases. MAIN OUTCOME MEASURES: Final consensus clinical diagnosis, final neuropathologic diagnosis, and death. RESULTS: Of the 204 patients enrolled in the study, re-review after as many as 5 years of follow-up resulted in a final clinical classification of 188 with probable AD. Seven patients were believed to have a significant vascular component to the dementia, three were found to have developed depression, and six were excluded on other clinical grounds. Neuropathologic examination of 50 brains indicated definite AD in 43. After removing these seven misdiagnosed patients, the final group of probable/definite AD totaled 181 individuals. Accuracy of the baseline clinical diagnosis relative to neuropathology was 86%, and when follow-up clinical data were considered, 91.4%. Detailed neuropsychological testing yielded high sensitivity (0.988) and specificity (0.983) to dementia. Analyses of survival time from study entry until death revealed that older patients were significantly more likely to die during follow-up, but neither sex, years of education, nor pattern of cognitive impairment were related to survival. CONCLUSIONS: These data provide the descriptive basis for future studies of this cohort. They indicate that longitudinal follow-up of demented cases increases accuracy of diagnosis, and that detailed cognitive testing aids in early classification.

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Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282
Clinical diagnosis of Alzheimer's disease: Report of the NINCDS‐ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
VALIDITY OF THE TRAIL MAKING TEST AS AN INDICATOR OF ORGANIC BRAIN DAMAGE
Reitan, R. M. (1958) · Perceptual and Motor Skills · doi:10.2466/pms.1958.8.3.271 · Cited by 8,167
The Association Between Quantitative Measures of Dementia and of Senile Change in the Cerebral Grey Matter of Elderly Subjects
Blessed, G., Tomlinson, B. E., & Roth, M. (1968) · British Journal of Psychiatry · doi:10.1192/bjp.114.512.797 · Cited by 4,117
The assessment of aphasia and related disorders
Goodglass, H., & Kaplan, E. (1972) · Medical Entomology and Zoology · Cited by 3,258
. Cerebral blood flow in dementia.
Hachinski, V. C., Iliff, L. D., Zilhka, E., Du Boulay, G. H., McAllister, V. L., Marshall, J., Russell, R. W. R., & Symon, L. (1975) · Archives of Neurology · doi:10.1001/archneur.1975.00490510088009 · Cited by 3,186
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Wechsler, D. (1945) · The Journal of Psychology · doi:10.1080/00223980.1945.9917223 · Cited by 2,949
BMPD statistical software manual
Dixon, W. (1988) · University of California Press eBooks · Cited by 2,536
Diagnosis of Alzheimer's Disease
Khachaturian, Z. S. (1985) · Archives of Neurology · doi:10.1001/archneur.1985.04060100083029 · Cited by 2,499
Le test de copie d'une figure complexe
Osterrieth, P. M. (1944) · Medical Entomology and Zoology · Cited by 2,336

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Untitled work
(1998) · doi:10.1002/(sici)1099-1166(1998110)13:11

Authors: Becker, J. T.
Affiliations: University of Pittsburgh Medical Center (US)
Corresponding author: Becker, James T.
ORCID: Becker, James T.
Venue: Archives of Neurology
Publisher: American Medical Association (AMA)
Published: 1994-06-01
Volume: 51
Issue: 6
Pages: 585
ISSN: 0003-9942 (ISSN-L)
DOI: 10.1001/archneur.1994.00540180063015
OpenAlex: W2074037951
Cited by: 576
References: 49
Citations by year: 2026: 53 · 2025: 67 · 2024: 63 · 2023: 59 · 2022: 53 · 2021: 61 · 2020: 62 · 2019: 26 · 2018: 15 · 2017: 14 · 2016: 7 · 2015: 7 · 2014: 1 · 2013: 1 · 2012: 2
FWCI: 5.79
Type: journal article
Language: en
Open access: closed
Locations: Archives of Neurology (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health (5p01ag003705-04)
National Institute on Aging (AG 05-133, P50AG005133, AG 03705)
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
Life Sciences › Neuroscience › Neurology › Neurological Disease Mechanisms and Treatments
Keywords: Alzheimer's disease, natural history (disease), diagnosis, neuropathologic diagnosis, diagnostic accuracy, dementia, neuropsychological assessment, survival, death, vascular dementia, depression, cohort studies
MeSH terms: Aged, Alzheimer Disease, Mental Disorders, Female, Humans, Male, Middle Aged, Nervous System, Nervous System Diseases, Neuropsychological Tests, Cohort Studies

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The Natural History of Alzheimer's Disease: Description of Study Cohort and Accuracy of Diagnosis

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