In:
Alzheimer's & Dementia, Wiley, Vol. 9, No. 3 ( 2013-05), p. 262-268
Abstract:
We aimed to develop a prediction model based on cerebrospinal fluid (CSF) biomarkers, that would yield a single estimate representing the probability that dementia in a memory clinic patient is due to Alzheimer's disease (AD). Methods All patients suspected of dementia in whom the CSF biomarkers had been analyzed were selected from a memory clinic database. Clinical diagnosis was AD (n = 272) or non‐AD (n = 289). The prediction model was developed with logistic regression analysis and included CSF amyloid β 42 , CSF phosphorylated tau 181 , and sex. Validation was performed on an independent data set from another memory clinic, containing 334 AD and 157 non‐AD patients. Results The prediction model estimated the probability that AD is present as follows: p(AD) = 1/(1 + e – [–0.3315 + score] ), where score is calculated from –1.9486 × ln(amyloid β 42 ) + 2.7915 × ln(phosphorylated tau 181 ) + 0.9178 × sex (male = 0, female = 1). When applied to the validation data set, the discriminative ability of the model was very good (area under the receiver operating characteristic curve: 0.85). The agreement between the probability of AD predicted by the model and the observed frequency of AD diagnoses was very good after taking into account the difference in AD prevalence between the two memory clinics. Conclusions We developed a prediction model that can accurately predict the probability of AD in a memory clinic population suspected of dementia based on CSF amyloid β 42 , CSF phosphorylated tau 181 , and sex.
Type of Medium:
Online Resource
ISSN:
1552-5260
,
1552-5279
DOI:
10.1016/j.jalz.2012.01.010
Language:
English
Publisher:
Wiley
Publication Date:
2013
detail.hit.zdb_id:
2201940-6
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