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Articles by Marcel M. Verbeek
Total Records ( 2 ) for Marcel M. Verbeek
  Niklas Mattsson , Ulf Andreasson , Staffan Persson , Maria C. Carrillo , Steven Collins , Sonia Chalbot , Neal Cutler , Diane Dufour- Rainfray , Anne M. Fagan , Niels H.H. Heegaard , Ging-Yuek Robin Hsiung , Bradley Hyman , Khalid Iqbal , D. Richard Lachno , Alberto Lleo , Piotr Lewczuk , Jose L. Molinuevo , Piero Parchi , Axel Regeniter , Robert Rissman , Hanna Rosenmann , Giuseppe Sancesario , Johannes Schroder , Leslie M. Shaw , Charlotte E. Teunissen , John Q. Trojanowski , Hugo Vanderstichele , Manu Vandijck , Marcel M. Verbeek , Henrik Zetterberg , Kaj Blennow and Stephan A. Kaser
  Background The cerebrospinal fluid (CSF) biomarkers amyloid beta 1–42, total tau, and phosphorylated tau are used increasingly for Alzheimer's disease (AD) research and patient management. However, there are large variations in biomarker measurements among and within laboratories. Methods Data from the first nine rounds of the Alzheimer's Association quality control program was used to define the extent and sources of analytical variability. In each round, three CSF samples prepared at the Clinical Neurochemistry Laboratory (Molndal, Sweden) were analyzed by single-analyte enzyme-linked immunosorbent assay (ELISA), a multiplexing xMAP assay, or an immunoassay with electrochemoluminescence detection. Results A total of 84 laboratories participated. Coefficients of variation (CVs) between laboratories were around 20% to 30%; within-run CVs, less than 5% to 10%; and longitudinal within-laboratory CVs, 5% to 19%. Interestingly, longitudinal within-laboratory CV differed between biomarkers at individual laboratories, suggesting that a component of it was assay dependent. Variability between kit lots and between laboratories both had a major influence on amyloid beta 1–42 measurements, but for total tau and phosphorylated tau, between-kit lot effects were much less than between-laboratory effects. Despite the measurement variability, the between-laboratory consistency in classification of samples (using prehoc-derived cutoffs for AD) was high (>90% in 15 of 18 samples for ELISA and in 12 of 18 samples for xMAP). Conclusions The overall variability remains too high to allow assignment of universal biomarker cutoff values for a specific intended use. Each laboratory must ensure longitudinal stability in its measurements and use internally qualified cutoff levels. Further standardization of laboratory procedures and improvement of kit performance will likely increase the usefulness of CSF AD biomarkers for researchers and clinicians.
  Petra E. Spies , Jurgen A.H.R. Claassen , Petronella G.M. Peer , Marinus A. Blankenstein , Charlotte E. Teunissen , Philip Scheltens , Wiesje M. van der Flier , Marcel G.M. Olde Rikkert and Marcel M. Verbeek
  Background 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 β42, CSF phosphorylated tau181, 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 β42) + 2.7915 × ln(phosphorylated tau181) + 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 β42, CSF phosphorylated tau181, and sex.
 
 
 
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