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Validation of the test for finding word retrieval deficits (WoFi) in detecting Alzheimer's disease in a naturalistic clinical setting

Author

Listed:
  • Eleni-Zacharoula Georgiou

    (University of Patras)

  • Maria Skondra

    (University of Patras)

  • Marina Charalampopoulou

    (University of Patras)

  • Panagiotis Felemegkas

    (University of Patras)

  • Asimina Pachi

    (University of Patras)

  • Georgia Stafylidou

    (University of Patras)

  • Dimitrios Papazachariou

    (University of Patras)

  • Robert Perneczky

    (Ludwig-Maximilians-Universität Munich
    The Imperial College of Science, Technology and Medicine
    German Center for Neurodegenerative Diseases (DZNE) Munich
    Munich Cluster for Systems Neurology (SyNergy))

  • Vasileios Thomopoulos

    (University of Patras)

  • Antonios Politis

    (National and Kapodistrian University of Athens
    Johns Hopkins Medical School)

  • Iracema Leroi

    (The University of Dublin)

  • Polychronis Economou

    (University of Patras)

  • Panagiotis Alexopoulos

    (University of Patras
    The University of Dublin
    Technical University of Munich
    Patras Dementia Day Care Centre)

Abstract

Background Detecting impaired naming capacity contributes to the detection of mild (MildND) and major (MajorND) neurocognitive disorder due to Alzheimer’s disease (AD). The Test for Finding Word retrieval deficits (WoFi) is a new, 50-item, auditory stimuli-based instrument. Objective The study aimed to adapt WoFi to the Greek language, to develop a short version of WoFi (WoFi-brief), to compare the item frequency and the utility of both instruments with the naming subtest of the widely used Addenbrooke’s cognitive examination III (ACEIIINaming) in detecting MildND and MajorND due to AD. Methods This cross-sectional, validation study included 99 individuals without neurocognitive disorder, as well as 114 and 49 patients with MildND and MajorND due to AD, respectively. The analyses included categorical principal components analysis using Cramer’s V, assessment of the frequency of test items based on corpora of television subtitles, comparison analyses, Kernel Fisher discriminant analysis models, proportional odds logistic regression (POLR) models and stratified repeated random subsampling used to recursive partitioning to training and validation set (70/30 ratio). Results WoFi and WoFi-brief, which consists of 16 items, have comparable item frequency and utility and outperform ACEIIINaming. According to the results of the discriminant analysis, the misclassification error was 30.9%, 33.6% and 42.4% for WoFi, WoFi-brief and ACEIIINaming, respectively. In the validation regression model including WoFi the mean misclassification error was 33%, while in those including WoFi-brief and ACEIIINaming it was 31% and 34%, respectively. Conclusions WoFi and WoFi-brief are more effective in detecting MildND and MajorND due to AD than ACEIIINaming.

Suggested Citation

  • Eleni-Zacharoula Georgiou & Maria Skondra & Marina Charalampopoulou & Panagiotis Felemegkas & Asimina Pachi & Georgia Stafylidou & Dimitrios Papazachariou & Robert Perneczky & Vasileios Thomopoulos & , 2023. "Validation of the test for finding word retrieval deficits (WoFi) in detecting Alzheimer's disease in a naturalistic clinical setting," European Journal of Ageing, Springer, vol. 20(1), pages 1-10, December.
    • Handle: RePEc:spr:eujoag:v:20:y:2023:i:1:d:10.1007_s10433-023-00772-z
    DOI: 10.1007/s10433-023-00772-z
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    References listed on IDEAS

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    1. Carlos Calderón & Christian Beyle & Oscar Véliz-García & Juan Bekios-Calfa, 2021. "Psychometric properties of Addenbrooke’s Cognitive Examination III (ACE-III): An item response theory approach," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-16, May.
    2. Joseph A. Hirsch & George M. Cuesta & Barry D. Jordan & Pasquale Fonzetti & Leann Levin, 2016. "The Auditory Naming Test Improves Diagnosis of Naming Deficits in Dementia," SAGE Open, , vol. 6(3), pages 21582440166, August.
    3. Kyriakos Skarlatos & Eleni S. Bekri & Dimitrios Georgakellos & Polychronis Economou & Sotirios Bersimis, 2023. "Projecting Annual Rainfall Timeseries Using Machine Learning Techniques," Energies, MDPI, vol. 16(3), pages 1-20, February.
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