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The combination of MMSE with VSRAD and eZIS has greater accuracy for discriminating mild cognitive impairment from early Alzheimer’s disease than MMSE alone
BACKGROUND: Alzheimer’s disease (AD) is assessed by carefully examining a patient’s cognitive impairment. However, previous studies reported inadequate diagnostic accuracy for dementia in primary care settings. Many hospitals use the automated quantitative evaluation method known as the Voxel-based...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7899318/ https://www.ncbi.nlm.nih.gov/pubmed/33617587 http://dx.doi.org/10.1371/journal.pone.0247427 |
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author | Tokumitsu, Keita Yasui-Furukori, Norio Takeuchi, Junko Yachimori, Koji Sugawara, Norio Terayama, Yoshio Tanaka, Nobuyuki Naraoka, Tatsunori Shimoda, Kazutaka |
author_facet | Tokumitsu, Keita Yasui-Furukori, Norio Takeuchi, Junko Yachimori, Koji Sugawara, Norio Terayama, Yoshio Tanaka, Nobuyuki Naraoka, Tatsunori Shimoda, Kazutaka |
author_sort | Tokumitsu, Keita |
collection | PubMed |
description | BACKGROUND: Alzheimer’s disease (AD) is assessed by carefully examining a patient’s cognitive impairment. However, previous studies reported inadequate diagnostic accuracy for dementia in primary care settings. Many hospitals use the automated quantitative evaluation method known as the Voxel-based Specific Regional Analysis System for Alzheimer’s Disease (VSRAD), wherein brain MRI data are used to evaluate brain morphological abnormalities associated with AD. Similarly, an automated quantitative evaluation application called the easy Z-score imaging system (eZIS), which uses brain SPECT data to detect regional cerebral blood flow decreases associated with AD, is widely used. These applications have several indicators, each of which is known to correlate with the degree of AD. However, it is not completely known whether these indicators work better when used in combination in real-world clinical practice. METHODS: We included 112 participants with mild cognitive impairment (MCI) and 128 participants with early AD in this study. All participants underwent MRI, SPECT, and the Mini-Mental State Examination (MMSE). Demographic and clinical characteristics were assessed by univariate analysis, and logistic regression analysis with a combination of MMSE, VSRAD and eZIS indicators was performed to verify whether the diagnostic accuracy in discriminating between MCI and early AD was improved. RESULTS: The area under the receiver operating characteristic curve (AUC) for the MMSE score alone was 0.835. The AUC was significantly improved to 0.870 by combining the MMSE score with two quantitative indicators from the VSRAD and eZIS that assessed the extent of brain abnormalities. CONCLUSION: Compared with the MMSE score alone, the combination of the MMSE score with the VSRAD and eZIS indicators significantly improves the accuracy of discrimination between patients with MCI and early AD. Implementing VSRAD and eZIS does not require professional clinical experience in the treatment of dementia. Therefore, the accuracy of dementia diagnosis by physicians may easily be improved in real-world primary care settings. |
format | Online Article Text |
id | pubmed-7899318 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78993182021-03-02 The combination of MMSE with VSRAD and eZIS has greater accuracy for discriminating mild cognitive impairment from early Alzheimer’s disease than MMSE alone Tokumitsu, Keita Yasui-Furukori, Norio Takeuchi, Junko Yachimori, Koji Sugawara, Norio Terayama, Yoshio Tanaka, Nobuyuki Naraoka, Tatsunori Shimoda, Kazutaka PLoS One Research Article BACKGROUND: Alzheimer’s disease (AD) is assessed by carefully examining a patient’s cognitive impairment. However, previous studies reported inadequate diagnostic accuracy for dementia in primary care settings. Many hospitals use the automated quantitative evaluation method known as the Voxel-based Specific Regional Analysis System for Alzheimer’s Disease (VSRAD), wherein brain MRI data are used to evaluate brain morphological abnormalities associated with AD. Similarly, an automated quantitative evaluation application called the easy Z-score imaging system (eZIS), which uses brain SPECT data to detect regional cerebral blood flow decreases associated with AD, is widely used. These applications have several indicators, each of which is known to correlate with the degree of AD. However, it is not completely known whether these indicators work better when used in combination in real-world clinical practice. METHODS: We included 112 participants with mild cognitive impairment (MCI) and 128 participants with early AD in this study. All participants underwent MRI, SPECT, and the Mini-Mental State Examination (MMSE). Demographic and clinical characteristics were assessed by univariate analysis, and logistic regression analysis with a combination of MMSE, VSRAD and eZIS indicators was performed to verify whether the diagnostic accuracy in discriminating between MCI and early AD was improved. RESULTS: The area under the receiver operating characteristic curve (AUC) for the MMSE score alone was 0.835. The AUC was significantly improved to 0.870 by combining the MMSE score with two quantitative indicators from the VSRAD and eZIS that assessed the extent of brain abnormalities. CONCLUSION: Compared with the MMSE score alone, the combination of the MMSE score with the VSRAD and eZIS indicators significantly improves the accuracy of discrimination between patients with MCI and early AD. Implementing VSRAD and eZIS does not require professional clinical experience in the treatment of dementia. Therefore, the accuracy of dementia diagnosis by physicians may easily be improved in real-world primary care settings. Public Library of Science 2021-02-22 /pmc/articles/PMC7899318/ /pubmed/33617587 http://dx.doi.org/10.1371/journal.pone.0247427 Text en © 2021 Tokumitsu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Tokumitsu, Keita Yasui-Furukori, Norio Takeuchi, Junko Yachimori, Koji Sugawara, Norio Terayama, Yoshio Tanaka, Nobuyuki Naraoka, Tatsunori Shimoda, Kazutaka The combination of MMSE with VSRAD and eZIS has greater accuracy for discriminating mild cognitive impairment from early Alzheimer’s disease than MMSE alone |
title | The combination of MMSE with VSRAD and eZIS has greater accuracy for discriminating mild cognitive impairment from early Alzheimer’s disease than MMSE alone |
title_full | The combination of MMSE with VSRAD and eZIS has greater accuracy for discriminating mild cognitive impairment from early Alzheimer’s disease than MMSE alone |
title_fullStr | The combination of MMSE with VSRAD and eZIS has greater accuracy for discriminating mild cognitive impairment from early Alzheimer’s disease than MMSE alone |
title_full_unstemmed | The combination of MMSE with VSRAD and eZIS has greater accuracy for discriminating mild cognitive impairment from early Alzheimer’s disease than MMSE alone |
title_short | The combination of MMSE with VSRAD and eZIS has greater accuracy for discriminating mild cognitive impairment from early Alzheimer’s disease than MMSE alone |
title_sort | combination of mmse with vsrad and ezis has greater accuracy for discriminating mild cognitive impairment from early alzheimer’s disease than mmse alone |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7899318/ https://www.ncbi.nlm.nih.gov/pubmed/33617587 http://dx.doi.org/10.1371/journal.pone.0247427 |
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