Application of the predictad software tool to predict progression in patients with mild cognitive impairment

Anja H. Simonsen (Corresponding Author), Jussi Mattila, Anne Mette Hejl, Kristian S. Frederiksen, Sanna Kaisa Herukka, Merja Hallikainen, Mark van Gils, Jyrki Lötjönen, Hilkka Soininen, Gunhild Waldemar

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)

Abstract

Background: The PredictAD tool integrates heterogeneous data such as imaging, cerebrospinal fluid biomarkers and results from neuropsychological tests for compact visualization in an interactive user interface. This study investigated whether the software tool could assist physicians in the early diagnosis of Alzheimer's disease. Methods: Baseline data from 140 patients with mild cognitive impairment were selected from the Alzheimer's Disease Neuroimaging Study. Three clinical raters classified patients into 6 categories of confidence in the prediction of early Alzheimer's disease, in 4 phases of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators. Results: A statistical significant trend (p < 0.05) towards better classification accuracy (from 62.6 to 70.0%) was found when using the PredictAD tool during the stepwise procedure. When the same data were presented on paper, classification accuracy of the raters dropped significantly from 70.0 to 63.2%. Conclusion: Best classification accuracy was achieved by the clinical raters when using the tool for decision support, suggesting that the tool can add value in diagnostic classification when large amounts of heterogeneous data are presented.

Original languageEnglish
Pages (from-to)344-350
JournalDementia and Geriatric Cognitive Disorders
Volume34
Issue number5-6
DOIs
Publication statusPublished - 2012
MoE publication typeA1 Journal article-refereed

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Software
Alzheimer Disease
Neuroimaging
Neuropsychological Tests
Cerebrospinal Fluid
Early Diagnosis
Biomarkers
Research Personnel
Cognitive Dysfunction
Physicians

Keywords

  • Alzheimer's disease
  • Mild cognitive impairment
  • PredictAD software tool

Cite this

Simonsen, A. H., Mattila, J., Hejl, A. M., Frederiksen, K. S., Herukka, S. K., Hallikainen, M., ... Waldemar, G. (2012). Application of the predictad software tool to predict progression in patients with mild cognitive impairment. Dementia and Geriatric Cognitive Disorders, 34(5-6), 344-350. https://doi.org/10.1159/000345554
Simonsen, Anja H. ; Mattila, Jussi ; Hejl, Anne Mette ; Frederiksen, Kristian S. ; Herukka, Sanna Kaisa ; Hallikainen, Merja ; van Gils, Mark ; Lötjönen, Jyrki ; Soininen, Hilkka ; Waldemar, Gunhild. / Application of the predictad software tool to predict progression in patients with mild cognitive impairment. In: Dementia and Geriatric Cognitive Disorders. 2012 ; Vol. 34, No. 5-6. pp. 344-350.
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abstract = "Background: The PredictAD tool integrates heterogeneous data such as imaging, cerebrospinal fluid biomarkers and results from neuropsychological tests for compact visualization in an interactive user interface. This study investigated whether the software tool could assist physicians in the early diagnosis of Alzheimer's disease. Methods: Baseline data from 140 patients with mild cognitive impairment were selected from the Alzheimer's Disease Neuroimaging Study. Three clinical raters classified patients into 6 categories of confidence in the prediction of early Alzheimer's disease, in 4 phases of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators. Results: A statistical significant trend (p < 0.05) towards better classification accuracy (from 62.6 to 70.0{\%}) was found when using the PredictAD tool during the stepwise procedure. When the same data were presented on paper, classification accuracy of the raters dropped significantly from 70.0 to 63.2{\%}. Conclusion: Best classification accuracy was achieved by the clinical raters when using the tool for decision support, suggesting that the tool can add value in diagnostic classification when large amounts of heterogeneous data are presented.",
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Simonsen, AH, Mattila, J, Hejl, AM, Frederiksen, KS, Herukka, SK, Hallikainen, M, van Gils, M, Lötjönen, J, Soininen, H & Waldemar, G 2012, 'Application of the predictad software tool to predict progression in patients with mild cognitive impairment', Dementia and Geriatric Cognitive Disorders, vol. 34, no. 5-6, pp. 344-350. https://doi.org/10.1159/000345554

Application of the predictad software tool to predict progression in patients with mild cognitive impairment. / Simonsen, Anja H. (Corresponding Author); Mattila, Jussi; Hejl, Anne Mette; Frederiksen, Kristian S.; Herukka, Sanna Kaisa; Hallikainen, Merja; van Gils, Mark; Lötjönen, Jyrki; Soininen, Hilkka; Waldemar, Gunhild.

In: Dementia and Geriatric Cognitive Disorders, Vol. 34, No. 5-6, 2012, p. 344-350.

Research output: Contribution to journalArticleScientificpeer-review

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AU - Simonsen, Anja H.

AU - Mattila, Jussi

AU - Hejl, Anne Mette

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AU - Herukka, Sanna Kaisa

AU - Hallikainen, Merja

AU - van Gils, Mark

AU - Lötjönen, Jyrki

AU - Soininen, Hilkka

AU - Waldemar, Gunhild

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N2 - Background: The PredictAD tool integrates heterogeneous data such as imaging, cerebrospinal fluid biomarkers and results from neuropsychological tests for compact visualization in an interactive user interface. This study investigated whether the software tool could assist physicians in the early diagnosis of Alzheimer's disease. Methods: Baseline data from 140 patients with mild cognitive impairment were selected from the Alzheimer's Disease Neuroimaging Study. Three clinical raters classified patients into 6 categories of confidence in the prediction of early Alzheimer's disease, in 4 phases of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators. Results: A statistical significant trend (p < 0.05) towards better classification accuracy (from 62.6 to 70.0%) was found when using the PredictAD tool during the stepwise procedure. When the same data were presented on paper, classification accuracy of the raters dropped significantly from 70.0 to 63.2%. Conclusion: Best classification accuracy was achieved by the clinical raters when using the tool for decision support, suggesting that the tool can add value in diagnostic classification when large amounts of heterogeneous data are presented.

AB - Background: The PredictAD tool integrates heterogeneous data such as imaging, cerebrospinal fluid biomarkers and results from neuropsychological tests for compact visualization in an interactive user interface. This study investigated whether the software tool could assist physicians in the early diagnosis of Alzheimer's disease. Methods: Baseline data from 140 patients with mild cognitive impairment were selected from the Alzheimer's Disease Neuroimaging Study. Three clinical raters classified patients into 6 categories of confidence in the prediction of early Alzheimer's disease, in 4 phases of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators. Results: A statistical significant trend (p < 0.05) towards better classification accuracy (from 62.6 to 70.0%) was found when using the PredictAD tool during the stepwise procedure. When the same data were presented on paper, classification accuracy of the raters dropped significantly from 70.0 to 63.2%. Conclusion: Best classification accuracy was achieved by the clinical raters when using the tool for decision support, suggesting that the tool can add value in diagnostic classification when large amounts of heterogeneous data are presented.

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