Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment
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Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment. / 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.
In: Dementia and Geriatric Cognitive Disorders, Vol. 34, No. 5-6, 2012, p. 344-350.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment
AU - Simonsen, Anja H
AU - Mattila, Jussi
AU - Hejl, Anne-Mette
AU - Frederiksen, Kristian S
AU - Herukka, Sanna-Kaisa
AU - Hallikainen, Merja
AU - van Gils, Mark
AU - Lötjönen, Jyrki
AU - Soininen, Hilkka
AU - Waldemar, Gunhild
N1 - Copyright © 2012 S. Karger AG, Basel.
PY - 2012
Y1 - 2012
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.
U2 - 10.1159/000345554
DO - 10.1159/000345554
M3 - Journal article
C2 - 23222123
VL - 34
SP - 344
EP - 350
JO - Dementia and Geriatric Cognitive Disorders
JF - Dementia and Geriatric Cognitive Disorders
SN - 1420-8008
IS - 5-6
ER -
ID: 48606339