TITLE

Predicting AD Conversion: Comparison between Prodromal AD Guidelines and Computer Assisted PredictAD Tool

AUTHOR(S)
Liu, Yawu; Mattila, Jussi; Ruiz, Miguel Ángel Muñoz; Paajanen, Teemu; Koikkalainen, Juha; van Gils, Mark; Herukka, Sanna-Kaisa; Waldemar, Gunhild; Lötjönen, Jyrki; Soininen, Hilkka
PUB. DATE
February 2013
SOURCE
PLoS ONE;Feb2013, Vol. 8 Issue 2, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Purpose: To compare the accuracies of predicting AD conversion by using a decision support system (PredictAD tool) and current research criteria of prodromal AD as identified by combinations of episodic memory impairment of hippocampal type and visual assessment of medial temporal lobe atrophy (MTA) on MRI and CSF biomarkers. Methods: Altogether 391 MCI cases (158 AD converters) were selected from the ADNI cohort. All the cases had baseline cognitive tests, MRI and/or CSF levels of Aβ1–42 and Tau. Using baseline data, the status of MCI patients (AD or MCI) three years later was predicted using current diagnostic research guidelines and the PredictAD software tool designed for supporting clinical diagnostics. The data used were 1) clinical criteria for episodic memory loss of the hippocampal type, 2) visual MTA, 3) positive CSF markers, 4) their combinations, and 5) when the PredictAD tool was applied, automatically computed MRI measures were used instead of the visual MTA results. The accuracies of diagnosis were evaluated with the diagnosis made 3 years later. Results: The PredictAD tool achieved the overall accuracy of 72% (sensitivity 73%, specificity 71%) in predicting the AD diagnosis. The corresponding number for a clinician’s prediction with the assistance of the PredictAD tool was 71% (sensitivity 75%, specificity 68%). Diagnosis with the PredictAD tool was significantly better than diagnosis by biomarkers alone or the combinations of clinical diagnosis of hippocampal pattern for the memory loss and biomarkers (p≤0.037). Conclusion: With the assistance of PredictAD tool, the clinician can predict AD conversion more accurately than the current diagnostic criteria.
ACCESSION #
87623653

 

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