Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment

Simonsen, Anja Hviid; Mattila, Jussi; Hejl, Anne Mette; Frederiksen, Kristian Steen; Herukka, Sanna Kaisa; Hallikainen, Merja; Gils, Mark van; Lötjönen, Jyrki; Waldemar, Gunhild

doi:10.1159/000345554

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…The PredictAD tool developed by this group has been further evaluated. This software increased classification accuracy over clinicians provided with test results directly in paper form and improved inter-rater agreement and the rater’s confidence in their decision [417]. Liu et al [418] examined the efficacy of PredictAD software in predicting AD conversion within 3 years in comparison with currently recommended criteria for prodromal AD: episodic memory impairment, visual assessment of medial temporal atrophy, and abnormal CSF biomarkers.…”

Section: Methods Papersmentioning

confidence: 99%

“…Simonsen et al [417] compared the accuracy of their PredictAD software with that of clinical raters asked to classify patients into a range of categories depending on their likelihood of developing the disease. The software increased classification accuracy over paper information alone from 63.2% to 70% and additionally improved interrater agreement and increased the raters’ confidence in their decision.…”

Section: Studies Of the Adni Cohortmentioning

confidence: 99%

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2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Weiner

Veitch

Aisen

et al. 2015

Alzheimer's & Dementia

272

224

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The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer’s disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer’s Dis 2006;9(Suppl 3):151–3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [18F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding ...

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Section: Methods Papersmentioning

confidence: 99%

Section: Studies Of the Adni Cohortmentioning

confidence: 99%

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Weiner

Veitch

Aisen

et al. 2015

Alzheimer's & Dementia

272

224

View full text Add to dashboard Cite

show abstract

“…In earlier studies, we have reported the DSI's ability to differentiate between AD and healthy aging [4] and the PredictAD tool's ability to provide decision support in predicting conversion from MCI to AD [8]. When comparing the three classification methods in this study, the DSI from the PredictAD tool performed significantly better than both the naïve Bayesian classifier and random forest at differentiating patients with AD from patients with other dementias.…”

Section: Discussionmentioning

confidence: 70%

“…Using their baseline data, clinicians predicted which subjects would progress to AD [8]. The prediction accuracy was significantly higher when using the PredictAD tool compared to using paper charts.…”

Section: Introductionmentioning

confidence: 99%

Application of the PredictAD Decision Support Tool to a Danish Cohort of Patients with Alzheimer's Disease and Other Dementias

Simonsen

Mattila

Hejl

et al. 2013

Dement Geriatr Cogn Disord

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Background: The diagnosis of Alzheimer's disease (AD) is based on an ever-increasing body of data and knowledge making it a complex task. The PredictAD tool integrates heterogeneous patient data using an interactive user interface to provide decision support. The aim of this project was to investigate the performance of the tool in distinguishing AD from non-AD dementia using a realistic clinical dataset. Methods: We retrieved clinical data from a group of patients diagnosed with AD (n = 72), vascular dementia (VaD, n = 30), frontotemporal dementia (FTD, n = 25) or dementia with Lewy bodies (DLB, n = 14) at the Copenhagen Memory Clinic at Rigshospitalet. Three classification methods were applied to the data in order to differentiate between AD and a group of non-AD dementias. The methods were the PredictAD tool's Disease State Index (DSI), the naïve Bayesian classifier and the random forest. Results: The DSI performed best for this realistic dataset with an accuracy of 76.6% compared to the accuracies for the naïve Bayesian classifier and random forest of 67.4 and 66.7%, respectively. Furthermore, the DSI differentiated between the four diagnostic groups with a p value of <0.0001. Conclusion: In this dataset, the DSI method used by the PredictAD tool showed a superior performance for the differentiation between patients with AD and those with other dementias. However, the methods need to be refined further in order to optimize the differential diagnosis between AD, FTD, VaD and DLB.

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“…Disease State Index (DSI) and Disease State Fingerprint (DSF) [ 9 , 10 ] form a decision support system, first developed for the PredictAD diagnostic tool, to help clinicians in establishing the prognosis for patients with mild cognitive impairment (MCI) or to assist in the diagnosis between different dementia cases [ 11 , 12 , 13 ]. DSI functions as a classifier, measuring the similarity of the patient's data to that collected from populations with known diagnoses, i.e.…”

Section: Introductionmentioning

confidence: 99%

Using the Disease State Fingerprint Tool for Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease

Muñoz-Ruiz

Hall

Mattila

et al. 2016

Dement Geriatr Cogn Disord Extra

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Background: Disease State Index (DSI) and its visualization, Disease State Fingerprint (DSF), form a computer-assisted clinical decision making tool that combines patient data and compares them with cases with known outcomes. Aims: To investigate the ability of the DSI to diagnose frontotemporal dementia (FTD) and Alzheimer's disease (AD). Methods: The study cohort consisted of 38 patients with FTD, 57 with AD and 22 controls. Autopsy verification of FTD with TDP-43 positive pathology was available for 14 and AD pathology for 12 cases. We utilized data from neuropsychological tests, volumetric magnetic resonance imaging, single-photon emission tomography, cerebrospinal fluid biomarkers and the APOE genotype. The DSI classification results were calculated with a combination of leave-one-out cross-validation and bootstrapping. A DSF visualization of a FTD patient is presented as an example. Results: The DSI distinguishes controls from FTD (area under the receiver-operator curve, AUC = 0.99) and AD (AUC = 1.00) very well and achieves a good differential diagnosis between AD and FTD (AUC = 0.89). In subsamples of autopsy-confirmed cases (AUC = 0.97) and clinically diagnosed cases (AUC = 0.94), differential diagnosis of AD and FTD performs very well. Conclusions: DSI is a promising computer-assisted biomarker approach for aiding in the diagnostic process of dementing diseases. Here, DSI separates controls from dementia and differentiates between AD and FTD.

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Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment

Cited by 11 publications

References 13 publications

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Application of the PredictAD Decision Support Tool to a Danish Cohort of Patients with Alzheimer's Disease and Other Dementias

Using the Disease State Fingerprint Tool for Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease

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