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P4‐124: Development and Validation of a Brief Cognitive Screening Instrument: The Sweet 16
Author(s) -
Fong Tamara G.,
Jones Richard N.,
Rudolph James L.,
Yang Frances M.,
Tommet Douglas,
Habtemariam Daniel,
Marcantonio Edward R.,
Langa Kenneth M.,
Inouye Sharon K.
Publication year - 2010
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2010.08.183
Subject(s) - equating , cohort , dementia , gerontology , cognition , receiver operating characteristic , medicine , psychometrics , mini–mental state examination , delirium , cognitive test , quality of life (healthcare) , physical therapy , cognitive impairment , psychology , psychiatry , clinical psychology , rasch model , developmental psychology , nursing , disease
Background: MR images taken from different centers appear dissimilar due to a variety of scanner-dependent effects. This situation is particularly acute in large, multi-centric settings such as ADNI. Our goal was to assess the accuracy of prediction to progression to AD for ADNI MCI subjects for an automated classification technique (Duchesne et al, Neurobiol. Aging 2008). Methods: A total of 481 subjects were available for final analysis. The Volume of Interest (VOI) Group consisted in 75 probable AD and 75 age-matched controls from the LENITEM dataset. The Study Group consisted in 331 MCI subjects from ADNI (129 progressors (1.50 years (SD: 0.69 years)) and 202 stable). MRI data for the VOI Group were acquired in Italy on a 1.0T scanner; ADNI data were acquired on 56 different 1.5T scanners. To increase technique robustness, we added noise removal and intensity standardization to the previous image pipeline. Model features were local volume change and standardized intensity sampled in a pathology-specific VOI, defined as areas of grey matter differences between AD/controls in the VOI group (Figure 1). We randomly split the Study Group in a Model Group of 166 and a Test Group of 165 subjects. We generated a linear model of 134 normally distributed image features explaining 95% of data variance from the Model Group. We projected Test Group data in the model space, and assessed classification accuracy with forward, stepwise linear discriminant analysis (p-to-enter1⁄4 0.15) in a k-fold fashion (k1⁄410), averaged over 5 trials.Results:We obtained 82.3% accuracy, 77.5% specificity, and 85.9% sensitivity with a median number of 37 variables in the discrimination function. We performed comparison studies using other publicly available data (ADAS-COG; hippocampal volumes; SPARE-AD) (these were not available for all subjects). Of note, classification based on SPARE-AD reached 71.7% accuracy on a subset of 61 MCI (Figure 2). Conclusions: With a predictive accuracy of 82.3%, on average 1.5 years before progression to clinically observed AD, the technique has the potential to alter patient management in a timely fashion, by tailoring follow-up and therapy choices.