The Severity-Calibrated Aphasia Naming Test

We present a 20-item naming test, the Severity-Calibrated Aphasia Naming Test (SCANT), that can serve as a proxy measure for an aphasia severity scale that is derived from a thorough test battery of connected speech production, single word production, speech repetition, and auditory verbal comprehension. We use feature selection techniques from machine learning to identify an optimal set of pictured items from a set of 174 pictures to be named for prediction of aphasia severity, based on data from 200 quasi-randomly selected participants with left-hemisphere stroke. We demonstrate the superiority of predictions based on the SCANT over those based on the full set of naming items. We estimate a 15% reduction in power when using the SCANT score versus the full test battery’s aphasia severity score as an outcome measure; for example, in order to maintain the same power to detect a significant average change in aphasia severity, a study with 25 participants using the full test battery to measure treatment effectiveness would require 30 participants if the SCANT were to be used as the testing instrument instead. The SCANT has extremely high inter-rater reliability, and it is sensitive and specific to the presence of aphasia. We provide a linear model to convert SCANT scores to aphasia severity scores, and we identify a change score cutoff of four SCANT items based on test-retest SCANT data and the modeled relation between SCANT and aphasia severity scores.