Performance of Digital Cognitive Assessment in Predicting Dementia Stages Delineated by Dementia Severity Rating Scale

Dementia is characterized by impairments in an individual’s cognitive and functional abilities. Digital cognitive assessments have been shown to be effective in detecting mild cognitive impairment and dementia but whether they can stage the disease remains to be studied. Retrospective analysis was performed on a BrainCheck dataset containing 1,751 dementia patients with different cognitive and functional assessments completed for cognitive care planning, including the Dementia Severity Rating Scale (DSRS), the Katz Index of Independence in Activities of Daily Living (ADL), and the BrainCheck standard battery of cognitive assessments, BrainCheck Assess (BC-Assess). The patients were staged according to their DSRS total score (DSRS-TS): 982 mild (DSRS-TS: 10-18), 656 moderate (19-26), and 113 severe (37-54) patients. Pearson correlation was used to assess the associations between BC-Assess overall score (BC-OS), ADL total score (ADL-TS), and DSRS-TS. Logistic regression was used to evaluate the possibility of using patients’ BC-OS and ADL-TS to predict their stage. We find moderate Pearson correlations between DSRS-TS and BC-OS (r=-0.53), between DSRS-TS and ADL-TS (r=-0.55), and a weak correlation between BC-OS and ADL-TS (r=0.37). Both BC-OS and ADL-TS significantly decrease with increasing severity. BC-OS demonstrates to be a good predictor of dementia stages, with area under the ROC curve (ROC-AUC) of classification using logistic regression ranging from 0.733 to 0.917. When BC-Assess is combined with ADL, higher prediction accuracies are achieved, with ROC-AUC ranging from 0.786 to 0.961. Our results suggest that BC-Assess could serve as an effective alternative tool to DSRS for grading dementia severity, particularly in cases where DSRS, or other global assessments, may be challenging to obtain due to logistical and time constraints.