Clinical Manifestations.

BACKGROUND

The Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) are used as clinical outcome assessments (COAs) to investigate Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD). The Cogstate Brief Battery (CBB) is a digital assessment of psychomotor function, attention, visual learning, and working memory. Digital assessments may overcome limitations of traditional assessments but score conversions haven't been undertaken.

METHOD

Data were aggregated for baseline visits from the A4, ADNI, and AXON studies. Participants included healthy/cognitively normal (HC), MCI, pre-clinical AD, and mild AD. An equipercentile linking routine for single-group designs, using bootstrap-resampled standard error (SE; variability of estimated CBB scores for each COA score) and bias (mean differences of resampled results from full model) metrics, was employed to fit score distributions. Reaction time (RT; log10 ms) and accuracy (proportion correct) for each of the CBB tests and two composite accuracy (LWM) and reaction time (ATT) scores were fit to ADAS-Cog and MMSE totals.

RESULT

Data for 8,878 participants were analyzed (HC = 4,021; PC = 773; MCI = 420; AD = 414; Unclassified/Mixed = 3,250). Scores ranged from 0-39 (ADAS-Cog13), 0-27 (ADAS-Cog11), and 9-30 (MMSE). Estimates of CBB scores by COA scores showed expected associations between assessments. CBB composites for Accuracy and RT at MMSE > = 26 were estimated at approximately 0.7-0.9 and 2.8-2.6 respectively. SE in ADAS-Cog and MMSE totals from CBB scores were larger for scores which reflect worse performance and were from smaller samples (accuracy: > 0.10; RT > 1.0). MMSE RT SE estimates were significantly smaller (all < 0.2) than ADAS-Cog estimates. Results of both ADAS-Cog versions were virtually identical.

CONCLUSION

Tables for converting ADAS-Cog and MMSE totals to CBB scores will be provided. Results can be reliably converted when performance is clinically better. Lack of samples in lower performance ranges presents an issue for reliable conversion. Demonstrating how traditional COAs compare to digital assessments can aid clinical interpretation and the understanding of limitations in each assessment. Using score equating tables may help establish a robust method for exploring associations and differences between assessments and scores.

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