SART and Individual Trial Mistake Thresholds: Predictive Model for Mobility Decline.
Rossella RizzoSilvin Paul KnightJames R C DavisLouise NewmanEoin DugganRose Anne KennyRomán Romero OrtuñoPublished in: Geriatrics (Basel, Switzerland) (2021)
The Sustained Attention to Response Task (SART) has been used to measure neurocognitive functions in older adults. However, simplified average features of this complex dataset may result in loss of primary information and fail to express associations between test performance and clinically meaningful outcomes. Here, we describe a new method to visualise individual trial (raw) information obtained from the SART test, vis-à-vis age, and groups based on mobility status in a large population-based study of ageing in Ireland. A thresholding method, based on the individual trial number of mistakes, was employed to better visualise poorer SART performances, and was statistically validated with binary logistic regression models to predict mobility and cognitive decline after 4 years. Raw SART data were available for 4864 participants aged 50 years and over at baseline. The novel visualisation-derived feature bad performance, indicating the number of SART trials with at least 4 mistakes, was the most significant predictor of mobility decline expressed by the transition from Timed Up-and-Go (TUG) < 12 to TUG ≥ 12 s (OR = 1.29; 95% CI 1.14-1.46; p < 0.001), and the only significant predictor of new falls (OR = 1.11; 95% CI 1.03-1.21; p = 0.011), in models adjusted for multiple covariates. However, no SART-related variables resulted significant for the risk of cognitive decline, expressed by a decrease of ≥2 points in the Mini-Mental State Examination (MMSE) score. This novel multimodal visualisation could help clinicians easily develop clinical hypotheses. A threshold approach to the evaluation of SART performance in older adults may better identify subjects at higher risk of future mobility decline.
Keyphrases
- cognitive decline
- mild cognitive impairment
- study protocol
- phase iii
- clinical trial
- physical activity
- phase ii
- randomized controlled trial
- palliative care
- type diabetes
- health information
- machine learning
- mental health
- healthcare
- bipolar disorder
- deep learning
- electronic health record
- big data
- working memory
- ionic liquid
- chronic pain
- adipose tissue
- social media
- drug induced