Publications by authors named "James R C Davis"

2 Publications

  • Page 1 of 1

SART and Individual Trial Mistake Thresholds: Predictive Model for Mobility Decline.

Geriatrics (Basel) 2021 Aug 31;6(3). Epub 2021 Aug 31.

The Irish Longitudinal Study on Ageing, Trinity College Dublin, D02 R590 Dublin, Ireland.

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 , 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; < 0.001), and the only significant predictor of new falls (OR = 1.11; 95% CI 1.03-1.21; = 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.
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http://dx.doi.org/10.3390/geriatrics6030085DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8482118PMC
August 2021

The Importance of Age in the Prediction of Mortality by a Frailty Index: A Machine Learning Approach in the Irish Longitudinal Study on Ageing.

Geriatrics (Basel) 2021 Aug 27;6(3). Epub 2021 Aug 27.

The Global Brain Health Institute (GBHI), Trinity College Dublin, D02 PN40 Dublin, Ireland.

The quantification of biological age in humans is an important scientific endeavor in the face of ageing populations. The frailty index (FI) methodology is based on the accumulation of health deficits and captures variations in health status within individuals of the same age. The aims of this study were to assess whether the addition of age to an FI improves its mortality prediction and whether the associations of the individual FI items differ in strength. We utilized data from The Irish Longitudinal Study on Ageing to conduct, by sex, machine learning analyses of the ability of a 32-item FI to predict 8-year mortality in 8174 wave 1 participants aged 50 or more years. By wave 5, 559 men and 492 women had died. In the absence of age, the FI was an acceptable predictor of mortality with AUCs of 0.7. When age was included, AUCs improved to 0.8 in men and 0.9 in women. After age, deficits related to physical function and self-rated health tended to have higher importance scores. Not all FI variables seemed equally relevant to predict mortality, and age was by far the most relevant feature. Chronological age should remain an important consideration when interpreting the prognostic significance of an FI.
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http://dx.doi.org/10.3390/geriatrics6030084DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8482125PMC
August 2021
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