Neuropsychological and Imaging Predictors of Longitudinal Cognitive Outcomes in Persons with Mild Cognitive Impairment (MCI)
Prashanth Poulose1, Ramshekhar Menon 1, Ravi Prasad Varma2, Meenu Surendran1, Sushama Ramachandran1, Rajesh Pillai1, Bejoy Thomas3
1Neurology, 2Epidemiology , AMCHSS , SCTIMST , Trivandrum, 3Neuroradiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology
Objective:
To explore the longitudinal cognitive outcomes and to ascertain predictors of conversion to dementia in a hospital-based mild cognitive impairment (MCI) cohort classified according to the neuropsychological phenotype at baseline
Background:
Gap in knowledge and dearth of indigenous literature , as there were no similar hospital based studies which identified MCI predictors and their longitudinal outcomes from the Indian subcontinent
Design/Methods:
Subjects aged >55 years who had a clinical diagnosis of MCI at initial visit between 2010 and 2018, with at least one formal neuropsychological assessment at baseline and follow-up of a minimum of 2 years were included. The prospective study was completed based on evaluation at last follow-up to gauge conversion to dementia, quantification of performance on activities of daily living and when available, longitudinal neuropsychological test scores
Results:
Ninety-five patients with MCI met the inclusion criteria with a mean age of 68.4 ± 6.4 years at baseline and a mean duration of follow-up for 6.4 ± 3.2 years. The cumulative conversion rate to dementia was 22.2% (21/95) and the annualized conversion rate was 3.3% per year of follow-up. The majority of subjects who had converted had multidomain MCI (66%). Only white matter changes on MRI brain revealed correlation with baseline neuropsychology tests. The multivariate logistic regression analysis revealed the utility of lower baseline list recognition (adjusted odds ratio: 0.735 [95% confidence interval: 0.589–0.916]; p 0.006), lower immediate logical memory (0.885 [0.790–0.990]; p 0.03), and high perseverative error scores on set shifting (3.116 [1.425–6.817]; p 0.004) as predictors of conversion. A model score of +2.615 could predict conversion with sensitivity of 72% and specificity of 98% over 6.4 years follow-up
Conclusions:
There was a higher risk of conversion associated with multidomain MCI. Logistic regression-based estimations of dementia risk utilizing domain-based neuropsychology test scores in MCI have high specificity for diagnosis at baseline