Dataset Information

A 'Comprehensive Visual Rating Scale' for predicting progression to dementia in patients with mild cognitive impairment.

ABSTRACT: BACKGROUND:Numerous efforts have been made to identify biomarkers for predicting the progression of dementia in patients with mild cognitive impairment (MCI), and recently, a comprehensive visual rating scale (CVRS) based on magnetic resonance imaging (MRI) has been validated to assess structural changes in the brain of elderly patients. Based on this, the present study investigated the use of CVRS for predicting dementia and elucidated its association with cognitive change in patients with MCI over a three-year follow-up. METHODS:We included 340 patients with MCI with more than one follow-up visit. Data were obtained from the Alzheimer's disease Neuroimaging Initiative study. We assessed all the patients using CVRS and determined their progression to dementia during a follow-up period of over 3 years. The cox proportional hazards model was used to analyze hazard ratios (HRs) of CVRS for disease progression. Further, multiple cognitive measures of the patients over time were fitted using the random effect model to assess the effect of initial CVRS score on subsequent cognitive changes. RESULTS:Of 340 patients, 69 (20.2%) progressed to dementia and the median baseline score (interquartile range) of CVRS significantly differed between stable MCI and progressive MCI (9 (5-13) vs 13 (8-17), p<0.001). The initial CVRS score was independently associated with an increased risk of progression to dementia (HR 1.123, 95% confidence interval [CI] 1.059-1.192). From 12 to 24 months, the effect of the interaction between CVRS and interval of follow-up visit on cognitive performance achieved significance (p<0.001). CONCLUSIONS:Baseline CVRS predicted the progression to dementia in patients with MCI, and was independently associated with longitudinal cognitive decline.

SUBMITTER: Jang JW

PROVIDER: S-EPMC6101367 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A 'Comprehensive Visual Rating Scale' for predicting progression to dementia in patients with mild cognitive impairment.

Jang Jae-Won JW Park Jeong Hoon JH Kim Seongheon S Park Young Ho YH Pyun Jung-Min JM Lim Jae-Sung JS Kim Youngho Y Youn Young Chul YC Kim SangYun S

PloS one 20180820 8

<h4>Background</h4>Numerous efforts have been made to identify biomarkers for predicting the progression of dementia in patients with mild cognitive impairment (MCI), and recently, a comprehensive visual rating scale (CVRS) based on magnetic resonance imaging (MRI) has been validated to assess structural changes in the brain of elderly patients. Based on this, the present study investigated the use of CVRS for predicting dementia and elucidated its association with cognitive change in patients w ...[more]

PMID: 30125290

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Project description:BackgroundPredicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion.MethodsIn the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window".ResultsThe proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set.ConclusionsPrognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.

Project description:BackgroundIndividuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level.MethodsUsing baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework.ResultsCognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions.ConclusionsWe developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment.

Dataset Information

A 'Comprehensive Visual Rating Scale' for predicting progression to dementia in patients with mild cognitive impairment.

Publications

A 'Comprehensive Visual Rating Scale' for predicting progression to dementia in patients with mild cognitive impairment.

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