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Erlangen Score as a tool to predict progression from mild cognitive impairment to dementia in Alzheimer's disease.

ABSTRACT: BACKGROUND:The previously described and validated Erlangen Score (ES) algorithm enables interpretation of the cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD), ordering them on an ordinal scale: from neurochemically normal (ES?=?0) through improbable AD (ES?=?1), possible AD (ES?=?2 or 3), to probable AD (ES?=?4). Here we assess the accuracy of the ES in predicting hazards of progression from the mild cognitive impairment (MCI) stage of AD to the dementia stage of the disease (Alzheimer's disease dementia (ADD)) in a novel, single-center cohort. METHODS:Baseline CSF biomarkers (amyloid beta (A?) 1-42, A?42/40, Tau, and pTau181), interpreted according to the ES, were used to estimate time to progression from the MCI stage of AD to ADD, conditional on age, gender, APOE ?4 genotype, and Mini Mental State Examination score in 144 MCI subjects, using the Extended Cox Model; the subjects were followed-up until they developed dementia or until they had been cognitively stable for at least 2?years. In addition, ES distributions were studied in 168 ADD cases and 66 neurologic controls. Further, we stratified MCI patients into those who progressed to ADD faster (within 3 years, n?=?47) and those who progressed slower (n?=?74). RESULTS:The distributions of the ES categories across the four diagnostic groups (Controls, MCI-Stable, MCI-AD, and ADD) were highly significantly different (Kruskal-Wallis ?2(df?=?3)?=?151.4, p?

SUBMITTER: Baldeiras I

PROVIDER: S-EPMC6320577 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Erlangen Score as a tool to predict progression from mild cognitive impairment to dementia in Alzheimer's disease.

Baldeiras Inês I Santana Isabel I Leitão Maria João MJ Vieira Daniela D Duro Diana D Mroczko Barbara B Kornhuber Johannes J Lewczuk Piotr P

Alzheimer's research & therapy 20190105 1

<h4>Background</h4>The previously described and validated Erlangen Score (ES) algorithm enables interpretation of the cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD), ordering them on an ordinal scale: from neurochemically normal (ES = 0) through improbable AD (ES = 1), possible AD (ES = 2 or 3), to probable AD (ES = 4). Here we assess the accuracy of the ES in predicting hazards of progression from the mild cognitive impairment (MCI) stage of AD to the dementia stage of the dis ...[more]

PMID: 30611311

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Project description:BackgroundMild cognitive impairment is often a precursor to dementia due to Alzheimer's disease, but many patients with mild cognitive impairment never develop dementia. New diagnostic criteria may lead to more patients receiving a diagnosis of mild cognitive impairment.ObjectiveTo develop a prediction index for the 3-year risk of progression from mild cognitive impairment to dementia relying only on information that can be readily obtained in most clinical settings.Design and participants382 participants diagnosed with amnestic mild cognitive impairment enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI), a multi-site, longitudinal, observational study.Main predictors measuresDemographics, comorbid conditions, caregiver report of participant symptoms and function, and participant performance on individual items from basic neuropsychological scales.Main outcome measureProgression to probable Alzheimer's disease.Key resultsSubjects had a mean (SD) age of 75 (7) years and 43% progressed to probable Alzheimer's disease within 3 years. Important independent predictors of progression included being female, resisting help, becoming upset when separated from caregiver, difficulty shopping alone, forgetting appointments, number of words recalled from a 10-word list, orientation and difficulty drawing a clock. The final point score could range from 0 to 16 (mean [SD]: 4.2 [2.9]). The optimism-corrected Harrell's c-statistic was 0.71(95% CI: 0.68-0.75). Fourteen percent of subjects with low risk scores (0-2 points, n = 124) converted to probable Alzheimer's disease over 3 years, compared to 51% of those with moderate risk scores (3-8 points, n = 223) and 91% of those with high risk scores (9-16 points, n = 35).ConclusionsAn index using factors that can be obtained in most clinical settings can predict progression from amnestic mild cognitive impairment to probable Alzheimer's disease and may help clinicians differentiate between mild cognitive impairment patients at low vs. high risk of progression.

Project description:BackgroundAmyloid pathology in subjects with mild cognitive impairment (MCI) is an important risk factor for progression to dementia due to Alzheimer's disease. Predicting the onset of dementia is challenging even in the presence of amyloid, as time to progression varies considerably among patients and depends on the onset of neurodegeneration. Survival analysis can account for variability in time to event, but has not often been applied to MRI measurements beyond singular predefined brain regions such as the hippocampus. Here we used a voxel-wise survival analysis to identify in an unbiased fashion brain regions where decreased gray matter volume is associated with time to dementia, and assessed the effects of amyloid on these associations.MethodsWe included 276 subjects with MCI (mean age 67?±?8, 41% female, mean Mini-Mental State Examination 26.6?±?2.4), baseline 3D T1-weighted structural MRI, baseline cerebrospinal fluid (CSF) biomarkers, and prospective clinical follow-up. We fitted for each voxel a proportional Cox hazards regression model to study whether decreased gray matter volume predicted progression to dementia in the total sample, and stratified for baseline amyloid status.ResultsDementia at follow-up occurred in 122 (44%) subjects over an average follow-up period of 2.5?±?1.5 years. Baseline amyloid positivity was associated with progression to dementia (hazard ratio 2.4, p?<?0.001). Within amyloid-positive subjects, decreased gray matter volume in the hippocampal, temporal, parietal, and frontal regions was associated with more rapid progression to dementia (median (interquartile range) hazard ratio across significant voxels 1.35 (1.32-1.40)). Repeating the analysis in amyloid-negative subjects revealed similar patterns (median (interquartile range) hazard ratio 1.76 (1.66-1.91)).ConclusionsIn subjects with MCI, both abnormal amyloid CSF and decreased gray matter volume were associated with future progression to dementia. The spatial pattern of decreased gray matter volume associated with progression to dementia was consistent for amyloid-positive and amyloid-negative subjects.

Project description:Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell's C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

Project description:BACKGROUND:Alzheimer's Disease (AD) can be conceptualized as a continuum: patients progress from normal cognition to mild cognitive impairment (MCI) due to AD, followed by increasing severity of AD dementia. Prior research has measured transition probabilities among later stages of AD, but not for the complete spectrum. OBJECTIVE:To estimate annual progression rates across the AD continuum and evaluate the impact of a delay in MCI due to AD on the trajectory of AD dementia and clinical outcomes. METHODS:Patient-level longitudinal data from the National Alzheimer's Coordinating Center for n=18,103 patients with multiple visits over the age of 65 were used to estimate annual, age-specific transitional probabilities between normal cognition, MCI due to AD, and AD severity states (defined by Clinical Dementia Rating score). Multivariate models predicted the likelihood of death and institutionalization for each health state, conditional on age and time from the previous evaluation. These probabilities were used to populate a transition matrix describing the likelihood of progressing to a particular disease state or death for any given current state and age. Finally, a health state model was developed to estimate the expected effect of a reduction in the risk of transitioning from normal cognition to MCI due to AD on disease progression rates for a cohort of 65-year-old patients over a 35-year time horizon. RESULTS:Annual transition probabilities to more severe states were 8%, 22%, 25%, 36%, and 16% for normal cognition, MCI due to AD, and mild/moderate/severe AD, respectively, at age 65, and increased as a function of age. Progression rates from normal cognition to MCI due to AD ranged from 4% to 10% annually. Severity of cognitive impairment and age both increased the likelihood of institutionalization and death. For a cohort of 100 patients with normal cognition at age 65, a 20% reduction in the annual progression rate to MCI due to AD avoided 5.7 and 5.6 cases of MCI due to AD and AD, respectively. This reduction led to less time spent in severe AD dementia health states and institutionalized, and increased life expectancy. CONCLUSION:Transition probabilities from normal cognition through AD severity states are important for understanding patient progression across the AD spectrum. These estimates can be used to evaluate the clinical benefits of reducing progression from normal cognition to MCI due to AD on lifetime health outcomes.

Dataset Information

Erlangen Score as a tool to predict progression from mild cognitive impairment to dementia in Alzheimer's disease.

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Erlangen Score as a tool to predict progression from mild cognitive impairment to dementia in Alzheimer's disease.

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