Disease progression model for Clinical Dementia Rating–Sum of Boxes in mild cognitive impairment and Alzheimer’s subjects from the Alzheimer’s Disease Neuroimaging Initiative

Abstract

BackgroundThe objective of this analysis was to develop a nonlinear disease progression model, using an expanded set of covariates that captures the longitudinal Clinical Dementia Rating Scale–Sum of Boxes (CDR–SB) scores. These were derived from the Alzheimer’s Disease Neuroimaging Initiative ADNI-1 study, of 301 Alzheimer’s disease and mild cognitive impairment patients who were followed for 2–3 years.MethodsThe model describes progression rate and baseline disease score as a function of covariates. The covariates that were tested fell into five groups: a) hippocampal volume; b) serum and cerebrospinal fluid (CSF) biomarkers; c) demographics and apolipoprotein Epsilon 4 (ApoE4) allele status; d) baseline cognitive tests; and e) disease state and comedications.ResultsCovariates associated with baseline disease severity were disease state, hippocampal volume, and comedication use. Disease progression rate was influenced by baseline CSF biomarkers, Trail-Making Test part A score, delayed logical memory test score, and current level of impairment as measured by CDR–SB. The rate of disease progression was dependent on disease severity, with intermediate scores around the inflection point score of 10 exhibiting high disease progression rate. The CDR–SB disease progression rate in a typical patient, with late mild cognitive impairment and mild Alzheimer’s disease, was estimated to be approximately 0.5 and 1.4 points/year, respectively.ConclusionsIn conclusion, this model describes disease progression in terms of CDR–SB changes in patients and its dependency on novel covariates. The CSF biomarkers included in the model discriminate mild cognitive impairment subjects as progressors and nonprogressors. Therefore, the model may be utilized for optimizing study designs, through patient population enrichment and clinical trial simulations.

Disease progression modeling of Alzheimer’s disease according to education level

“…Instead, previous modeling studies have reported different disease progression rates depending on disease severity, [12][13][14] the prediction of disease onset time, 15 and the impact of biomarkers on the disease course. 14,[16][17][18] In addition, our model is based on the CDR-SB, which is one of the most widely used dementia staging systems, and represents a validated, well-described, and reliable measure of disease progression. 19,20 The CDR-SB has the advantage of being able to measure changes precisely over time from the preclinical stage to the end state of disease.…”

Testing whether the progression of Alzheimer's disease change with the year of publication, additional design and geographical area: a modeling analysis of literature aggregate data

“…For example, there is substantial evidence that APOE ε4 alleles are high risk factor for AD progression [15]. Other important biomarkers, such as the ratio of p-tau 181P over Aβ in the cerebrospinal fluid and hippocampal volume [15,17,18], was also not possible to account for because the proportion of studies that did not include these key covariates was too high to be integrated into the model. Second, the extrapolation of the final model to predict ADAS-cog changes after two years might be limited.…”

“…The disease progression model, which describes the disease trajectory and underlying placebo effect, is widely used in AD clinical research to understand natural AD progression and inform future trial design [11,12]. To date, several AD disease progression models for various outcomes have been published [11,[13][14][15][16][17][18][19][20]. However, as a large amount of new trial data has been published during the past decade, it is necessary to examine whether the disease progression rate has changed over such a long period.…”

Transfer Learning for Predicting Conversion from Mild Cognitive Impairment to Dementia of Alzheimer’s Type based on 3D-Convolutional Neural Network

“…Measures For relating 3D-CNN prediction probability to diagnostic and clinical measures, we include the Clinical Dementia Rating-Sum of Boxes (CDRSB), Alzheimer's Disease Assessment Scale -cognitive 11 item (ADAS 11) and cognitive 13 item (ADAS 13), Mini Mental State Exam (MMSE), Rey Auditory Verbal Learning Test (RAVLT) -RAVLT Immediate, RAVLT Learning, RAVLT Forgetting, RAVLT Percent Forgetting, and Functional Activities Questionnaire (FAQ) [11,23,24,27,28,36].…”