Modeling Alzheimer's Disease Progression Using Disease Onset Time and Disease Trajectory Concepts Applied to CDR-SOB Scores From ADNI

Disease‐onset time (DOT) and disease trajectory concepts were applied to derive an Alzheimer's disease (AD) progression population model using the clinical dementia rating scale—sum of boxes (CDR‐SOB) from the AD neuroimaging initiative (ADNI) database. The model enabled the estimation of a DOT and a disease trajectory for each patient. The model also allowed distinguishing fast and slow‐progressing subpopulations according to the functional assessment questionnaire, normalized hippocampal volume, and CDR‐SOB score at study entry. On the basis of these prognostic factors, 81% of the mild cognitive impairment (MCI) subjects could correctly be assigned to slow or fast progressers, and 77% of MCI to AD conversions could be predicted whereas the model described correctly 84% of the conversions. Finally, synchronization of the biomarker‐time profiles on estimated individual DOT virtually expanded the population observation period from 3 to 8 years. DOT‐disease trajectory model is a powerful approach that could be applied to many progressive diseases.