Cognitive and functional progression in Alzheimer disease: A prediction model of latent classes

• Published in: International journal of geriatric psychiatry 2018-08, Vol.33 (8), p.1057-1064
• Main Authors: Haaksma, Miriam L., Calderón-Larrañaga, Amaia, Olde Rikkert, Marcel G.M., Melis, René J.F., Leoutsakos, Jeannie‐Marie S.
• Format: Article
• Language: English
• Subjects: Activities of Daily Living; Aged; Aged, 80 and over; Alzheimer Disease - classification; Alzheimer Disease - diagnosis; Alzheimer Disease - physiopathology; Alzheimer's disease; cognition; Cognition Disorders - diagnosis; Cognitive ability; Cohort Studies; Dementia; Dementia disorders; Disability Evaluation; disease course; Disease Progression; Education; Female; functioning; Geriatric psychiatry; growth mixture model; Humans; Logistic Models; Male; Medicin och hälsovetenskap; Mental Status and Dementia Tests; Middle Aged; Models, Theoretical; Neurodegenerative diseases; Neuropsychological Tests; Psychiatric Status Rating Scales; ROC Curve; trajectory; Utah
• ISSN: 0885-6230; 1099-1166; 1099-1166
• DOI: 10.1002/gps.4893

Objective We sought to replicate a previously published prediction model for progression, developed in the Cache County Dementia Progression Study, using a clinical cohort from the National Alzheimer's Coordinating Center. Methods We included 1120 incident Alzheimer disease (AD) cases with at least one assessment after diagnosis, originating from 31 AD centres from the United States. Trajectories of the Mini‐Mental State Examination (MMSE) and Clinical Dementia Rating sum of boxes (CDR‐sb) were modelled jointly over time using parallel‐process growth mixture models in order to identify latent classes of trajectories. Bias‐corrected multinomial logistic regression was used to identify baseline predictors of class membership and compare these with the predictors found in the Cache County Dementia Progression Study. Results The best‐fitting model contained 3 classes: Class 1 was the largest (63%) and showed the slowest progression on both MMSE and CDR‐sb; classes 2 (22%) and 3 (15%) showed moderate and rapid worsening, respectively. Significant predictors of membership in classes 2 and 3, relative to class 1, were worse baseline MMSE and CDR‐sb, higher education, and lack of hypertension. Combining all previously mentioned predictors yielded areas under the receiver operating characteristic curve of 0.70 and 0.75 for classes 2 and 3, respectively, relative to class 1. Conclusions Our replication study confirmed that it is possible to predict trajectories of progression in AD with relatively good accuracy. The class distribution was comparable with that of the original study, with most individuals being members of a class with stable or slow progression. This is important for informing newly diagnosed AD patients and their caregivers.