Nursing home (NH) residents with dementia is exposed to high rates of psychotropic prescriptions. Our objectives were to: (1) pool the prevalence estimates of psychotropic polypharmacy from the existing literature and (2) examine potentially influential factors that are related to a higher or lower prevalence.

Meta-analysis of data collected from randomized trials, quasi-experimental, prospective or retrospective cohort, and cross-sectional studies. English-language searches of PubMed and PsycINFO were completed by November 2020. Included studies reported prevalence estimates of psychotropic polypharmacy (i.e. defined as either two-or-more or three-or-more medications concurrently) in NH residents with dementia.

NH residents with dementia.

Random-effects models were used to pool the prevalence of psychotropic polypharmacy in NH residents with dementia across studies. Estimates were provided for both two-or-more and three-or-more concurrent medications. Heterogeneity and publication bias were measured. Meta-regression examined the influence of the percentage of the sample who were male, mean age of the sample, geographic region (continent), sample size, and study year on the prevalence of psychotropic polypharmacy.

Twenty-five unique articles were included comprising medications data from 92,370 NH residents with dementia in 12 countries. One-in-three (33%, [95% CI: 28%, 39%]) NH residents with dementia received two-or-more psychotropic medications concurrently. One-in-eight (13%, [95% CI: 10%, 17%]) received three-or-more psychotropic medications concurrently. Estimates were highly variable across both definitions of psychotropic polypharmacy (p < 0.001). Among study-level demographics, geographic region, sample size, or study year, only male sex was associated with greater use of two-or-more psychotropic medications (Unadjusted OR = 1.02, p = 0.006; Adjusted OR = 1.04, p = 0.07).

Psychotropic polypharmacy is common among NH residents with dementia. Identifying the causes of utilization and the effects on resident health and well-being should be prioritized by federal entities seeking to improve NH quality.

To compare cognitive phenotypes of participants with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI), estimate progression to MCI/dementia by phenotype and assess classification error with machine learning.

Dataset consisted of 163 participants with SCD and 282 participants with aMCI from the Czech Brain Aging Study. Cognitive assessment included the Uniform Data Set battery and additional tests to ascertain executive function, language, immediate and delayed memory, visuospatial skills, and processing speed. Latent profile analyses were used to develop cognitive profiles, and Cox proportional hazards models were used to estimate risk of progression. Random forest machine learning algorithms reported cognitive phenotype classification error.

Latent profile analysis identified three phenotypes for SCD, with one phenotype performing worse across all domains but not progressing more quickly to MCI/dementia after controlling for age, sex, and education. Three aMCI phenotypes were characterized by mild deficits, memory and language impairment (dysnomic aMCI), and severe multi-domain aMCI (i.e., deficits across all domains). A dose–response relationship between baseline level of impairment and subsequent risk of progression to dementia was evident for aMCI profiles after controlling for age, sex, and education. Machine learning more easily classified participants with aMCI in comparison to SCD (8% vs. 21% misclassified).

Cognitive performance follows distinct patterns, especially within aMCI. The patterns map onto risk of progression to dementia.