Contribution of Frailty to Multimorbidity Patterns and Trajectories: Longitudinal Dynamic Cohort Study of Aging People

Abstract

Background: Multimorbidity and frailty are characteristics of aging that need individualized evaluation, and there is a 2-way causal relationship between them. Thus, considering frailty in analyses of multimorbidity is important for tailoring social and health care to the specific needs of older people.

Objective: This study aimed to assess how the inclusion of frailty contributes to identifying and characterizing multimorbidity patterns in people aged 65 years or older.

Methods: Longitudinal data were drawn from electronic health records through the SIDIAP (Sistema d'Informació pel Desenvolupament de la Investigació a l'Atenció Primària) primary care database for the population aged 65 years or older from 2010 to 2019 in Catalonia, Spain. Frailty and multimorbidity were measured annually using validated tools (eFRAGICAP, a cumulative deficit model; and Swedish National Study of Aging and Care in Kungsholmen [SNAC-K], respectively). Two sets of 11 multimorbidity patterns were obtained using fuzzy c-means. Both considered the chronic conditions of the participants. In addition, one set included age, and the other included frailty. Cox models were used to test their associations with death, nursing home admission, and home care need. Trajectories were defined as the evolution of the patterns over the follow-up period.

Results: The study included 1,456,052 unique participants (mean follow-up of 7.0 years). Most patterns were similar in both sets in terms of the most prevalent conditions. However, the patterns that considered frailty were better for identifying the population whose main conditions imposed limitations on daily life, with a higher prevalence of frail individuals in patterns like chronic ulcers &peripheral vascular. This set also included a dementia-specific pattern and showed a better fit with the risk of nursing home admission and home care need. On the other hand, the risk of death had a better fit with the set of patterns that did not include frailty. The change in patterns when considering frailty also led to a change in trajectories. On average, participants were in 1.8 patterns during their follow-up, while 45.1% (656,778/1,456,052) remained in the same pattern.

Conclusions: Our results suggest that frailty should be considered in addition to chronic diseases when studying multimorbidity patterns in older adults. Multimorbidity patterns and trajectories can help to identify patients with specific needs. The patterns that considered frailty were better for identifying the risk of certain age-related outcomes, such as nursing home admission or home care need, while those considering age were better for identifying the risk of death. Clinical and social intervention guidelines and resource planning can be tailored based on the prevalence of these patterns and trajectories.

Keywords: clustering; electronic health record; frailty; multimorbidity; primary care; trajectory.

Figure 1

Visual summary of the process of obtaining multimorbidity patterns and trajectories. A: Electronic Health Records (EHR) for three subjects, represented as a line per year of follow-up. B: Clustering gathers each year of each patient. C: Multimorbidity Pattern Assignment. D: Multimorbidity Trajectory.

Figure 2

Flow chart of the study population. The figure reports the number of individuals who met each exclusion criterion, as well as the number of individuals who met all the criteria simultaneously (unique IDs). CHI: Catalan Health Institute; SIDIAP: Sistema d’Informació pel Desenvolupament de la Investigació a l’Atenció Primària.

Figure 3

Prevalence of each multimorbidity pattern for each age. (A) Multimorbidity & age; (B) multimorbidity & frailty. For each age, the information considered is from the individuals of that age in any time, regardless of the year of the study.

Figure 4

Transition matrices for multimorbidity & age (A) and multimorbidity & frailty (B) with k=11. Each cell shows the proportion of individuals transitioning from their initial pattern (y-axis) to the last pattern observed (x-axis).