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The article presents a study which aimed to understand from clinicians and older adults ways to optimize a proposed home-based deprescribing intervention that focuses on tailoring medications based on patient's daily and life goals. Topics include clinicians included in the study, daily activities/goals for older adults, and the need to identify older adults prototypes and design flexible deprescribing interventions that can target a broad range of older adults for maximum impact.

Background: Although the atherogenic index of plasma (AIP) based on a single measurement is a known risk factor for cardiovascular disease (CVD), little is known about whether changes in AIP over time are related to incident CVD. We aimed to determine whether AIP trajectory, which reflects homogenous AIP trends for a particular period, is associated with CVD risk. Methods: Data from 5,843 participants of the Korean Genome and Epidemiology Study (KoGES) were analyzed. The KoGES had been conducted biennially from the baseline survey (2001–2002) to the eighth follow-up survey (2017–2018). The research design specifies the exposure period from baseline to the third follow-up, designates the latent period at the fourth follow-up, and establishes the event accrual period from the fifth to the eighth follow-up. During the exposure period, we identified two trajectories: a decreasing (n = 3,036) and an increasing group (n = 2,807) using latent variable mixture modeling. Information on CVD was collected initially through the self-reporting, followed by in depth person-to-person interview conducted by a well-trained examiner. During the event accrual period, the cumulative incidence rates of CVD between the two AIP trajectory groups were estimated using Kaplan–Meier analysis with the log-rank test. Multiple Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: The increasing AIP trajectory group had a significantly higher cumulative incidence rate of CVD than the decreasing AIP trajectory group. Compared to the decreasing AIP trajectory group, the increasing AIP trajectory group had a higher risk of incident CVD (HR: 1.31, 95% CI: 1.02–1.69) after adjusting for confounders. Conclusions: The risk of incident CVD increased when the AIP level showed an increasing trend and remained high over a long period. This suggests that checking and managing the trajectory of the AIP can be a preventive strategy for incident CVD. [ABSTRACT FROM AUTHOR]