Impact of an Area Agency on Aging pharmacist-led Community Care Transition Initiative.

BACKGROUND: Rural older adults are at risk of readmissions and medication-related problems after hospital discharge. OBJECTIVES: This study aimed to compare 30-day hospital readmissions between participants and nonparticipants and describe medication therapy problems (MTPs) and barriers to care, self-management, and social needs among participants. PRACTICE DESCRIPTION: The Michigan Region VII Area Agency on Aging (AAA) Community Care Transition Initiative (CCTI) for rural older adults after hospitalization. PRACTICE INNOVATION: Eligible AAA CCTI participants were identified by an AAA community health worker (CHW) trained as a pharmacy technician. Eligibility criteria were Medicare insurance; diagnoses at risk of readmission; length of stay, acuity of admission, comorbidities, and emergency department visits score more than 4; and discharge to home from January 2018 to December 2019. The AAA CCTI included a CHW home visit, telehealth pharmacist comprehensive medication review (CMR), and follow-up for up to 1 year. EVALUATION METHODS: A retrospective cohort study examined the primary outcomes of 30-day hospital readmissions and MTPs, categorized by the Pharmacy Quality Alliance MTP Framework. Primary care provider (PCP) visit completion, barriers to self-management, health, and social needs were collected. Descriptive statistics, Mann-Whitney U, and chi-square analyses were used. RESULTS: Of 825 eligible discharges, 477 (57.8%) enrolled in the AAA CCTI; differences in 30-day readmissions between participants and nonparticipants were not statistically significant (11.5% vs. 16.1%, P = 0.07). More than one-third of participants (34.6%) completed their PCP visit within 7 days. MTPs were identified in 76.1% of the pharmacist visits (mean MTP 2.1 [SD 1.4]). Adherence (38.2%) and safety-related (32.0%) MTPs were common. Physical health and financial issues were barriers to self-management. CONCLUSION: AAA CCTI participants did not have lower hospital readmission rates. The AAA CCTI identified and addressed barriers to self-management and MTPs in participants after the care transition home. Community-based, patient-centered strategies to improve medication use and meet rural adults' health and social needs after care transitions are warranted.

A Computable Algorithm for Medication Optimization in Heart Failure With Reduced Ejection Fraction.

Background: Guideline-directed medical therapy (GDMT) optimization can improve outcomes in heart failure with reduced ejection fraction. Objectives: The objective of this study was to determine if a novel computable algorithm appropriately recommended GDMT. Methods: Clinical trial data from the GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure) and HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) trials were evaluated with a computable medication optimization algorithm that outputs GDMT recommendations and a medication optimization score (MOS). Algorithm-based recommendations were compared to medication changes. A Cox proportional-hazards model was used to estimate the associations between MOS and the composite primary end point for both trials. Results: The algorithm recommended initiation of angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, beta-blockers, and mineralocorticoid receptor antagonists in 52.8%, 34.9%, and 68.1% of GUIDE-IT visits, respectively, when not prescribed the drug. Initiation only occurred in 20.8%, 56.9%, and 15.8% of subsequent visits. The algorithm also identified dose titration in 48.8% of visits for angiotensin-converting enzyme inhibitor/angiotensin receptor blockers and 39.4% of visits for beta-blockers. Those increases only occurred in 24.3% and 36.8% of subsequent visits. A higher baseline MOS was associated with a lower risk of cardiovascular death or heart failure hospitalization (HR: 0.41; 95% CI: 0.21-0.80; P = 0.009) in GUIDE-IT and all-cause death and hospitalization (HR: 0.61; 95% CI: 0.44-0.84; P = 0.003) in HF-ACTION. Conclusions: The algorithm accurately identified patients for GDMT optimization. Even in a clinical trial with robust protocols, GDMT could have been further optimized in a meaningful number of visits. The algorithm-generated MOS was associated with a lower risk of clinical outcomes. Implementation into clinical care may identify and address suboptimal GDMT in patients with heart failure with reduced ejection fraction.

The Effects of the ManageHF4Life Mobile App on Patients With Chronic Heart Failure: Randomized Controlled Trial.

BACKGROUND: The successful management of heart failure (HF) involves guideline-based medical therapy as well as self-management behavior. As a result, the management of HF is moving toward a proactive real-time technological model of assisting patients with monitoring and self-management. OBJECTIVE: The aim of this paper was to evaluate the efficacy of enhanced self-management via a mobile app intervention on health-related quality of life, self-management, and HF readmissions. METHODS: A single-center randomized controlled trial was performed. Participants older than 45 years and admitted for acute decompensated HF or recently discharged in the past 4 weeks were included. The intervention group ("app group") used a mobile app, and the intervention prompted daily self-monitoring and promoted self-management. The control group ("no-app group") received usual care. The primary outcome was the change in Minnesota Living with Heart Failure Questionnaire (MLHFQ) score from baseline to 6 and 12 weeks. Secondary outcomes were the Self-Care Heart Failure Index (SCHFI) questionnaire score and recurrent HF admissions. RESULTS: A total of 83 participants were enrolled and completed all baseline assessments. Baseline characteristics were similar between the groups except for the prevalence of ischemic HF. The app group had a reduced MLHFQ at 6 weeks (mean 37.5, SD 3.5 vs mean 48.2, SD 3.7; P=.04) but not at 12 weeks (mean 44.2, SD 4 vs mean 45.9, SD 4; P=.78), compared to the no-app group. There was no effect of the app on the SCHFI at 6 or 12 weeks. The time to first HF readmission was not statistically different between the app group and the no-app group (app group 11/42, 26% vs no-app group 12/41, 29%; hazard ratio 0.89, 95% CI 0.39-2.02; P=.78) over 12 weeks. CONCLUSIONS: The adaptive mobile app intervention, which focused on promoting self-monitoring and self-management, improved the MLHFQ at 6 weeks but did not sustain its effects at 12 weeks. No effect was seen on HF self-management measured by self-report. Further research is needed to enhance engagement in the app for a longer period and to determine if the app can reduce HF readmissions in a larger study. TRIAL REGISTRATION: ClinicalTrials.gov NCT03149510; https://clinicaltrials.gov/ct2/show/NCT03149510.