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Medication nonadherence following myocardial infarction (MI) is prevalent and increases the risk of recurrent cardiovascular events. Socioeconomic factors including medication cost, financial insecurity, and poor health literacy are associated with nonadherence. We present a patient with a history of recurrent MI who was nonadherent due to socioeconomic challenges. Our patient subsequently developed ST-elevation MI secondary to in-stent thrombosis. This case illustrates the importance of pre-discharge screening for barriers to adherence.

Introduction: In patients with heart failure with reduced ejection fraction (HFrEF), use of guideline directed medical therapies (GDMT) reduces mortality. Whether there is geographic variation in GDMT prescribing is not well-characterized. Hypothesis: We assessed the hypothesis that there is wide geographic variation nationally within the Veterans Affairs (VA) healthcare system for the receipt of GDMT. Methods: We linked the primary residence address of a cohort of Veterans with HFrEF receiving care at VA facilities (n=178,856) to hospital referral regions (HRRs). Using VA and non-VA pharmacy data between July 1, 2020 and July 1, 2021, we defined receipt of GDMT. For each HRR, we calculated the percentage of eligible Veterans that were prescribed each class of GDMT and a composite GDMT z-score. Lastly, we constructed national choropleth maps to depict HRR geographic variation in composite z-scores and prescription of each class of GDMT. Results: There was significant variation in individual GDMT class prescription and composite score across HRRs as shown in choropleth maps ( Figure) . Within HRRs, beta-blocker prescription was highest, with a median 80% of Veterans (interquartile range [IQR] 77.3% to 82.2%), afterload-reducing agents including angiotensin converting enzyme inhibitor / angiotensin receptor blocker / angiotensin receptor-neprilysin inhibitors (ARNI) 69.3% (IQR 66.4% to 72.1%), mineralocorticoid receptor antagonists (MRA) 29.2% (IQR 25.8% to 33.9%), sodium-glucose cotransporter 2 inhibitors (SGLT2I) 10.3% (IQR 7.7% to 12.8%), and ARNI 12.2% (IQR 8.6% to 15.3%). HRRs with the highest and lowest composite scores were often found in the same U.S. Census Regions. Conclusions: In conclusion, wide geographic disparities are present for GDMT prescribing to Veterans across HRRs. Veteran receipt of ARNI, SGLT2I, and MRA is low nationwide. Targeted approaches may be necessary to improve GDMT prescription for Veterans in lower performing HRRs.

Current progenitor cell therapies have only modest efficacy, which has limited their clinical adoption. This may be the result of a cellular heterogeneity that decreases the number of functional progenitors delivered to diseased tissue, and prevents correction of underlying pathologic cell population disruptions. Here, we develop a high-resolution method of identifying phenotypically distinct progenitor cell subpopulations via single-cell transcriptional analysis and advanced bioinformatics. When combined with high-throughput cell surface marker screening, this approach facilitates the rational selection of surface markers for prospective isolation of cell subpopulations with desired transcriptional profiles. We establish the usefulness of this platform in costly and highly morbid diabetic wounds by identifying a subpopulation of progenitor cells that is dysfunctional in the diabetic state, and normalizes diabetic wound healing rates following allogeneic application. We believe this work presents a logical framework for the development of targeted cell therapies that can be customized to any clinical application.

Current progenitor cell therapies have only modest efficacy, which has limited their clinical adoption. This may be the result of a cellular heterogeneity that decreases the number of functional progenitors delivered to diseased tissue, and prevents correction of underlying pathologic cell population disruptions. Here, we develop a high-resolution method of identifying phenotypically distinct progenitor cell subpopulations via single-cell transcriptional analysis and advanced bioinformatics. When combined with high-throughput cell surface marker screening, this approach facilitates the rational selection of surface markers for prospective isolation of cell subpopulations with desired transcriptional profiles. We establish the usefulness of this platform in costly and highly morbid diabetic wounds by identifying a subpopulation of progenitor cells that is dysfunctional in the diabetic state, and normalizes diabetic wound healing rates following allogeneic application. We believe this work presents a logical framework for the development of targeted cell therapies that can be customized to any clinical application.