IMPORTANCE Unlike warfarin, which requires routine laboratory testing and dose adjustment, target-specific oral anticoagulants like dabigatran do not. However, optimal follow-up infrastructure and modifiable site-level factors associated with improved adherence to dabigatran are unknown. OBJECTIVES To assess site-level variation in dabigatran adherence and to identify site-level practices associated with higher dabigatran adherence. DESIGN, SETTING, AND PARTICIPANTS Mixed-methods study involving retrospective quantitative and cross-sectional qualitative data. A total of 67 Veterans Health Administration sites with 20 or more patients filling dabigatran prescriptions between 2010 and 2012 for nonvalvular atrial fibrillation were sampled (4863 total patients; median, 51 patients per site). Forty-seven pharmacists from 41 eligible sites participated in the qualitative inquiry. EXPOSURE Site-level practices identified included appropriate patient selection, pharmacist-driven patient education, and pharmacist-led adverse event and adherence monitoring. MAIN OUTCOMES AND MEASURES Dabigatran adherence (intensity of drug use during therapy) defined by proportion of days covered (ratio of days supplied by prescription to follow-up duration) of 80% or more. RESULTS The median proportion of patients adherent to dabigatran was 74% (interquartile range [IQR], 66%-80%). After multivariable adjustment, dabigatran adherence across sites varied by a median odds ratio of 1.57. Review of practices across participating sites showed that appropriate patient selection was performed at 31 sites, pharmacist-led education was provided at 30 sites, and pharmacist-led monitoring at 28 sites. The proportion of adherent patients was higher at sites performing appropriate selection (75% vs 69%), education (76% vs 66%), and monitoring (77% vs 65%). Following multivariable adjustment, association between pharmacist-led education and dabigatran adherence was not statistically significant (relative risk [RR], 0.94; 95% CI, 0.83-1.06). Appropriate patient selection (RR, 1.14; 95% CI, 1.05-1.25), and provision of pharmacist-led monitoring (RR, 1.25; 95% CI, 1.11-1.41) were associated with better patient adherence. Additionally, longer duration of monitoring and providing more intensive care to nonadherent patients in collaboration with the clinician improved adherence. CONCLUSIONS AND RELEVANCE Among nonvalvular atrial fibrillation patients treated with dabigatran, there was variability in patient medication adherence across Veterans Health Administration sites. Specific pharmacist-based activities were associated with greater patient adherence to dabigatran.

National implementation of VA-ECHO was positively associated with hepatitis C treatment initiation by primary care providers, without differences in sustained virologic response.

Amyloid precursor protein (APP), a transmembrane glycoprotein, is well known for its involvement in the pathogenesis of Alzheimer disease of the aging brain, but its normal function is unclear. APP is a prominent component of the adult as well as the developing brain. It is enriched in axonal growth cones (GCs) and has been implicated in cell adhesion and motility. We tested the hypothesis that APP is an extracellular matrix adhesion molecule in experiments that isolated the function of APP from that of well-established adhesion molecules. To this end we plated wild-type, APP-, or β1-integrin (Itgb1)- misexpressing mouse hippocampal neurons on matrices of either laminin, recombinant L1, or synthetic peptides binding specifically to Itgb1 s or APP. We measured GC adhesion, initial axonal outgrowth, and substrate preference on alternating matrix stripes and made the following observations: Substrates of APP-binding peptide alone sustain neurite outgrowth; APP dosage controls GC adhesion to laminin and APP-binding peptide as well as axonal outgrowth in Itgb1− independent manner; and APP directs GCs in contact guidance assays. It follows that APP is an independently operating cell adhesion molecule that affects the GC's phenotype on APP-binding matrices including laminin, and that it is likely to affect axon pathfinding in vivo.

BACKGROUND: Lack of healthcare access to due to physician shortages is a significant driver of telemedicine expansion in rural areas. Telemedicine is effective for management of chronic conditions such as diabetes but its effectiveness in primary care settings is unknown. OBJECTIVE: To evaluate differences in diabetes care before and after implementation of a longitudinal virtual primary care program. DESIGN: Propensity score-matched cohort study utilizing difference-in-differences analysis. PARTICIPANTS: Patients with diabetes who received care at VA primary care clinics between January 2018 and December 2019 where the Virtual Integrated Multisite Patient Aligned Care Teams (V-IMPACT) program was implemented. EXPOSURE: Patient participation in at least one V-IMPACT visit while usual care patients did not participate in V-IMPACT. MAIN MEASURES: The primary outcome was change in hemoglobin A1C (HbA1C) and secondary outcomes included change in the proportion of patients meeting diabetes quality indicators: blood pressure control, statin use, angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (ACEi/ARB) use, and annual microalbuminuria testing. KEY RESULTS: Our propensity-matched cohort included 9010 patients split evenly between those who participated in V-IMPACT and those who remained in usual in-person care. Among individuals with diabetes who participated in V-IMPACT, the change in mean HbA1C was − 0.055% (95% CI − 0.088 to − 0.022%) while those in usual care had a − 0.047% (95% CI − 0.080 to − 0.014%) change before and after program implementation. We observed a 5.1% (95% CI 2.4 to 7.7%) absolute increase in the proportion prescribed statins in the V-IMPACT group, a 5.3% (95% CI 2.5 to 8.2%) increase prescribed ACE/ARBs, and a 4.6% (95% 1.7 to 7.5%) increase in completed yearly microalbuminuria testing. V-IMPACT was not associated with a significant difference in the proportion with controlled blood pressure at < 140/90 or < 130/90 mmHg thresholds. CONCLUSIONS: Quality of diabetes care delivered by a longitudinal virtual primary care model was similar if not better than traditional in-person care.

Nonindex readmissions are common and associated with worse outcomes; the common findings across cohorts highlight the importance for hospitals and care systems participating in value-based payment models. Hospitals and care systems should invest in improved methods for real-time identification and intervention for these patients.

While black patients had a higher rate of mortality than white patients in unadjusted analyses, race was not independently associated with 1-year mortality among patients undergoing PCI in VA hospitals.

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BackgroundMultilevel models for non-normal outcomes are widely used in medical and health sciences research. While methods for interpreting fixed effects are well-developed, methods to quantify and interpret random cluster variation and compare it with other sources of variation are less established. Random cluster variation, sometimes referred to as general contextual effects (GCE), may be the main focus of a study; therefore, easily interpretable methods are needed to quantify GCE. We propose a Reference Effect Measure (REM) approach to 1) quantify GCE and compare it to individual subject and cluster covariate effects, and 2) quantify relative magnitudes of GCE and variation from sets of measured factors.MethodsTo illustrate REM, we consider a two-level mixed logistic model with patients clustered within hospitals and a random intercept for hospitals. We compare patients at hospitals at given percentiles of the estimated random effect distribution to patients at a median or ‘reference’ hospital. These estimates are then compared numerically and graphically to individual fixed effects to quantify GCE in the context of effects of other measured variables (aim 1). We then extend this approach by comparing variation from the random effect distribution to variation from sets of fixed effects to understand their magnitudes relative to overall outcome variation (aim 2).ResultsUsing an example of initiation of rhythm control treatment in atrial fibrillation (AF) patients within the Veterans Affairs (VA), we use REM to demonstrate that random variation across hospitals (GCE) in initiation of treatment is substantially greater than that due to most individual patient factors, and explains at least as much variation in treatment initiation as do all patient factors combined. These results are contrasted with a relatively small GCE compared with patient factors in 1 year mortality following hospitalization for AF patients.ConclusionsREM provides a means of quantifying random effect variation (GCE) with multilevel data and can be used to explore drivers of outcome variation. This method is easily interpretable and can be presented visually. REM offers a simple, interpretable approach for evaluating questions of growing importance in the study of health care systems.