ALBERT

Description: Although survival and quality of life have improved in patients with advanced heart failure (HF) after implantation of left ventricular assist devices (LVADs), they still pose risks of hemocompatibility-related complications, including thrombosis and bleeding. Development of biomarkers predictive of these LVAD-associated complications could guide decision making for both clinicians and patients. Recently, we showed higher plasma TGF-β1 levels within one-week after implantation with a miniaturized mechanical-bearing axial-flow pump HeartMate II (HM-II), and reasoned that platelet activation by the rotor may have caused the release of TGF-β1 in plasma in HF patients (Mancini et al. Transl. Res. 2018; 192:15-29). Recent clinical trials with the newest LVAD, the Heartmate 3 (HM-3), which uses a fully magnetically-levitated pump, showed superior clinical outcomes, including significantly reduced incidences of pump thrombosis and stroke (Mehra et al. N Engl J Med. 2019; 380:1618-1627). In this study, we evaluated release of TGF-β1 in plasma following implantation of HM-II and HM-3 LVADs compared to either coronary artery bypass graft (CABG) surgery or extracorporeal membrane oxygenation (ECMO) therapy. We measured serial total TGF-β1 levels in 38 Stage-D HF patients (11 received HM-II and 27 received HM-3). As a control, we collected blood samples from 10 patients undergoing CABG surgery, and 10 patients receiving ECMO therapy following acute onset cardio-pulmonary failure. Blood samples were collected before and 4-8 hours after procedures, and thereafter daily for up to one week. Plasma was prepared by centrifuging blood at 12,000 rpm for 5 min at 4°C within 10 min of blood drawing, which reduces in vitro release of TGF-β1 from platelets and thus allows accurate measurement of plasma TGF-β1. Total TGF-β1 levels were measured after acidification and neutralization of samples using DUO-ELISA kit (R&D Systems). Baseline total plasma TGF-β1 levels were higher in HF patients before LVAD implantation than in healthy controls [4.7 ± 1.9 ng/mL in HF patients (n= 38); 3.3 ± 0.8 ng/mL in healthy controls (n= 6); p=0.006)]. Total TGF-β1 levels surged transiently to 14.6 ± 6.1 ng/mL within 4-8 hours after LVAD implantation [(p

Description: Background A central goal among researchers and policy makers seeking to implement clinical interventions is to identify key facilitators and barriers that contribute to implementation success. Despite calls from a number of scholars, empirical insights into the complex structural and cultural predictors of why decision aids (DAs) become routinely embedded in health care settings remains limited and highly variable across implementation contexts. Methods We examined associations between “reach”, a widely used indicator (from the RE-AIM model) of implementation success, and multi-level site characteristics of nine LVAD clinics engaged over 18 months in implementation and dissemination of a decision aid for left ventricular assist device (LVAD) treatment. Based on data collected from nurse coordinators, we explored factors at the level of the organization (e.g. patient volume), patient population (e.g. health literacy; average sickness level), clinician characteristics (e.g. attitudes towards decision aid; readiness for change) and process (how the aid was administered). We generated descriptive statistics for each site and calculated zero-order correlations (Pearson’s r) between all multi-level site variables including cumulative reach at 12 months and 18 months for all sites. We used principal components analysis (PCA) to examine any latent factors governing relationships between and among all site characteristics, including reach. Results We observed strongest inclines in reach of our decision aid across the first year, with uptake fluctuating over the second year. Average reach across sites was 63% (s.d. = 19.56) at 12 months and 66% (s.d. = 19.39) at 18 months. Our PCA revealed that site characteristics positively associated with reach on two distinct dimensions, including a first dimension reflecting greater organizational infrastructure and standardization (characteristic of larger, more established clinics) and a second dimension reflecting positive attitudinal orientations, specifically, openness and capacity to give and receive decision support among coordinators and patients. Conclusions Successful implementation plans should incorporate specific efforts to promote supportive and mutually informative interactions between clinical staff members and to institute systematic and standardized protocols to enhance the availability, convenience and salience of intervention tool in routine practice. Further research is needed to understand whether “core predictors” of success vary across different intervention types.