Journal of Cardiac Failure最新文献

Introduction: Patients with heart failure, especially those with lower socioeconomic position, are vulnerable to adverse outcomes of care fragmentation. Care coordination mitigates care fragmentation, but a comprehensive assessment of relationships among all relevant clinicians that enable it is lacking. This explanatory sequential mixed-methods study explores how patients' clinician network characteristics relate to their social context and clinical outcomes following heart failure care transitions.

Methods: We conducted a bipartite social network analysis to describe clinician networks for 1,269 patients first hospitalized with heart failure in a southeastern U.S. health system. Networks were constructed using electronic health record notes one year before, during, and one year after the index hospitalization (January 2015- February 2020). We then used stratified purposive sampling to select 11 adults with diverse socio-economic positions but similar illness severity and comorbidity. For these patients, we conducted qualitative chart reviews of clinical notes. Each patient's clinician network size, density, and centrality were integrated with qualitative findings to explore clinician networks' relationship to patients' social context and outcomes.

Results: Patients with higher socio-economic positions used fewer acute care services and lived longer; their clinicians, particularly outpatient clinicians, tended to have denser and more centrally located team networks before the index hospitalization that persisted after the index hospitalization; their telephone notes indicated more regular and reciprocal communication patterns between patients and clinicians.

Conclusions: Early involvement and better communication among clinician networks with greater density and centrality may help to explain better care transition outcomes observed among patients with higher socio-economic positions.

Heart failure care has advanced, yet outcomes remain inconsistent and clinical deterioration is still recognized too late. Traditional artificial intelligence has evolved from rule-based, predictive, to generative and generally operates at the population level. Thus, it cannot keep pace with the rapidly shifting physiologic states that define heart failure. Digital twin technology offers a decisive shift: a continuously calibrated, mechanistically grounded computational replica of an individual patient that integrates multimodal physiologic, imaging, clinical, molecular, and behavioral data. Anchored in physics-informed models and paired with advanced artificial intelligence layers, the digital twin functions as a real-time simulator rather than a static predictor. It enables 4 transformative applications: early physiologic instability forecasting, virtual comparator control arms for pragmatic trials, mechanism-anchored phenotyping, and system-level resource optimization. If built with rigor, equity, and transparent validation, digital twins can transition heart failure care from reactive management to anticipatory, individualized, and mechanistically informed decision-making, bringing long-sought precision to this complex syndrome. Yet, this staged translation will require responsible, evidence-based implementation and precise recognition of limitations.

Purpose: Donation after circulatory death (DCD) as a donor for heart transplantation is a new practice. We sought to determine if center volume (high vs low) influenced perioperative outcomes and short-term survival.

Methods: The United Network for Organ Sharing registry was used to identify DCD heart recipients from 2019 to 2025. Recipients were stratified based on center volume: high-volume (>15 transplants over the observational period from 2019 to 2020) or low volume (≤15 transplants). Comparative statistics and Kaplan-Meier methods with the log-rank test were used to compare groups and determine survival. A Cox regression analysis using select donor and recipient criteria was created to determine the association of center volume and mortality.

Results: Low-volume group recipients had significantly longer waitlist times, temporary mechanical circulatory support use, and were more likely to be status 1 or 2 compared to the high-volume group. However, high-volume centers had significantly higher exceptions granted per center compared to low-volume centers. There were no significant differences in perioperative outcomes or short-term survival. Following adjustment, center volume was not independently associated with increased mortality, while increasing ischemic time was.

Conclusions: Transplant center volume had no significant effect on perioperative outcomes or survival after DCD heart transplantation. The discrepancy in presumed medical urgency and increased waitlist time in the low-volume cohort is highlighted by significantly more exceptions granted to high-volume recipients. This should be a noted area for improvement in the new allocation system to make heart transplantation more equitable for all recipients.