Cardiac allograft vasculopathy in heart transplant recipients from hepatitis C viremic donors.

BACKGROUND: Recent studies suggest the transplantation of Hepatitis C (HCV) hearts from viremic donors is associated with comparable 1 year survival to nonviremic donors. Though HCV viremia is a known risk factor for accelerated atherosclerosis, data on cardiac allograft vasculopathy (CAV) outcomes are limited. We compared the incidence of CAV in heart transplant recipients from HCV viremic donors (nucleic acid amplification test positive; NAT+) compared to non-HCV infected donors (NAT-). METHODS: We retrospectively reviewed annual coronary angiograms with intravascular ultrasound from April 2017 to August 2020 at two large cardiac transplant centers. CAV was graded according to ISHLT guidelines. Maximal intimal thickness (MIT) ≥ 0.5 mm was considered significant for subclinical disease. RESULTS: Among 270 heart transplant recipients (mean age 54; 77% male), 62 patients were transplanted from NAT+ donors. CAV ≥ grade 1 was present in 8.8% of the NAT+ versus 16.8% of the NAT- group at 1 year, 20% versus 28.8% at 2 years, and 33.3% versus 41.5% at 3 years. After adjusting for donor age, donor smoking history, recipient BMI, recipient, hypertension, and recipient diabetes, NAT+ status did not confer increased risk of CAV (HR.80; 95% CI.45-1.40, p = 0.43) or subclinical IVUS disease (HR.87; 95% CI.58-1.30, p = 0.49). Additionally, there was no difference in the presence of rapidly progressive lesions on IVUS. CONCLUSION: Our data show that NAT+ donors conferred no increased risk for early CAV or subclinical IVUS disease following transplantation in a cohort of heart transplant patients who were treated for HCV, suggesting the short-term safety of this strategy to maximize the pool of available donor hearts.

Precision Genetic Therapies: Balancing Risk and Benefit in Patients with Heart Failure.

PURPOSE OF REVIEW: Precision genetic medicine is evolving at a rapid pace and bears significant implications for clinical cardiology. Herein, we discuss the latest advancements and emerging strategies in gene therapy for cardiomyopathy and heart failure. RECENT FINDINGS: Elucidating the genetic architecture of heart failure has paved the way for precision therapies in cardiovascular medicine. Recent preclinical studies and early-phase clinical trials have demonstrated encouraging results that support the development of gene therapies for heart failure arising from a variety of etiologies. In addition to the discovery of new therapeutic targets, innovative delivery platforms are being leveraged to improve the safety and efficacy of cardiac gene therapies. Precision genetic therapy represents a potentially safe and effective approach for improving outcomes in patients with heart failure. It holds promise for radically transforming the treatment paradigm for heart failure by directly targeting the underlying etiology. As this new generation of cardiovascular medicines progress to the clinic, it is especially important to carefully evaluate the benefits and risks for patients.

Incidence of Acute Rejection Compared With Endomyocardial Biopsy Complications for Heart Transplant Patients in the Contemporary Era.

BACKGROUND: The reference standard of detecting acute rejection (AR) in adult heart transplant (HTx) patients is an endomyocardial biopsy (EMB). The majority of EMBs are performed in asymptomatic patients. However, the incidence of treated AR compared with EMB complications has not been compared in the contemporary era (2010-current). METHODS: The authors retrospectively analyzed 2769 EMBs obtained in 326 consecutive HTx patients between August 2019 and August 2022. Variables included surveillance versus for-cause indication, recipient and donor characteristics, EMB procedural data and pathological grades, treatment for AR, and clinical outcomes. RESULTS: The overall EMB complications rate was 1.6%. EMBs performed within 1 mo after HTx compared with after 1 mo from HTx showed significantly increased complications (OR, 12.74, P < 0.001). The treated AR rate was 14.2% in the for-cause EMBs and 1.2% in the surveillance EMBs. We found the incidence of AR versus EMB complications was significantly lower in the surveillance compared with the for-cause EMB group (OR, 0.05, P < 0.001). We also found the incidence of EMB complications was higher than treated AR in surveillance EMBs. CONCLUSIONS: The yield of surveillance EMBs has declined in the contemporary era, with a higher incidence of EMB complications compared with detected AR. The risk of EMB complications was highest within 1 mo after HTx. Surveillance EMB protocols in the contemporary era may need to be reevaluated.

Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

BACKGROUND: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems. OBJECTIVE: To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score. DESIGN: Retrospective, cohort study. PARTICIPANTS: Data from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19. MAIN MEASURES: One-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically. KEY RESULTS: Compared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69-0.73), 0.71 (0.69-0.72), and 0.71 (95% CI 0.70-0.73), respectively. All three scores showed good calibration across the full risk spectrum. CONCLUSIONS: These findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist.