Abbas, Mohsin

Axillary artery intra-aortic balloon pump (axIABP) placement has been implemented as a bridging solution before heart transplantation. This study evaluates complications associated with axIABP support and describes an approach to minimize adverse events. We previously described a percutaneous approach for axIABP placement. However, patients receiving axIABP between September 1, 2017, and September 26, 2019 (n = 32) demonstrated a high rate of balloon pump malfunction (8/32; 25%) and other complications (totaling 15/32; 47%). Sixty-four patients were sequentially treated under a revised protocol. Compared to the initial cohort, no significant differences in demographics were noted. A significant reduction in rate of balloon malfunction (8/32, 25% vs. 1/64, 2%; p < 0.001) and total complications (15/32, 47% vs. 10/64, 16%; p = 0.0025) during the period of support were noted after intervention. Subsequent analysis of total complications per device size (40 vs. ≤ 34 ml balloon) revealed significantly reduced complications in patients with smaller devices (40% vs. 13%, respectively; p = 0.0022). This study provides guidelines to limit complications in patients supported with axIABP, facilitating a protracted period of bridging support.

Introduction: At the forefront of post-heart transplant care is early detection of transplant rejection. However, the current gold standard for detecting acute cellular transplant rejection is recurrent invasive endomyocardial biopsies (EMB). We aimed at developing a non-invasive biomarker for acute cellular heart transplant rejection (ACR) using a plasma extracellular vesicle (EV) platform. Methods: Peripheral and coronary sinus plasma samples were prospectively collected from 183 (31.7% female) patients from 2020 to 2023 during routine surveillance EMB. A discovery cohort comprised five patients who displayed ISHLT 0-1R biopsy followed by an ISHLT 2R grade rejection event. Plasma EVs were isolated from the baseline (0-1R) and rejection (2R) time points. Subsequently, integrative analysis of plasma EV proteomic, miRNomic, and metabolomics was performed pairwise with the samples. Results: The study collected 943 sample time points from 183 patients. Of these sample collections, 43 (4.5%) cases were 2R rejections. Our proteomic analysis of the plasma EVs identified 28 proteins found explicitly during a grade 2R rejection and 87 differentially expressed proteins during a 2R rejection event. An Ingenuity Pathway Analysis (IPA) analysis of the upregulated proteins highlighted EVs mediated mTOR signaling and antigen presentation as central to rejection events. Although all patients in the discovery cohort were negative for antibody-mediated rejection (AMR), the proteomic findings also revealed increased B-cell receptor signaling, implying a mechanistic intersection of ACR and AMR. Conclusions: A distinct set of proteins were found in plasma EVs during ACR events, providing potentially specific and sensitive biomarkers to reduce the need for EMB that could be exploited for ACR diagnosis. The proteins identified formed an immune signaling network that suggests a novel role for EVs in ACR pathogenesis.

Abstract Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78–0.90] and 95% (19/20; 95% CI: 75–100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61–0.96) and 100% (2/2; 95% CI: 16–100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.