Kidney transplantation on extracorporeal life support for primary cardiac allograft dysfunction.

Patients undergoing heart-kidney transplants who have primary graft dysfunction (PGD) of the heart are at risk of losing both organs, which may cause reluctance on the part of the transplant team to proceed with transplanting the kidney while the transplanted heart is being supported by mechanical device. We describe a case series in which 2 patients received kidney transplants while on veno-arterial ECMO support for PGD after heart transplant. Both patients are alive more than 1 year following transplant, with good cardiac and renal function and no signs of cardiac rejection. Kidney transplant surgery is safe for patients on veno-arterial ECMO support for cardiac PGD. It allows the heart recipient to receive a kidney from the same donor with both immunologic and survival advantages.

Donation after circulatory death: A transplant cardiologist's take on neuroprognostication.

Donation after circulatory death (DCD) is becoming increasingly utilized in heart transplantation and has the potential to further expand the donor pool. As transplant cardiologists gain more familiarity with DCD donor selection, there are many issues that lack consensus including how we incorporate the neurologic examination, how we measure functional warm ischemic time (fWIT), and what fWIT thresholds are acceptable. DCD donor selection calls for prognostication tools to help determine how quickly a donor may expire, and in current practice there is no standardization in how we make these predictions. Current scoring systems help to determine which donor may expire within a specified time window either require the temporary disconnection of ventilatory support or do not incorporate any neurologic examination or imaging. Moreover, the specified time windows differ from other DCD solid organ transplantation without standardization or strong scientific justification for these thresholds. In this perspective, we highlight the challenges faced by transplant cardiologists as they navigate the muddy waters of neuroprognostication in DCD cardiac donation. Given these difficulties, this is also a call to action for the creation of a more standardized approach to improve the DCD donor selection process for appropriate resource allocation and organ utilization.

Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model.

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.