Narula, Tathagat

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.

Abstract Background We have previously shown bone marrow-derived mesenchymal stem cells (MSCs) may shift immune responses toward anti-inflammatory pathways and stabilize the course of obstructive chronic lung allograft syndrome (o-CLAD) after lung transplantation. In this study, we measured the response of lower dose infusions. Methods We infused low-dose MSCs intravenously in 13 patients who had developed moderate-to-severe o-CLAD. Three had previously received an infusion of MSCs from a different donor and were re-dosed at 1 × 106 MSC/kg, while 5 received a first dose at 1 × 106 MSC/kg and five received an even lower dose at 0.5 × 106 MSC/kg. We recorded pulmonary function tests before and after infusion, and patients were followed clinically for 12 months. Results Infusions were well tolerated, and no significant adverse events were recorded in the first 30 days. There was significant decline (mean ± SD) in forced vital capacity (FVC) (3.49 ± 1.03 vs 3.18 ± 0.94 L, P = .03) and forced expiratory volume in 1 second (FEV1) (2.28 ± 0.86 vs 1.77 ± 0.49 L, P = .04) over the year preceding infusion. FVC (3.18 ± 0.94 vs 3.46 ± 0.99 L, P = .53) and FEV1 was not significantly changed (1.77 ± 0.49 vs 1.88 ± 0.75, P = .72) when comparing values immediately prior to infusion to those obtained 1 year after infusion, indicating a possible stabilizing effect on lung function decline due to o-CLAD. Conclusion Intravenous infusions of bone marrow-derived MSCs are well tolerated in lung transplant recipients with moderate-to-severe CLAD. Low-dose MSCs appear to slow progression of CLAD in some patients.