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

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.

Keywords: Artificial intelligence; Cardiac allograft rejection; Deep learning; Electrocardiography; Heart transplantation.

Graphical abstract

An artificial intelligence enabled ECG can effectively detect cardiac allograft rejection among heart transplant recipients. AI, artificial intelligence; AUC, area under the receiver operating characteristic curve; ECG, electrocardiogram; ISHLT, International Society for Heart and Lung Transplantation; NPV, negative predictive value; PPV, positive predictive value.

Figure 1

Study flow diagram.

Figure 2

Artificial intelligence electrocardiogram—model architecture.

Figure 3

Receiver operating characteristic curve for detection of cardiac allograft rejection in a retrospective cohort of heart transplant recipients using a deep-learning model.

Figure 4

Subgroup analysis showing deep learning model performance in a retrospective cohort of heart transplant recipients.

Figure 5

(A) Twelve-lead electrocardiogram obtained prior to rejection episode. (B) Twelve-lead electrocardiogram obtained during the rejection episode. (C) Deep learning prediction probabilities for a single patient over time—prior to, during an episode of, and following recovery from moderate acute cellular rejection. The grey-shaded area (corresponding to values less than 0.4) highlights prediction probabilities below the threshold for a positive screen.

Figure 6

Receiver operating characteristic curve for detection of cardiac allograft rejection in a prospective cohort of heart transplant recipients using a deep-learning model.