Utility of an Artificial Intelligence Enabled Electrocardiogram for Risk Assessment in Liver Transplant Candidates

Himesh B. Zaver, Obaie Mzaik, Jonathan Thomas, Joanna Roopkumar, Demilade Adedinsewo, Andrew P. Keaveny, Tushar Patel

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Post-operative cardiac complications occur infrequently but contribute to mortality after liver transplantation (LT). Artificial intelligence-based algorithms based on electrocardiogram (AI-ECG) are attractive for use during pre-operative evaluation to screen for risk of post-operative cardiac complications, but their use for this purpose is unknown. Aims: The aim of this study was to evaluate the performance of an AI-ECG algorithm in predicting cardiac factors such as asymptomatic left ventricular systolic dysfunction or potential for developing post-operative atrial fibrillation (AF) in cohorts of patients with end-stage liver disease either undergoing evaluation for transplant or receiving a liver transplant. Methods: A retrospective study was performed in two consecutive adult cohorts of patients who were either evaluated for LT or underwent LT at a single center between 2017 and 2019. ECG were analyzed using an AI-ECG trained to recognize patterns from a standard 12-lead ECG which could identify the presence of left ventricular systolic dysfunction (LVEF < 50%) or subsequent atrial fibrillation. Results: The performance of AI-ECG in patients undergoing LT evaluation is similar to that in a general population but was lower in the presence of prolonged QTc. AI-ECG analysis on ECG in sinus rhythm had an AUROC of 0.69 for prediction of de novo post-transplant AF. Although post-transplant cardiac dysfunction occurred in only 2.3% of patients in the study cohorts, AI-ECG had an AUROC of 0.69 for prediction of subsequent low left ventricular ejection fraction. Conclusions: A positive screen for low EF or AF on AI-ECG can alert to risk of post-operative cardiac dysfunction or predict new onset atrial fibrillation after LT. The use of an AI-ECG can be a useful adjunct in persons undergoing transplant evaluation that can be readily implemented in clinical practice. Graphical Abstract: [Figure not available: see fulltext.].

Original languageEnglish (US)
Pages (from-to)2379-2388
Number of pages10
JournalDigestive diseases and sciences
Volume68
Issue number6
DOIs
StatePublished - Jun 2023

Keywords

  • Artificial Intelligence enabled electrocardiogram
  • Artificial intelligence
  • Convolutional neural network
  • End stage liver disease
  • Liver transplantation
  • Receiver operator characteristic

ASJC Scopus subject areas

  • Physiology
  • Gastroenterology

Fingerprint

Dive into the research topics of 'Utility of an Artificial Intelligence Enabled Electrocardiogram for Risk Assessment in Liver Transplant Candidates'. Together they form a unique fingerprint.

Cite this