Decoding Silent Signals: A Systematic Review of ECG and the Quest for an Early Diagnosis of Cirrhotic Cardiomyopathy.

Abstract

Cirrhotic Cardiomyopathy (CCM) is a well-known cardiac complication with a high incidence of underdiagnosis by healthcare systems in patients with advanced liver disease. While most of the manifestations of cirrhosis stay within the liver function affected, evidence suggests systemic pathology with myocardial dysfunction. Noninvasive ECG markers of potential CCM include QT interval prolongation, QRS hyper-voltage, and T-wave abnormalities. However, the relationship between ECG abnormalities, structural and functional changes in the heart has not been fully described. This systematic review follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting guidelines, providing a literature search of ECG changes and evaluation in patients with cirrhosis to establish their diagnostic and predictive value. A structured search of major literature databases showed eight high-quality studies that met the criteria for inclusion in the review. QT prolongation has been associated with the severity of the liver disease, as represented by the clinical scoring system. Changes to the myocardial structure lead to the modification of QRS voltage levels and T-wave configuration, as well as increased arrhythmia risk. Variable sensitivity and specificity were found in diagnosing ventricular function with different echocardiographic parameters, leaving the need for multiple types of diagnostics to provide an accurate evaluation of ventricular function. Identification of ECG abnormalities in different stages of CCM is needed for an early detection system that will result in a higher quality of patient care. Evaluating ECG markers, combined with echocardiography data, N-terminal pro-B-type natriuretic peptide (NT-proBNP), and cardiac troponin biomarkers, leads to improved CCM diagnosis and risk stratification for liver transplant patients. Longitudinal research combined with AI-assisted ECG should be the primary focus in future research for standardization of CCM diagnosis and clinical care.