Left Atrioventricular Coupling for Early Prediction of Incident Atrial Fibrillation

HomeRadiologyVol. 303, No. 2 PreviousNext Reviews and CommentaryFree AccessEditorialLeft Atrioventricular Coupling for Early Prediction of Incident Atrial FibrillationTim Leiner Tim Leiner Author AffiliationsFrom the Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55901.Address correspondence to the author (e-mail: [email protected]).Tim Leiner Published Online:Feb 22 2022https://doi.org/10.1148/radiol.213293MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In See also the article by Pezel et al in this issue.Tim Leiner, MD, PhD, EBCR, FISMRM, is a professor of radiology at Mayo Clinic and Utrecht University Medical Center in Utrecht, the Netherlands. He works on development and clinical implementation of advanced cardiovascular imaging techniques, including machine learning algorithms for earlier recognition and improved diagnosis of diseases of the heart and vessels.Download as PowerPointOpen in Image Viewer Anatomy is to physiology as geography is to history; it describes the theatre of events.–Jean François Fernel (1)Atrial fibrillation (AF) is the most common sustained heart rhythm disorder in adults and affects approximately 10 million individuals in the United States (2). The lifetime risk for AF in people of European ancestry at age 55 years is approximately 37%, and its prevalence is expected to rise to 12.1 million individuals in 2030 (3). Among Medicare patients aged 65 years or older, the prevalence of AF increased by approximately 5% annually, from 41.1 per 1000 beneficiaries in 1993 to 85.5 per 1000 beneficiaries in 2007 (4).Clinical sequelae of AF are common and include left ventricular (LV) dysfunction, heart failure, stroke, cognitive decline, vascular dementia, depression, and overall impaired quality of life. Patients with AF are frequently hospitalized, and care for patients with AF can be costly. Societal costs result not only from direct costs of care related to AF itself but also because of the associated complications of the disease, its treatment, and comorbid conditions, and societal costs of lost productivity. Total incremental costs for AF in the United States are estimated to range from $6 billion to $26 billion annually (5).Although the pathophysiologic cause of AF remains incompletely understood, there is substantial evidence that early intervention by modification of clinical risk factors and comorbidities can reduce the incidence and complications of AF. Early detection of AF matters, and early rhythm control with antiarrhythmic drugs and ablation therapy in patients with early AF aged older than 75 years or with a congestive heart failure, high blood pressure, patient age, diabetes, previous stroke or clot, vascular disease, age, and sex (or, CHA2DS2-VASc) score of at least 2 and preexisting cardiovascular conditions leads to a reduction in death, stroke, and cardiovascular events compared with heart rate control alone (6).Screening tools for AF include pulse palpation, readings from blood pressure monitors and electrocardiographic analysis, and smartphone and smartwatch apps. Although the widespread availability of these mobile health technologies provides excellent opportunities for early AF detection, many of these technologies have not been validated.Cardiac imaging is another potent tool that holds promise for early detection of AF. Advances in cardiac cross-sectional imaging have enabled routine and accurate assessment of cardiac chamber size over the cardiac cycle and the study of variations therein in relation to incident AF. For example, Abhayaratna et al (7) found reduced left atrium (LA) reservoir function to be an independent marker for future AF or atrial flutter. However, cardiac chamber function should not be viewed in isolation but rather as a complex and precisely orchestrated sequence of hemodynamic interactions between LA filling and emptying and LV filling and emptying. This process is often referred to as left atrioventricular mechanical coupling. Alterations in the interplay between these two processes are thought to be early precursors for AF.In this issue of Radiology, Pezel et al (8) describe an index of left atrioventricular coupling, the ratio of LA to LV end-diastolic volume (left atrioventricular coupling index [LACI]), and found that changes in this index over time independently predict future incident AF. Measurement of LACI requires conventional short-axis contour tracing of the LV to determine LV volume, and tracing of LA contours on left two-chamber and four-chamber cine images of the heart in combination with modeling to calculate LA volume. Both measurements are easy to automate by using deep learning techniques (9), and it is likely that the true three-dimensional volume of the LA over the cardiac cycle and (as a derivative measure) LACI can be determined in an accurate and fully automated fashion. The investigators used longitudinal cardiac MRI data from the Multi-Ethnic Study of Atherosclerosis, or MESA. They studied 1911 participants without cardiovascular disease at baseline and again after 10 years. Eighty-seven participants developed AF at an average of approximately 4 years after the second MRI examination (ie, an average of approximately 14 years after the first MRI examination). Impaired LACI and the change in LACI, expressed as greater LACI and greater average annualized change in LACI between the two MRI examinations, was found to be a strong and independent predictor of incident AF in two multivariable logistic regression models, considering a number of known AF predictors. LACI outperformed the Cohort for Heart and Aging Research in Genomic Epidemiology–Atrial Fibrillation, or CHARGE-AF, score (10) (a simple and widely used risk model to predict the risk of AF on the basis of variables readily available in primary care settings) and improved the prediction of incident AF.What is the clinical significance of these findings? AF is a disease with major societal impact, and early detection and treatment are effective at reducing AF-associated morbidity and mortality. The ability to reliably identify patients at increased risk for future AF is an opportunity to improve individual risk prediction and improve quality of life. The atrioventricular coupling index as described by Pezel et al is easy to measure in patients in whom cross-sectional imaging is available. Nevertheless, before widespread introduction of LACI in clinical practice, several additional steps are needed. First, LACI needs to be validated in external cohorts such as the UK Biobank or the German National Cohort, and preferably in other longitudinal health studies that incorporated cardiac MRI.Furthermore, as the investigators acknowledge, the domain of this study is healthy individuals without cardiovascular disease at baseline undergoing cardiac MRI. It remains to be investigated whether measurement of LACI from clinically indicated cardiac MRI studies can still independently predict future incident AF. A more rapid approach to widespread clinical application would be to develop methods to derive LACI from transthoracic echocardiography. Combined with advances in artificial intelligence analysis of the acquired images, it is conceivable that LACI can be measured during routine primary care outpatient office visits in a reliable and cost-effective manner.In conclusion, Pezel et al (8) are to be congratulated for showing the promise of LACI in identifying patients at increased risk for incident AF. Although it is unlikely that cardiac MRI-derived LACI will be able to reduce patient and societal burden of AF in the short term, echocardiography-derived LACI would be an excellent and likely cost-effective tool.Disclosures of Conflicts of Interest: T.L. grants or contracts from Dutch Technology Foundation, Netherlands Heart Foundation; royalties from Pie Medical; speakers bureau payments from Philips Healthcare, Bayer Healthcare; executive committee member and intermediate past president of the International Society for Magnetic Resonance in Medicine; shareholder, Quantib.References1. Fernel JF. De naturali parte medicinae. 1st ed. Andreas Wechel;1554. Google Scholar2. Miyasaka Y, Barnes ME, Gersh BJ, et al. Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. Circulation 2006;114(2):119–125. Crossref, Medline, Google Scholar3. Colilla S, Crow A, Petkun W, Singer DE, Simon T, Liu X. Estimates of current and future incidence and prevalence of atrial fibrillation in the U.S. adult population. Am J Cardiol 2013;112(8):1142–1147. Crossref, Medline, Google Scholar4. Piccini JP, Hammill BG, Sinner MF, et al. Incidence and prevalence of atrial fibrillation and associated mortality among Medicare beneficiaries, 1993-2007. Circ Cardiovasc Qual Outcomes 2012;5(1):85–93. Crossref, Medline, Google Scholar5. Kim MH, Johnston SS, Chu BC, Dalal MR, Schulman KL. Estimation of total incremental health care costs in patients with atrial fibrillation in the United States. Circ Cardiovasc Qual Outcomes 2011;4(3):313–320. Crossref, Medline, Google Scholar6. Kirchhof P, Camm AJ, Goette A, et al. Early Rhythm-Control Therapy in Patients with Atrial Fibrillation. N Engl J Med 2020;383(14):1305–1316. Crossref, Medline, Google Scholar7. Abhayaratna WP, Fatema K, Barnes ME, et al. Left atrial reservoir function as a potent marker for first atrial fibrillation or flutter in persons > or = 65 years of age. Am J Cardiol 2008;101(11):1626–1629. Crossref, Medline, Google Scholar8. Pezel T, Venkatesh BA, Quinaglia T, et al. Change in Left Atrioventricular Coupling Index to Predict Incident Atrial Fibrillation: The Multi-Ethnic Study of Atherosclerosis (MESA). Radiology 2022;303(2):317–326. Abstract, Google Scholar9. Leiner T, Rueckert D, Suinesiaputra A, et al. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson 2019;21(1):61. Crossref, Medline, Google Scholar10. Alonso A, Krijthe BP, Aspelund T, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J Am Heart Assoc 2013;2(2):e000102. Crossref, Medline, Google ScholarArticle HistoryReceived: Dec 28 2021Revision requested: Jan 12 2022Revision received: Jan 14 2022Accepted: Jan 21 2022Published online: Feb 22 2022Published in print: May 2022 FiguresReferencesRelatedDetailsAccompanying This ArticleChange in Left Atrioventricular Coupling Index to Predict Incident Atrial Fibrillation: The Multi-Ethnic Study of Atherosclerosis (MESA)Feb 22 2022RadiologyRecommended Articles Going beyond Cardiomegaly: Evaluation of Cardiac Chamber Enlargement at Non–Electrocardiographically Gated Multidetector CT: Current Techniques, Limitations, and Clinical ImplicationsRadiology: Cardiothoracic Imaging2019Volume: 1Issue: 1Change in Left Atrioventricular Coupling Index to Predict Incident Atrial Fibrillation: The Multi-Ethnic Study of Atherosclerosis (MESA)Radiology2022Volume: 303Issue: 2pp. 317-326Left Atrial Function Predicts Outcome in Dilated Cardiomyopathy: Fast Long-Axis Strain Analysis Derived from MRIRadiology2021Volume: 302Issue: 1pp. 72-81Comparison of Biplane Area-Length Method and 3D Volume Quantification by Using Cardiac MRI for Assessment of Left Atrial Volume in Atrial FibrillationRadiology: Cardiothoracic Imaging2023Volume: 5Issue: 2The Importance of Left Atrial Function after Myocardial InfarctionRadiology2020Volume: 296Issue: 2pp. 310-311See More RSNA Education Exhibits Assessment of Right Ventricular Function Using ECG-gated Computed Tomography: Simple & Reproducible techniquesDigital Posters2020Diastology With Cardiac MRI- A Practical GuideDigital Posters2021Don’t Skip a Beat! Critical Imaging Findings in Adults with Congenital Heart Disease Presenting to the Emergency DepartmentDigital Posters2019 RSNA Case Collection Penetrating intracardiac cement embolism RSNA Case Collection2021Acute pulmonary embolismRSNA Case Collection2022Breast edemaRSNA Case Collection2021 Vol. 303, No. 2 Metrics Altmetric Score PDF download