Usefulness of Left Atrial Volume in Predicting First Congestive Heart Failure in Patients ≥65 Years of Age With Well-Preserved Left Ventricular Systolic Function

doi:10.1016/j.amjcard.2005.05.031

The American Journal of Cardiology

Volume 96, Issue 6, 15 September 2005, Pages 832-836

https://doi.org/10.1016/j.amjcard.2005.05.031 Get rights and content

Left atrial (LA) volume is a barometer of diastolic dysfunction. Whether it predicts congestive heart failure (CHF) in patients with preserved left ventricular (LV) systolic function is not known. Olmsted County, Minnesota, residents aged ≥65 years referred for transthoracic echocardiography from 1990 to 1998, who were in sinus rhythm without a history of CHF were followed in the medical records to 2003 (mean follow-up duration 4.3 ± 2.7 years). Of the 1,495 patients identified, 1,375 (92%) with LV ejection fractions ≥50% (mean age 75 ± 7 years; 59% women) constituted the study population, 138 (10%) of whom developed CHF. Baseline LA volume ≥32 ml/m² was an independent predictor of first CHF (p <0.001). Of the 138 patients who had first CHF, ejection fractions were assessed within 4 weeks of diagnosis in 98 subjects, 74 (76%) of whom had ejection fractions remaining at ≥50%, with a mean increase in LA volume of 8 ± 10 ml/m² (p <0.001) from baseline. The age-adjusted CHF-free survival rates for LA volume tertiles (<28, 28 to ≤37, and >37 ml/m²) were 95%, 91%, and 83%, respectively (p <0.001). In conclusion, LA volume independently predicted first CHF in an elderly cohort with well-preserved LV systolic function.

Section snippets

Study design and population

After obtaining study approval from the Mayo Foundation Institutional Review Board, potential subjects were identified from a computerized search of the echocardiographic database (after the exclusion of patients who had not provided authorization for the use of their clinical data in research). Search criteria were residence in Olmsted County, Minnesota; a record of ≥1 transthoracic echocardiogram performed at the Mayo Clinic, the Olmsted Medical Center, or their affiliated hospitals from

Results

We identified 1,495 Olmsted County residents (aged ≥65 years without a history of CHF, atrial fibrillation, stroke, valvular heart disease, congenital heart disease, or pacemaker implantation) who were referred for ≥1 echocardiographic study from 1990 to 1998. Of these patients, 1,375 (92%) had LV ejection fractions ≥50% at baseline and constituted the study population (811 women and 564 men). The mean age of the cohort was 75 ± 7 years. The referral indications included dyspnea and/or chest

Discussion

In this study involving an elderly cohort with well-preserved LV systolic function referred for echocardiographic study, a considerable proportion (10%) developed first CHF within 5 years of follow-up. The incidence rate of CHF was slightly higher than that reported for the participants in the Cardiovascular Health Study (also aged ≥65 years),⁹ probably because of the referral-based nature of our study population. In most patients who developed CHF in our study, systolic function remained

References (15)

L.J. Melton
History of the Rochester Epidemiology Project
Mayo Clin Proc
(1996)
J.-F. Ren et al.
Two-dimensional echocardiographic determination of left atrial emptying volumea noninvasive index in quantifying the degree of nonrheumatic mitral regurgitation
J Am Coll Cardiol
(1983)
T.S.M. Tsang et al.
Left ventricular diastolic dysfunction as a predictor of the first diagnosed nonvalvular atrial fibrillation in 840 elderly men and women
J Am Coll Cardiol
(2002)
R. Devereux et al.
Echocardiographic assessment of left ventricular hypertrophycomparison to necropsy findings
Am J Cardiol
(1986)
J. Gottdiener et al.
Predictors of congestive heart failure in the elderlythe Cardiovascular Health Study
J Am Coll Cardiol
(2000)
T. Tsang et al.
Prediction of risk for first age-related cardiovascular events in an elderly populationthe incremental value of echocardiography
J Am Coll Cardiol
(2003)
T.S.M. Tsang et al.
Risks for atrial fibrillation and congestive heart failure in patients ≥65 years of age with abnormal left ventricular diastolic relaxation
Am J Cardiol
(2004)

There are more references available in the full text version of this article.

Cited by (279)

Comparing Artificial Intelligence-Enabled Electrocardiogram Models in Identifying Left Atrium Enlargement and Long-term Cardiovascular Risk
2024, Canadian Journal of Cardiology
The role of P-wave in identifying left atrial enlargement (LAE) with the use of artificial intelligence (AI)–enabled electrocardiography (ECG) models is unclear. It is also unknown if AI-enabled single-lead ECG could be used as a diagnostic tool for LAE surveillance. We aimed to build AI-enabled P-wave and single-lead ECG models to identify LAE using sinus rhythm (SR) and non-SR ECGs, and compare the prognostic ability of severe LAE, defined as left atrial diameter ≥ 50 mm, assessed by AI-enabled ECG models vs echocardiography.
This retrospective study used data from 382,594 consecutive adults with paired 12-lead ECG and echocardiography performed within 2 weeks of each other at Chang Gung Memorial Hospital. UNet++ was used for P-wave segmentation. ResNet-18 was used to develop deep convolutional neural network–enabled ECG models for discriminating LAE. External validation was performed with the use of data from 11,753 patients from another hospital.
The AI-enabled 12-lead ECG model outperformed other ECG models for classifying LAE, but the single-lead ECG models also showed excellent performance at a left atrial diameter cutoff of 50 mm. AI-enabled ECG models had excellent and fair discrimination on LAE using the SR and the non-SR data set, respectively. Severe LAE identified by AI-enabled ECG models was more predictive of future cardiovascular disease than echocardiography; however, the cumulative incidence of new-onset atrial fibrillation and heart failure was higher in patients with echocardiography-severe LAE than with AI-enabled ECG-severe LAE.
P-Wave plays a crucial role in discriminating LAE in AI-enabled ECG models. AI-enabled ECG models outperform echocardiography in predicting new-onset cardiovascular diseases associated with severe LAE.
Le rôle de l’onde P dans la détection de la dilatation auriculaire gauche (DAG) à l’aide des modèles d’électrocardiographie (ECG) basés sur l’intelligence artificielle (IA) est incertain. On ne sait pas non plus si l’EGC à dérivation unique basé sur l’IA pourrait servir d’outil de diagnostic pour la surveillance de la DAG. Nous avions pour objectif de créer des modèles d’ECG à onde P basés sur l’IA et d’ECG à dérivation unique pour détecter la DAG au moyen d’ECG en rythme sinusal (RS) et en non-RS, et de comparer la capacité pronostique de la DAG grave, défini par un diamètre auriculaire gauche ≥ 50 mm, évalué par les modèles d’ECG basé sur l’IA vs l’ECG.
La présente étude rétrospective était fondée sur les données de 382 594 adultes consécutifs ayant subi un ECG à 12 dérivations et une échocardiographie réalisées à 2 semaines d’intervalle au Chang Gung Memorial Hospital. Le UNet++ a été utilisé pour segmenter l’onde P. Le ResNet-18 a été utilisé pour élaborer les modèles d’ECG basés sur le réseau de neurones profonds à convolution pour discriminer la DAG. La validation externe a été réalisée à partir des données de 11 753 patients d’un autre hôpital.
Le modèle d’ECG à 12 dérivations basé sur l’IA a surpassé les autres modèles d’ECG dans la classification de la DAG, mais les modèles d’ECG à dérivation unique ont aussi montré une excellente performance à un seuil de diamètre auriculaire gauche de 50 mm. Les modèles d’ECG basés sur l’IA ont montré une discrimination excellente et équitable de la DAG selon l’ensemble des données RS et non-RS, et ce respectivement. La DAG grave détectée par les modèles d’ECG basés sur l’IA était plus prédictive des maladies cardiovasculaires futures que l’ECG. Toutefois, l’incidence cumulative de la fibrillation auriculaire et de l’insuffisance cardiaque d’apparition récente était plus élevée chez les patients ayant une DAG grave à l’ECG que ceux ayant une DAG grave à l’ECG basé sur l’IA.
L’onde P joue un rôle crucial dans la discrimination de la DAG dans les modèles d’ECG basés sur l’IA. Les modèles d’ECG basés sur l’IA surpassent l’ECG dans la prédiction des maladies cardiovasculaires d’apparition récente associées à la DAG grave.
Study the relationship between left atrial (LA) volume and left ventricular (LV) diastolic dysfunction and LV hypertrophy: Correlate LA volume with cardiovascular risk factors
2024, Disease-a-Month
Heart failure (HF) with normal ejection fraction - the isolated diastolic heart failure, depicts increasing prevalence and health care burden in recent times. Having less mortality rate compared to systolic heart failure but high morbidity, it is evolving as a major cardiac concern. With increasing clinical use of Left atrial volume (LAV) quantitation in clinical settings, LAV has emerged as an important independent predictor of cardiovascular outcome in HF with normal ejection fraction. This article is intended to review the diastolic and systolic heart failure, their association with left atrial volume, in depth study of Left atrial function dynamics with determinants of various functional and structural changes.
Left atrial overload detection in ECG using frequency domain features with machine learning and deep learning algorithms
2023, Biomedical Signal Processing and Control
There are many studies conducted to identify left atrial overload (LAO) biomarkers using ECG however, low specificity remained to be a common problem. Therefore, in this study, we explored specific features and best AI models for a highly accurate diagnosis of LAO using the ECG signal, with a broad comparison from baseline to state-of-the-art methodologies. The frequency domain properties of the ECG signal were obtained by wavelet transform and wavelet scattering methods to detect LAO from the ECG signal. ECG data were obtained from the PTB-XL database. For feature extraction, 10 s ECG waveforms from 403 healthy and 352 LAO-diagnosed individuals were used after carefully filtering. Each signal was then divided into 1.5 s epochs resulting in 4530 ECG signals. Then, four different machine learning (support vector machine, K-nearest neighbor, linear discriminant analysis, random forest) and three different deep learning algorithms (long-short term memory, 1-D convolutional neural network, 1D-CNN, and 2D convolutional neural network, 2D-CNN) were tested for the detection of LAO. Our results showed that frequency-domain features are much more capable of detecting LAO than time-domain features. Wavelet scattering features were superior to wavelet transform features such as Shannon entropy, variance, and energy, but we achieved the highest success rate with 2D-CNN and continuous wavelet transforms scalogram inputs (accuracy: 92%). Given its high success rate, 2D-CNN may assist clinicians by detecting the pathology with a low-cost and operator-independent method based on a short-term recording.
Left Atrial Deformation in Heart Failure: A Clinical Update
2023, Current Problems in Cardiology
Citation Excerpt :
Nevertheless, LA volume alteration may not effectively represent acute changes in LV filling pressure.4 However, this marker has been reported as a strong predictor of adverse outcomes in a variety of cardiac diseases.5,8,9,10,11,12 Moreover, indexes that are based on LA volume change, such as LA ejection fraction, LA emptying fraction, and LA expansion index13 have clinical implications.
Despite left ventricular global longitudinal strain is an eminent and validated marker of cardiovascular disease, assessment of left atrial size and function may have incremental role in diagnosis and management of cardiovascular disease. Left atrial strain, measured by 2-dimensional speckle tracking echocardiography, is a non–invasive biomarker for the assessment of left atrial function. This novel marker, has additional value to traditional echocardiography markers of left atrial function such as left atrial diameter and volume for the diagnosis and management of left ventricular diastolic dysfunction, heart failure with preserved ejection fraction, atrial fibrillation and valvular disease. However, there are potent limitations for its use in the daily clinical practice, regarding loading conditions, image acquisition and heart rate. The aim of this review is to summarize the published evidence about left atrial strain, as assessed by speckle tracking imaging, and to discuss its clinical implications and its potent limitations.
Atrial cardiomyopathy: Diagnosis, clinical implications and unresolved issues in anticoagulation therapy
2023, Journal of Electrocardiology
Atrial cardiomyopathy (AC) is an evolving pathophysiological entity that has expanded our understanding regarding the atrium and its role in arrhythmogenesis and cardiac thromboembolism. The pathological myocardium in AC promotes arrhythmogenesis through mechanical dysfunction (hypocontractility, fibrosis), adverse alterations of the endothelium and secretion of prothrombotic factors (IL-6, IL-8, TNF-a). ‘Red flags’, indicative of AC, can be recognized either non-invasively by electrocardiography, echocardiography and cardiac magnetic resonance imaging or invasively by high-density electroanatomical mapping as low bipolar voltage areas of the affected myocardium. Signs of AC have been strongly associated with an increased risk of ischemic stroke, even embolic strokes of undetermined source, regardless of the coexistence of atrial fibrillation (AF). The underlying existence of AC has been negatively correlated with the success rate of catheter ablation of AF. The clinical value of AC is the provision of a novel pathway regarding the potential mechanisms of cerebrovascular events of cardiac thromboembolic origin. In addition, AC may serve as a risk stratification tool to predict the long-term responders of AF catheter ablation.
Indexing Left Atrial Volumes: Alternative Indexing Methods Better Predict Outcomes in Overweight and Obese Populations
2022, JACC: Cardiovascular Imaging
Left atrial volume (LAV) is often adjusted for body surface area (BSA). In overweight individuals this may result in underestimation of left atrial (LA) dilation. The authors investigated whether alternative indexing techniques better predict mortality and cardiovascular (CV) events.
The purpose of this study was to evaluate the efficacy of different methods of indexing LAV in predicting mortality and CV events across a range of body sizes.
LAV was adjusted for BSA, idealized BSA (iBSA), height, and height-squared (H²) in patients aged over 50 years who underwent outpatient echocardiography and longitudinal follow-up at our institution. LA dilation was categorized using published criteria. Mortality and CV events were assessed via medical records.
LAVs were calculated in 17,454 individuals. In this study, 71.2% were overweight or obese. Indexing using iBSA, height, and H² resulted in reclassification of LA size in up to 28.4% (P < 0.001) compared with indexing using BSA. In severely obese individuals (body mass index [BMI] ≥40 kg/m²), LA dilation indexed for BSA no longer predicted mortality (P = 0.70). Other indexing methods remained predictive of mortality. Height, H², and iBSA all had greater performance, compared with BSA, for prediction of mortality and CV events in all overweight patients with H² showing the best overall performance (P < 0.001). Net reclassification index for mortality was significant for all alternative indexing techniques (P < 0.001) and patients whose LA was reclassified from normal to dilated had increased risk of mortality (P < 0.001) and CV events (P < 0.001) across all BMI categories.
LA dilation based on standard indexing using BSA is nondiscriminatory for prediction of mortality in the severely obese. Indexing using height, H², or iBSA to diagnose LA dilation better predicts mortality in this population and has better overall predictive performance across all overweight and obese populations. Using BSA indexing may lead to underappreciation of LA dilation and underestimation of patients at increased risk.

View all citing articles on Scopus

: This study was supported by the American Heart Association, Dallas, Texas, and the American Society of Echocardiography, Raleigh, North Carolina.

View full text

Heart failureUsefulness of Left Atrial Volume in Predicting First Congestive Heart Failure in Patients ≥65 Years of Age With Well-Preserved Left Ventricular Systolic Function

Section snippets

Study design and population

Results

Discussion

Mayo Clin Proc

J Am Coll Cardiol

J Am Coll Cardiol

Am J Cardiol

J Am Coll Cardiol

J Am Coll Cardiol

Am J Cardiol

Heart failure
Usefulness of Left Atrial Volume in Predicting First Congestive Heart Failure in Patients ≥65 Years of Age With Well-Preserved Left Ventricular Systolic Function