A deep patient-similarity learning framework for the assessment of diastolic dysfunction in elderly patients.

AIMS: Age-related changes in cardiac structure and function are well recognized and make the clinical determination of abnormal left ventricular (LV) diastolic dysfunction (LVDD) particularly challenging in the elderly. We investigated whether a deep neural network (DeepNN) model of LVDD, previously validated in a younger cohort, can be implemented in an older population to predict incident heart failure (HF). METHODS AND RESULTS: A previously developed DeepNN was tested on 5596 older participants (66-90 years; 57% female; 20% Black) from the Atherosclerosis Risk in Communities Study. The association of DeepNN predictions with HF or all-cause death for the American College of Cardiology Foundation/American Heart Association Stage A/B (n = 4054) and Stage C/D (n = 1542) subgroups was assessed. The DeepNN-predicted high-risk compared with the low-risk phenogroup demonstrated an increased incidence of HF and death for both Stage A/B and Stage C/D (log-rank P < 0.0001 for all). In multi-variable analyses, the high-risk phenogroup remained an independent predictor of HF and death in both Stages A/B {adjusted hazard ratio [95% confidence interval (CI)] 6.52 [4.20-10.13] and 2.21 [1.68-2.91], both P < 0.0001} and Stage C/D [6.51 (4.06-10.44) and 1.03 (1.00-1.06), both P < 0.0001], respectively. In addition, DeepNN showed incremental value over the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) guidelines [net re-classification index, 0.5 (CI 0.4-0.6), P < 0.001; C-statistic improvement, DeepNN (0.76) vs. ASE/EACVI (0.70), P < 0.001] overall and maintained across stage groups. CONCLUSION: Despite training with a younger cohort, a deep patient-similarity-based learning framework for assessing LVDD provides a robust prediction of all-cause death and incident HF for older patients.

Electrocardiogram-Based Machine Learning Emulator Model for Predicting Novel Echocardiography-Derived Phenogroups for Cardiac Risk-Stratification: A Prospective Multicenter Cohort Study.

Purpose: Electrocardiography (ECG)-derived machine learning models can predict echocardiography (echo)-derived indices of systolic or diastolic function. However, systolic and diastolic dysfunction frequently coexists, which necessitates an integrated assessment for optimal risk-stratification. We explored an ECG-derived model that emulates an echo-derived model that combines multiple parameters for identifying patient phenogroups at risk for major adverse cardiac events (MACE). Methods: In this substudy of a prospective, multicenter study, patients from 3 institutions (n=727) formed an internal cohort, and the fourth institution was reserved as an external test set (n=518). A previously validated patient similarity analysis model was used for labeling the patients as low-/high-risk phenogroups. These labels were utilized for training an ECG-derived deep neural network model to predict MACE risk per phenogroup. After 5-fold cross-validation training, the model was tested on the reserved external dataset. Results: Our ECG-derived model showed robust classification of patients, with area under the receiver operating characteristic curve of 0.86 (95% CI: 0.79-0.91) and 0.84 (95% CI: 0.80-0.87), sensitivity of 80% and 76%, and specificity of 88% and 75% for the internal and external test sets, respectively. The ECG-derived model demonstrated an increased probability for MACE in high-risk vs low-risk patients (21% vs 3%; P<0.001), which was similar to the echo-trained model (21% vs 5%; P<0.001), suggesting comparable utility. Conclusions: This novel ECG-derived machine learning model provides a cost-effective strategy for predicting patient subgroups in whom an integrated milieu of systolic and diastolic dysfunction is associated with a high risk of MACE.

High Prevalence of Pericardial Involvement in College Student Athletes Recovering From COVID-19.

OBJECTIVES: This study sought to explore the spectrum of cardiac abnormalities in student athletes who returned to university campus in July 2020 with uncomplicated coronavirus disease 2019 (COVID-19). BACKGROUND: There is limited information on cardiovascular involvement in young individuals with mild or asymptomatic COVID-19. METHODS: Screening echocardiograms were performed in 54 consecutive student athletes (mean age 19 years; 85% male) who had positive results of reverse transcription polymerase chain reaction nasal swab testing of the upper respiratory tract or immunoglobulin G antibodies against severe acute respiratory syndrome coronavirus type 2. Sequential cardiac magnetic resonance imaging was performed in 48 (89%) subjects. RESULTS: A total of 16 (30%) athletes were asymptomatic, whereas 36 (66%) and 2 (4%) athletes reported mild and moderate COVID-19 related symptoms, respectively. For the 48 athletes completing both imaging studies, abnormal findings were identified in 27 (56.3%) individuals. This included 19 (39.5%) athletes with pericardial late enhancements with associated pericardial effusion. Of the individuals with pericardial enhancements, 6 (12.5%) had reduced global longitudinal strain and/or an increased native T1. One patient showed myocardial enhancement, and reduced left ventricular ejection fraction or reduced global longitudinal strain with or without increased native T1 values was also identified in an additional 7 (14.6%) individuals. Native T2 findings were normal in all subjects, and no specific imaging features of myocardial inflammation were identified. Hierarchical clustering of left ventricular regional strain identified 3 unique myopericardial phenotypes that showed significant association with the cardiac magnetic resonance findings (p = 0.03). CONCLUSIONS: More than 1 in 3 previously healthy college athletes recovering from COVID-19 infection showed imaging features of a resolving pericardial inflammation. Although subtle changes in myocardial structure and function were identified, no athlete showed specific imaging features to suggest an ongoing myocarditis. Further studies are needed to understand the clinical implications and long-term evolution of these abnormalities in uncomplicated COVID-19.

Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features.

BACKGROUND: Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinical tools for identifying diastolic dysfunction before echocardiography remain imprecise. OBJECTIVES: This study sought to develop machine-learning models that quantitatively estimate myocardial relaxation using clinical and electrocardiography (ECG) variables as a first step in the detection of LV diastolic dysfunction. METHODS: A multicenter prospective study was conducted at 4 institutions in North America enrolling a total of 1,202 subjects. Patients from 3 institutions (n = 814) formed an internal cohort and were randomly divided into training and internal test sets (80:20). Machine-learning models were developed using signal-processed ECG, traditional ECG, and clinical features and were tested using the test set. Data from the fourth institution was reserved as an external test set (n = 388) to evaluate the model generalizability. RESULTS: Despite diversity in subjects, the machine-learning model predicted the quantitative values of the LV relaxation velocities (e') measured by echocardiography in both internal and external test sets (mean absolute error: 1.46 and 1.93 cm/s; adjusted R2 = 0.57 and 0.46, respectively). Analysis of the area under the receiver operating characteristic curve (AUC) revealed that the estimated e' discriminated the guideline-recommended thresholds for abnormal myocardial relaxation and diastolic and systolic dysfunction (LV ejection fraction) the internal (area under the curve [AUC]: 0.83, 0.76, and 0.75) and external test sets (0.84, 0.80, and 0.81), respectively. Moreover, the estimated e' allowed prediction of LV diastolic dysfunction based on multiple age- and sex-adjusted reference limits (AUC: 0.88 and 0.94 in the internal and external sets, respectively). CONCLUSIONS: A quantitative prediction of myocardial relaxation can be performed using easily obtained clinical and ECG features. This cost-effective strategy may be a valuable first clinical step for assessing the presence of LV dysfunction and may potentially aid in the early diagnosis and management of heart failure patients.

A Network-Based "Phenomics" Approach for Discovering Patient Subtypes From High-Throughput Cardiac Imaging Data.

OBJECTIVES: The authors present a method that focuses on cohort matching algorithms for performing patient-to-patient comparisons along multiple echocardiographic parameters for predicting meaningful patient subgroups. BACKGROUND: Recent efforts in collecting multiomics data open numerous opportunities for comprehensive integration of highly heterogenous data to classify a patient's cardiovascular state, eventually leading to tailored therapies. METHODS: A total of 42 echocardiography features, including 2-dimensional and Doppler measurements, left ventricular (LV) and atrial speckle-tracking, and vector flow mapping data, were obtained in 297 patients. A similarity network was developed to delineate distinct patient phenotypes, and then neural network models were trained for discriminating the phenotypic presentations. RESULTS: The patient similarity model identified 4 clusters (I to IV), with patients in each cluster showed distinctive clinical presentations based on American College of Cardiology/American Heart Association heart failure stage and the occurrence of short-term major adverse cardiac and cerebrovascular events. Compared with other clusters, cluster IV had a higher prevalence of stage C or D heart failure (78%; p < 0.001), New York Heart Association functional classes III or IV (61%; p < 0.001), and a higher incidence of major adverse cardiac and cerebrovascular events (p < 0.001). The neural network model showed robust prediction of patient clusters, with area under the receiver-operating characteristic curve ranging from 0.82 to 0.99 for the independent hold-out validation set. CONCLUSIONS: Automated computational methods for phenotyping can be an effective strategy to fuse multidimensional parameters of LV structure and function. It can identify distinct cardiac phenogroups in terms of clinical characteristics, cardiac structure and function, hemodynamics, and outcomes.

A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound.

BACKGROUND: Maturation of ultrasound myocardial tissue characterization may have far-reaching implications as a widely available alternative to cardiac magnetic resonance (CMR) for risk stratification in left ventricular (LV) remodeling. METHODS: We extracted 328 texture-based features of myocardium from still ultrasound images. After we explored the phenotypes of myocardial textures using unsupervised similarity networks, global LV remodeling parameters were predicted using supervised machine learning models. Separately, we also developed supervised models for predicting the presence of myocardial fibrosis using another cohort who underwent cardiac magnetic resonance (CMR). For the prediction, patients were divided into a training and test set (80:20). FINDINGS: Texture-based tissue feature extraction was feasible in 97% of total 534 patients. Interpatient similarity analysis delineated two patient groups based on the texture features: one group had more advanced LV remodeling parameters compared to the other group. Furthermore, this group was associated with a higher incidence of cardiac deaths (p = 0.001) and major adverse cardiac events (p < 0.001). The supervised models predicted reduced LV ejection fraction (<50%) and global longitudinal strain (<16%) with area under the receiver-operator-characteristics curves (ROC AUC) of 0.83 and 0.87 in the hold-out test set, respectively. Furthermore, the presence of myocardial fibrosis was predicted from only ultrasound myocardial texture with an ROC AUC of 0.84 (sensitivity 86.4% and specificity 83.3%) in the test set. INTERPRETATION: Ultrasound texture-based myocardial tissue characterization identified phenotypic features of LV remodeling from still ultrasound images. Further clinical validation may address critical barriers in the adoption of ultrasound techniques for myocardial tissue characterization. FUNDING: None.

Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine.

OBJECTIVES: The authors applied unsupervised machine-learning techniques for integrating echocardiographic features of left ventricular (LV) structure and function into a patient similarity network that predicted major adverse cardiac event(s) (MACE) in an individual patient. BACKGROUND: Patient similarity analysis is an evolving paradigm for precision medicine in which patients are clustered or classified based on their similarities in several clinical features. METHODS: A retrospective cohort of 866 patients was used to develop a network architecture using 9 echocardiographic features of LV structure and function. The data for 468 patients from 2 prospective cohort registries were then added to test the model's generalizability. RESULTS: The map of cross-sectional data in the retrospective cohort resulted in a looped patient network that persisted even after the addition of data from the prospective cohort registries. After subdividing the loop into 4 regions, patients in each region showed unique differences in LV function, with Kaplan-Meier curves demonstrating significant differences in MACE-related rehospitalization and death (both p < 0.001). Addition of network information to clinical risk predictors resulted in significant improvements in net reclassification, integrated discrimination, and median risk scores for predicting MACE (p < 0.05 for all). Furthermore, the network predicted the cardiac disease cycle in each of the 96 patients who had second echocardiographic evaluations. An improvement or remaining in low-risk regions was associated with lower MACE-related rehospitalization rates than worsening or remaining in high-risk regions (3% vs. 37%; p < 0.001). CONCLUSIONS: Patient similarity analysis integrates multiple features of cardiac function to develop a phenotypic network in which patients can be mapped to specific locations associated with specific disease stage and clinical outcomes. The use of patient similarity analysis may have relevance for automated staging of cardiac disease severity, personalized prediction of prognosis, and monitoring progression or response to therapies.

Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study.

Aims: Coronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms. Methods and results: In this substudy of a prospective, multicentre trial, a total of 534 subjects referred for CAC scores and electrocardiographic data were split into 80% training and 20% testing sets. Two binary outcome ML logistic regression models were developed for prediction of CAC scores equal to 0 and ≥400. Both CAC = 0 and CAC ≥400 models yielded values for the area under the curve, sensitivity, specificity, and accuracy of 84%, 92%, 70%, and 75%, and 87%, 91%, 75%, and 81%, respectively. We further tested the CAC ≥400 model to risk stratify a cohort of 87 subjects referred for invasive coronary angiography. Using an intermediate or higher pretest probability (≥15%) to predict CAC ≥400, the model predicted the presence of significant coronary artery stenosis (P = 0.025), the need for revascularization (P < 0.001), notably bypass surgery (P = 0.021), and major adverse cardiovascular events (P = 0.023) during a median follow-up period of 2 years. Conclusion: ML techniques can extract information from electrocardiographic data and clinical variables to predict CAC score categories and similarly risk-stratify patients with suspected coronary artery disease.

Targeting senescent cells alleviates obesity-induced metabolic dysfunction.

Adipose tissue inflammation and dysfunction are associated with obesity-related insulin resistance and diabetes, but mechanisms underlying this relationship are unclear. Although senescent cells accumulate in adipose tissue of obese humans and rodents, a direct pathogenic role for these cells in the development of diabetes remains to be demonstrated. Here, we show that reducing senescent cell burden in obese mice, either by activating drug-inducible "suicide" genes driven by the p16Ink4a promoter or by treatment with senolytic agents, alleviates metabolic and adipose tissue dysfunction. These senolytic interventions improved glucose tolerance, enhanced insulin sensitivity, lowered circulating inflammatory mediators, and promoted adipogenesis in obese mice. Elimination of senescent cells also prevented the migration of transplanted monocytes into intra-abdominal adipose tissue and reduced the number of macrophages in this tissue. In addition, microalbuminuria, renal podocyte function, and cardiac diastolic function improved with senolytic therapy. Our results implicate cellular senescence as a causal factor in obesity-related inflammation and metabolic derangements and show that emerging senolytic agents hold promise for treating obesity-related metabolic dysfunction and its complications.

Network Tomography for Understanding Phenotypic Presentations in Aortic Stenosis.

OBJECTIVES: This study sought to build a patient-patient similarity network using multiple features of left ventricular (LV) structure and function in patients with aortic stenosis (AS). The study further validated the observations in an experimental murine model of AS. BACKGROUND: The LV response in AS is variable and results in heterogeneous phenotypic presentations. METHODS: The patient similarity network was developed using topological data analysis (TDA) from cross-sectional echocardiographic data collected from 246 patients with AS. Multivariate features of AS were represented on the map, and the network topology was compared with that of a murine AS model by imaging 155 animals at 3, 6, 9, or 12 months of age. RESULTS: The topological map formed a loop in which patients with mild and severe AS were aggregated on the right and left sides, respectively (p < 0.001). These 2 regions were linked through moderate AS; with upper arm of the loop showing patients with predominantly reduced ejection fractions (EFs), and the lower arm showing patients with preserved EFs (p < 0.001). The region of severe AS showed >3 times the increased risk of balloon valvuloplasty, and transcatheter or surgical aortic valve replacement (hazard ratio: 3.88; p < 0.001) compared with the remaining patients in the map. Following aortic valve replacement, patients recovered and moved toward the zone of mild and moderate AS. Topological data analysis in mice showed a similar distribution, with 1 side of the loop corresponding to higher peak aortic velocities than the opposite side (p < 0.0001). The validity of the cross-sectional data that revealed a path of AS progression was confirmed by comparing the locations occupied by 2 groups of mice that were serially imaged. LV systolic and diastolic dysfunction were frequently identified even during moderate AS in both humans and mice. CONCLUSIONS: Multifeature assessments of patient similarity by machine-learning processes may allow precise phenotypic recognition of the pattern of LV responses during the progression of AS.

Pathophysiology of Aortic Valve Stenosis: Is It Both Fibrocalcific and Sex Specific?

Our understanding of the fundamental biology and identification of efficacious therapeutic targets in aortic valve stenosis has lagged far behind the fields of atherosclerosis and heart failure. In this review, we highlight the most clinically relevant problems facing men and women with fibrocalcific aortic valve stenosis, discuss the fundamental biology underlying valve calcification and fibrosis, and identify key molecular points of intersection with sex hormone signaling.

Echocardiographic Approaches and Protocols for Comprehensive Phenotypic Characterization of Valvular Heart Disease in Mice.

The aim of this manuscript and accompanying video is to provide an overview of the methods and approaches used for imaging heart valve function in rodents, with detailed descriptions of the appropriate methods for anesthesia, the echocardiographic windows used, the imaging planes and probe orientations for image acquisition, the methods for data analysis, and the limitations of emerging technologies for the evaluation of cardiac and valvular function. Importantly, we also highlight several future areas of research in cardiac and heart valve imaging that may be leveraged to gain insights into the pathogenesis of valve disease in preclinical animal models. We propose that using a systematic approach to evaluating cardiac and heart valve function in mice can result in more robust and reproducible data, as well as facilitate the discovery of previously underappreciated phenotypes in genetically-altered and/or physiologically-stressed mice.

17α-Estradiol Alleviates Age-related Metabolic and Inflammatory Dysfunction in Male Mice Without Inducing Feminization.

Aging is associated with visceral adiposity, metabolic disorders, and chronic low-grade inflammation. 17α-estradiol (17α-E2), a naturally occurring enantiomer of 17ß-estradiol (17ß-E2), extends life span in male mice through unresolved mechanisms. We tested whether 17α-E2 could alleviate age-related metabolic dysfunction and inflammation. 17α-E2 reduced body mass, visceral adiposity, and ectopic lipid deposition without decreasing lean mass. These declines were associated with reductions in energy intake due to the activation of hypothalamic anorexigenic pathways and direct effects of 17α-E2 on nutrient-sensing pathways in visceral adipose tissue. 17α-E2 did not alter energy expenditure or excretion. Fasting glucose, insulin, and glycosylated hemoglobin were also reduced by 17α-E2, and hyperinsulinemic-euglycemic clamps revealed improvements in peripheral glucose disposal and hepatic glucose production. Inflammatory mediators in visceral adipose tissue and the circulation were reduced by 17α-E2. 17α-E2 increased AMPKα and reduced mTOR complex 1 activity in visceral adipose tissue but not in liver or quadriceps muscle, which is in contrast to the generalized systemic effects of caloric restriction. These beneficial phenotypic changes occurred in the absence of feminization or cardiac dysfunction, two commonly observed deleterious effects of exogenous estrogen administration. Thus, 17α-E2 holds potential as a novel therapeutic for alleviating age-related metabolic dysfunction through tissue-specific effects.

Chronic senolytic treatment alleviates established vasomotor dysfunction in aged or atherosclerotic mice.

While reports suggest a single dose of senolytics may improve vasomotor function, the structural and functional impact of long-term senolytic treatment is unknown. To determine whether long-term senolytic treatment improves vasomotor function, vascular stiffness, and intimal plaque size and composition in aged or hypercholesterolemic mice with established disease. Senolytic treatment (intermittent treatment with Dasatinib + Quercetin via oral gavage) resulted in significant reductions in senescent cell markers (TAF(+) cells) in the medial layer of aorta from aged and hypercholesterolemic mice, but not in intimal atherosclerotic plaques. While senolytic treatment significantly improved vasomotor function (isolated organ chamber baths) in both groups of mice, this was due to increases in nitric oxide bioavailability in aged mice and increases in sensitivity to NO donors in hypercholesterolemic mice. Genetic clearance of senescent cells in aged normocholesterolemic INK-ATTAC mice phenocopied changes elicited by D+Q. Senolytics tended to reduce aortic calcification (alizarin red) and osteogenic signaling (qRT-PCR, immunohistochemistry) in aged mice, but both were significantly reduced by senolytic treatment in hypercholesterolemic mice. Intimal plaque fibrosis (picrosirius red) was not changed appreciably by chronic senolytic treatment. This is the first study to demonstrate that chronic clearance of senescent cells improves established vascular phenotypes associated with aging and chronic hypercholesterolemia, and may be a viable therapeutic intervention to reduce morbidity and mortality from cardiovascular diseases.

Nonbiased Molecular Screening Identifies Novel Molecular Regulators of Fibrogenic and Proliferative Signaling in Myxomatous Mitral Valve Disease.

BACKGROUND: Pathological processes underlying myxomatous mitral valve degeneration (MMVD) remain poorly understood. We sought to identify novel mechanisms contributing to the development of this condition. METHODS AND RESULTS: Microarrays were used to measure gene expression in 11 myxomatous and 11 nonmyxomatous human mitral valves. Differential gene expression (thresholds P<0.05; fold-change >1.5) and pathway activation (Ingenuity) were confirmed using quantitative reverse transcriptase polymerase chain reaction and immunohistochemistry. Contributions of bone morphogenetic protein 4 and transforming growth factor (TGF)-ß2 to differential gene expression were evaluated in vitro. Contributions of angiotensin II to differential pathway activation were examined in mice in vivo. A total of 2602 genes were differentially expressed between myxomatous and nonmyxomatous valves. Canonical TGF-ß signaling was increased in MMVD because of increased ligand expression and derepression of SMA mothers against decapentaplegic 2/3 signaling and was confirmed with quantitative reverse transcriptase polymerase chain reaction and immunohistochemistry. Myxomatous valves demonstrated activation of canonical bone morphogenetic protein and Wnt/ß-catenin signaling and upregulation of their common target runt-related transcription factor 2. Our data set provided transcriptional and immunohistochemical evidence for activated immune cell infiltration. In vitro treatment of mitral valve interstitial cells with TGF-ß2 increased ß-catenin signaling at mRNA and protein levels, suggesting interactions between TGF-ß2 and Wnt signaling. In vivo infusion of mice with angiotensin II recaptured several changes in signaling pathways characteristic of human MMVD. CONCLUSIONS: These data support a new disease framework whereby activation of TGF-ß2, bone morphogenetic protein 4, Wnt/ß-catenin, or immune signaling plays major roles in the pathogenesis of MMVD. We propose these pathways act in a context-dependent manner to drive phenotypic changes that fundamentally differ from those observed in aortic valve disease and open novel avenues guiding future research into the pathogenesis of MMVD.

Infiltrative cardiovascular diseases: cardiomyopathies that look alike.

Infiltrative cardiomyopathies are characterized by the deposition of abnormal substances that cause the ventricular walls to become progressively rigid, thereby impeding ventricular filling. Some infiltrative cardiac diseases increase ventricular wall thickness, while others cause chamber enlargement with secondary wall thinning. Increased wall thickness, small ventricular volume, and occasional dynamic left ventricular outflow obstruction (e.g., amyloidosis) can outwardly appear similar to conditions with true myocyte hypertrophy (e.g., hypertrophic cardiomyopathy, hypertensive heart disease). Likewise, infiltrative disease that presents with a dilated left ventricle with global or regional wall motion abnormalities and aneurysm formation (e.g., sarcoidosis) may mimic ischemic cardiomyopathy. Low-voltage QRS complex was the sine qua non of infiltrative cardiomyopathy (i.e., cardiac amyloid). However, low-voltage QRS complex is not a uniform finding with the infiltrative cardiomyopathies. The clinical presentation, along with functional and morphologic features, often provides enough insight to establish a working diagnosis. In most circumstances, however, tissue or serologic evaluation is needed to validate or clarify the cardiac diagnosis and institute appropriate therapy.

Does left atrial size predict mortality in asymptomatic patients with severe aortic stenosis?

BACKGROUND: We assessed the hypothesis that diastolic function represented by left atrial size determines the rate of development of symptoms and the risk of all-cause mortality in asymptomatic patients with severe aortic stenosis (AS). METHODS: From a database of 622 asymptomatic patients with isolated severe AS (velocity by Doppler >or= 4 m/sec) followed for 5.4 +/- 4 years, we reviewed the echocardiograms and evaluated Doppler echocardiographic indices of diastolic function. Prediction of symptom development and mortality by left atrial diameter with and without adjusting for clinical and echocardiographic parameters was performed using Cox proportional-hazards regression analysis. RESULTS: The age was 71 +/- 11 years and 317 (62%) patients were males. The aortic valve mean gradient was 46 +/- 11 mmHg, and the Doppler-derived aortic valve area was 0.9 +/- 0.2 cm(2). During follow-up, symptoms developed in 233 (45%), valve surgery was performed in 290 (57%) and 138 (27%) died. Left atrial enlargement was significantly correlated with symptom development (P < 0.05) but the association diminished after adjusting for aortic valve area and peak velocity (P = 0.2). However, atrial diameter predicted death independent of age and gender (P = 0.007), comorbid conditions (P = 0.03), and AS severity and Doppler parameters of diastolic function (P = 0.002). CONCLUSION: Diastolic function, represented as left atrial diameter, is related to mortality in asymptomatic patients with severe AS.

C-reactive protein, left atrial volume, and atrial fibrillation: a prospective study in high-risk elderly.

BACKGROUND: The data regarding the interrelationships of high-sensitive C-reactive protein (CRP), left atrial (LA) volume, and atrial fibrillation (AF) are sparse. Additionally, while LA volume has been shown to be useful for prediction of AF in low-to-moderate risk populations, its predictive value in clinically high-risk populations is unknown. METHODS: SAFHIRE (Study of Atrial Fibrillation in High Risk Elderly) is an ongoing prospective study of the pathophysiology of first AF in persons aged > or = 65 years with > or = 2 other AF risk factors [systemic hypertension, proven coronary artery disease, heart failure (HF), diabetes]. Participants are followed annually, and undergo an interview, physical examination, blood work, electrocardiogram, and echocardiogram assessment. RESULTS: Of 800 participants, mean age of 74 +/- 6 years, 34 developed first AF over 1.7+/- 0.9 years. A history of systemic hypertension and proven coronary artery disease was present in 97% and 78%, respectively. CRP was unrelated to LA volume on univariable or multivariable analyses (P > 0.10), and not predictive of first AF on univariable or multivariable models (all P > 0.10). Indexed LA volume was an independent predictor of first AF (unadjusted P< 0.0001; age and sex adjusted P = 0.0006; adjusted for multiple factors, HR 1.3/5 ml per m(2), 95% CI, 1.09 to 1.48, P = 0.001). CONCLUSION: In this elderly population at high clinical risk for the development of first AF, CRP was unrelated to LA volume and nonpredictive of first AF, while indexed LA volume was incremental to clinical risk factors for the prediction of first AF.

Diastolic dysfunction and left atrial enlargement as contributing factors to functional mitral regurgitation in dilated cardiomyopathy: data from the Acorn trial.

BACKGROUND: Functional mitral regurgitation (MR) is commonly seen in dilated cardiomyopathy (DCM), which may result from left ventricular (LV) dilatation and alteration in the geometric relationship of mitral valve apparatus. However, not all patients with DCM show significant MR and left atrial (LA) enlargement. The aim of this study was to assess responsible factors for developing mitral valve regurgitation. METHODS: Of 300 patients enrolled in the Acorn trial, baseline echocardiography studies were available in 288, of whom 144 were excluded because of a variety of reasons. Echocardiographic data were examined for the remaining 144 patients in sinus rhythm with DCM, but without organic mitral valve disease and ischemic heart disease. Mitral regurgitation was assessed by color-flow imaging. All echocardiographic parameters were indexed to body surface area. RESULTS: Of 144 patients, 87 had MR grade > or =2 (group 1) and 57 had MR grade 0 or +1 (group 2). Group 1 had larger tenting area, tenting height, tethering distance, LA volume index, and mitral annular area than group 2 (all P < .001); LV volume index and ejection fraction were similar between groups. The major determinant of MR severity was tenting area (r = 0.49, P < .001), and this was best related to mitral annular area (r = 0.85, P < .001). Mitral annular area was most strongly associated with LA volume (r = 0.56, P < .001). In addition, LA volume index was highly correlated with LV diastolic dysfunction (r = 0.58, P < .001), both in total and in group 2 only. CONCLUSIONS: For patients with DCM in the Acorn trial, MR severity was associated with LA volume and mitral annular area but not with LV volume. Mitral annular area and LA volume were closely related, even in patients without significant MR. These findings suggest that LA enlargement caused by advanced diastolic dysfunction may contribute to causing significant MR by augmenting mitral annular dilatation in DCM.