Echocardiographic findings in atrial flutter.

Zoneraich, Samuel; Zoneraich, Olga; Rhee, Jai J.

doi:10.1161/01.cir.52.3.455

Cited by 39 publications

(6 citation statements)

References 1 publication

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“…(7) The percent change in minor axis diameter (%AD) was calculated as (LVDd -LVDs)/LVDd X 100 or (RVDd -RVDs) /RVDd X 100. ( 8 ) The aortic root diameter was measured from the anterior edge of the anterior aortic wall to the anterior edge of the posterior aortic wall at ventricular end-diastole. Five consecutive cycles were measured and the average value was utilized for analysis.…”

Section: Echocardiogramsmentioning

confidence: 99%

Assessment of Cardiac Function in Marathon Runners by Graphic Noninvasive Techniques

Zoneraich

Rhee

Zoneraich

et al. 1977

Annals of the New York Academy of Sciences

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The volume overload type of heart often observed in endurance athletes, was simulate a diseased heart. We used a battery of noninvasive graphic techniques, i.e., echocardiogram, apexcardiogram, carotid pulse, electrocardiogram, vectorcardiogram, phonocardiogram, systolic time intervals, and treadmill stress testing in 12 professional marathon runners, mean age 33.8 +/- 11.1. Twenty nonathletes matched for age, height, sex, and weight served as a control group. Left ventricular (LV) end-diastolic dimension in marathon runners averaged 5.53 +/- 0.5 cm compared to 4.81 +/- 0.04 cm in nonathletes (p less than 0.001), LV end-diastolic volume was 172.69 +/- 43.3 ml compared to 113.57 +/- 30.41 ml in nonathletes (p less than 0.001), stroke volume was 122.27 +/- 32.8 ml compared to 78.42 +/- 20.44 ml in non-athletes (p less than 0.001), the thickness of the posterior LV wall was 1.0 +/- 0.2 cm compared to 0.7 +/- 0.1 cm in nonathletes (p less than 0.001), and LV mass was significantly increased, 212.43 +/- 55.8 g compared to 123.48 +/- 24.54 g in non-athletes (p less than 0.01). Left atrium and aortic root were also relatively larger in athletes (p less than 0.01). Right ventricular end-diastolic dimension was enlarged in marathon runners (2.02 +/- 0.65 cm). No statistically significant differences were noted in ejection fraction, percentage of internal diameter shortening (% delta D) and PEP/LVET. The carotid tracing had a bisferiens pulse in five marathon runners. The apexcardiogram showed a bifid systolic thrust in three and absence of abnormal A wave. These abnormalities were related to the overload type of heart as proven by echocardiogram. "Early repolarization syndrome" (abnormal RS-T segment elevation) and notched T waves in ECG had a counterpart a semilunar configuration in the VCG. Three athletes met ECG criteria and one met VCG criteria of LVH. The treadmill exercise ECG was negative in all 12 athletes. Biventricular enlargement and increased left ventricular mass are present in the marathon runner's heart. Myocardial contractility at rest was, however, not statistically different from nonathletes.

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Section: Echocardiogramsmentioning

confidence: 99%

Assessment of Cardiac Function in Marathon Runners by Graphic Noninvasive Techniques

Zoneraich

Rhee

Zoneraich

et al. 1977

Annals of the New York Academy of Sciences

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“…Close relation between flutter waves in ECG and undulatory motion of left atrial posterial wall has been described 1. TTE also documented another interesting finding (online video 1), that is, premature closure of mitral and tricuspid valves.…”

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confidence: 69%

Clapping atrio-ventricular valves

Senguttuvan

Goswami

2012

Heart

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“…However, in some cases, there are distinct structural and functional features that distinguish AFL and AT from AF that have been detected using these imaging methods. For example, right atrial contractile and reservoir function, flutter movements in the left posterior atrial wall, and loss of A wave on pulse wave in Doppler imaging are functional features that are characteristic of AFL [ 91 , 92 , 93 ]. A small study found that a small LA size can increase the risk of AT [ 94 ].…”

Section: Detection and Prediction Of Atrial Arrhythmias By Artificial...mentioning

confidence: 99%

The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation

Lallah,

Laite,

Bangash

et al. 2023

Rev. Cardiovasc. Med.

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Catheter ablation (CA) is considered as one of the most effective methods technique for eradicating persistent and abnormal cardiac arrhythmias. Nevertheless, in some cases, these arrhythmias are not treated properly, resulting in their recurrences. If left untreated, they may result in complications such as strokes, heart failure, or death. Until recently, the primary techniques for diagnosing recurrent arrhythmias following CA were the findings predisposing to the changes caused by the arrhythmias on cardiac imaging and electrocardiograms during follow-up visits, or if patients reported having palpitations or chest discomfort after the ablation. However, these follow-ups may be time-consuming and costly, and they may not always determine the root cause of the recurrences. With the introduction of artificial intelligence (AI), these follow-up visits can be effectively shortened, and improved methods for predicting the likelihood of recurring arrhythmias after their ablation procedures can be developed. AI can be divided into two categories: machine learning (ML) and deep learning (DL), the latter of which is a subset of ML. ML and DL models have been used in several studies to demonstrate their ability to predict and identify cardiac arrhythmias using clinical variables, electrophysiological characteristics, and trends extracted from imaging data. AI has proven to be a valuable aid for cardiologists due to its ability to compute massive amounts of data and detect subtle changes in electric signals and cardiac images, which may potentially increase the risk of recurrent arrhythmias after CA. Despite the fact that these studies involving AI have generated promising outcomes comparable to or superior to human intervention, they have primarily focused on atrial fibrillation while atrial flutter (AFL) and atrial tachycardia (AT) were the subjects of relatively few AI studies. Therefore, the aim of this review is to investigate the interaction of AI algorithms, electrophysiological characteristics, imaging data, risk score calculators, and clinical variables in predicting cardiac arrhythmias following an ablation procedure. This review will also discuss the implementation of these algorithms to enable the detection and prediction of AFL and AT recurrences following CA.

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Echocardiographic findings in atrial flutter.

Cited by 39 publications

References 1 publication

Assessment of Cardiac Function in Marathon Runners by Graphic Noninvasive Techniques

Assessment of Cardiac Function in Marathon Runners by Graphic Noninvasive Techniques

Clapping atrio-ventricular valves

The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation

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