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Pseudo cardiac tamponade in the setting of excess pericardial fat.

ABSTRACT: Cardiac tamponade is the phenomenon of hemodynamic compromise caused by a pericardial effusion. Following a myocardial infarction, the most common causes of pericardial fluid include early pericarditis, Dressler's syndrome, and hemopericardium secondary to a free wall rupture. On transthoracic echocardiography, pericardial fluid appears as an echo-free space in between the visceral and parietal layers of the pericardium. Pericardial fat has a similar appearance on echocardiography and it may be difficult to discern the two entities. We present a case of a post-MI patient demonstrating pseudo tamponade physiology in the setting of excessive pericardial fat.

SUBMITTER: Nguyen T

PROVIDER: S-EPMC2632988 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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Pseudo cardiac tamponade in the setting of excess pericardial fat.

Nguyen Thang T Kumar Kanwal K Francis Andrew A Walker Jonathan R JR Raabe Michael M Zieroth Shelley S Jassal Davinder S DS

Cardiovascular ultrasound 20090122

Cardiac tamponade is the phenomenon of hemodynamic compromise caused by a pericardial effusion. Following a myocardial infarction, the most common causes of pericardial fluid include early pericarditis, Dressler's syndrome, and hemopericardium secondary to a free wall rupture. On transthoracic echocardiography, pericardial fluid appears as an echo-free space in between the visceral and parietal layers of the pericardium. Pericardial fat has a similar appearance on echocardiography and it may be ...[more]

PMID: 19161596

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Project description:BackgroundTimely and accurate detection of pericardial effusion and assessment cardiac tamponade remain challenging and highly operator dependent.ObjectivesArtificial intelligence has advanced many echocardiographic assessments, and we aimed to develop and validate a deep learning model to automate the assessment of pericardial effusion severity and cardiac tamponade from echocardiogram videos.MethodsWe developed a deep learning model (EchoNet-Pericardium) using temporal-spatial convolutional neural networks to automate pericardial effusion severity grading and tamponade detection from echocardiography videos. The model was trained using a retrospective dataset of 1,427,660 videos from 85,380 echocardiograms at Cedars-Sinai Medical Center (CSMC) to predict PE severity and cardiac tamponade across individual echocardiographic views and an ensemble approach combining predictions from five standard views. External validation was performed on 33,310 videos from 1,806 echocardiograms from Stanford Healthcare (SHC).ResultsIn the held out CSMC test set, EchoNet-Pericardium achieved an AUC of 0.900 (95% CI: 0.884-0.916) for detecting moderate or larger pericardial effusion, 0.942 (95% CI: 0.917-0.964) for large pericardial effusion, and 0.955 (95% CI: 0.939-0.968) for cardiac tamponade. In the SHC external validation cohort, the model achieved AUCs of 0.869 (95% CI: 0.794-0.933) for moderate or larger pericardial effusion, 0.959 (95% CI: 0.945-0.972) for large pericardial effusion, and 0.966 (95% CI: 0.906-0.995) for cardiac tamponade. Subgroup analysis demonstrated consistent performance across ages, sexes, left ventricular ejection fraction, and atrial fibrillation statuses.ConclusionsOur deep learning-based framework accurately grades pericardial effusion severity and detects cardiac tamponade from echocardiograms, demonstrating consistent performance and generalizability across different cohorts. This automated tool has the potential to enhance clinical decision-making by reducing operator dependence and expediting diagnosis.

Dataset Information

Pseudo cardiac tamponade in the setting of excess pericardial fat.

Publications

Pseudo cardiac tamponade in the setting of excess pericardial fat.

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