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Pericardial Tamponade and Other Cardiac Complications in POEMS Syndrome.

ABSTRACT: Polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes (POEMS) is a multiorgan syndrome with rare and heterogenous cardiac manifestations. We present the case of a man with pericardial effusion complicated by cardiac tamponade, new onset atrial fibrillation, and high-degree atrioventricular block leading to a diagnosis of POEMS syndrome. (Level of Difficulty: Advanced.).

SUBMITTER: Levene J

PROVIDER: S-EPMC8310984 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Pericardial Tamponade and Other Cardiac Complications in POEMS Syndrome.

Levene Jacqueline J Murray Nathanial N Desai Sima S Simpson Timothy F TF Karam Chafic C Silbermann Rebecca R Masri Ahmad A

JACC. Case reports 20210217 2

Polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes (POEMS) is a multiorgan syndrome with rare and heterogenous cardiac manifestations. We present the case of a man with pericardial effusion complicated by cardiac tamponade, new onset atrial fibrillation, and high-degree atrioventricular block leading to a diagnosis of POEMS syndrome. (<b>Level of Difficulty: Advanced.</b>). ...[more]

PMID: 34317520

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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

Pericardial Tamponade and Other Cardiac Complications in POEMS Syndrome.

Publications

Pericardial Tamponade and Other Cardiac Complications in POEMS Syndrome.

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