Biatrial Appendage Thrombi in a Heart Failure Patient With Sinus Rhythm

Oe, Hiroki; Ohno, Yuko; Yamanaka, Toshiaki; Watanabe, Nobuhisa; Ito, Hiroshi

doi:10.1161/circulationaha.115.018522

Cited by 3 publications

(2 citation statements)

References 4 publications

(2 reference statements)

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“…An estimated 750,000 Americans experience a myocardial infarction (MI) each year (1). Rehospitalizations after an initial cardiovascular (CV) disease (CVD) event are also a major concern, as there are more than 200,000 recurrent MI's each year (1). In one study 30% of those hospitalized for an MI were readmitted within 90 days (2).…”

Section: Introductionmentioning

confidence: 99%

Effect of Socioeconomic Status on Propensity to Change Risk Behaviors Following Myocardial Infarction: Implications for Healthy Lifestyle Medicine

Gaalema

Elliott

Morford

et al. 2017

Progress in Cardiovascular Diseases

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Introduction Failure to change risk behaviors following myocardial infarction (MI) increases the likelihood of recurrent MI and death. Lower-socioeconomic status (SES) patients are more likely to engage in high-risk behaviors prior to MI. Less well known is whether propensity to change risk behaviors after MI also varies inversely with SES. Methods We performed a systematized literature review addressing changes in risk behaviors following MI as a function of SES. Results 2160 abstracts were reviewed and 44 met eligibility criteria. Behaviors included smoking cessation, cardiac rehabilitation (CR), medication adherence, diet, and physical activity (PA). For each behavior, lower-SES patients were less likely to change after MI. Overall, lower-SES patients were 2 to 4 times less likely to make needed behavior changes (OR's 0.25–0.56). Conclusion Lower-SES populations are less successful at changing risk behaviors post-MI. Increasing their participation in CR/secondary prevention programs, which address multiple risk behaviors, including increasing PA and exercise, should be a priority of healthy lifestyle medicine (HLM).

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Section: Introductionmentioning

confidence: 99%

Effect of Socioeconomic Status on Propensity to Change Risk Behaviors Following Myocardial Infarction: Implications for Healthy Lifestyle Medicine

Gaalema

Elliott

Morford

et al. 2017

Progress in Cardiovascular Diseases

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“…Simultaneous bi-atrial appendage clot formation has been reported in the settings of atrial fibrillation, heart failure in sinus rhythm and also in a patient with heparin induced thrombocytopenia. [5][6][7] Several reasons have been implicated for the lower prevalence of RAA clots. A study by Subramaniam et al in 92 patients with TEE imaging showed that RAA had a wider neck compared to LAA but RAA area and LAA area were found to be similar.…”

Section: Discussionmentioning

confidence: 99%

Biatrial appendages clots in a rheumatic heart disease patient – an important lesson to remember

Gupta¹,

Shewale²,

Sadananda³

et al. 2016

JCDR

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A 39 years lady presented with 6 months history of NYHA class II fatigue and dyspnea. On clinical examination she had irregular pulse, loud and variable S1, loud P2 and a mid-diastolic murmur in mitral area. A 12 lead ECG revealed atrial fibrillation. Transthoracic echocardiogram showed severe mitral stenosis with valve area of 1.0 cm2 and mild mitral regurgitation. The patient was planned for Balloon mitral valvotomy. Prior to the procedure, transesophageal echocardiogram (TEE) was done to rule out clots. TEE revealed dilated left atrium (LA) while left atrial appendage (LAA) had a 17 x 10mm clot (Figure 1A). LAA velocity was reduced (Figure 2). The Right atrial appendage (RAA) also had a clot measuring 18 x 12mm (Figure 1B). Patient had mild mitral regurgitation. In view of biatrial appendages clots, oral anticoagulation with warfarin was advised and balloon valvotomy was deferred.

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Left Atrial Appendage Segmentation Using Fully Convolutional Neural Networks and Modified Three-Dimensional Conditional Random Fields

Cheng

Feng

Wang

et al. 2018

IEEE J. Biomed. Health Inform.

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Thrombosis has become a global disease threatening human health. The left atrial appendage (LAA) is a major source of thrombosis in patients with atrial fibrillation (AF). Positive correlation exists between LAA volume and AF risk. LAA morphology has been suggested to influence thromboembolic risk in AF patients and to help predict thromboembolic events in low-risk patient groups. Automatic segmentation of LAA can greatly help physicians diagnose AF. In consideration of the large anatomical variations of the LAA, we proposed a robust method for automatic LAA segmentation on computed tomographic angiography (CTA) data using fully convolutional neural networks with three-dimensional (3-D) conditional random fields (CRFs). After manual localization of ROI of LAA, we adopted the FCN in natural image segmentation and transferred their learned models by fine-tuning the networks to segment each 2-D LAA slice. Subsequently, we used a modified dense 3-D CRF that accounts for the 3-D spatial information and larger contextual information to refine the segmentations of all slices. Our method was evaluated on 150 sets of CTA data using five-fold cross validation. Compared with manual annotation, we obtained a mean dice overlap of and a mean volume overlap of with a computation time of less than 40 s per volume. Experimental results demonstrated the robustness of our method in dealing with large anatomical variations and computational efficiency for adoption in a daily clinical routine.).

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Biatrial Appendage Thrombi in a Heart Failure Patient With Sinus Rhythm

Abstract: The online-only Data Supplement is available with this article at http://circ.ahajournals.org/lookup/suppl

Cited by 3 publications

References 4 publications

Effect of Socioeconomic Status on Propensity to Change Risk Behaviors Following Myocardial Infarction: Implications for Healthy Lifestyle Medicine

Effect of Socioeconomic Status on Propensity to Change Risk Behaviors Following Myocardial Infarction: Implications for Healthy Lifestyle Medicine

Biatrial appendages clots in a rheumatic heart disease patient – an important lesson to remember

Left Atrial Appendage Segmentation Using Fully Convolutional Neural Networks and Modified Three-Dimensional Conditional Random Fields

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