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Acute Coronary Syndrome (ACS) due to Coronary Artery Embolism in a Patient with Atrial Fibrillation.

ABSTRACT: Acute coronary syndrome (ACS) secondary to a coronary embolism is an unusual occurrence, yet an important consideration given the difficult diagnosis. We report a case of a 69-year-old male with a medical history of paroxysmal atrial fibrillation who presented with chest pain and shortness of breath. A coronary angiogram was significant for three focal transluminal and translucent areas in the ostial, mid, and distal circumflex artery consistent with embolic disease. The patient was subsequently managed medically with anticoagulation. Despite being a relatively rare entity, thromboembolism into the coronary arteries can provoke an acute myocardial infarction, with atrial fibrillation being the most common risk factor. Treatment modalities for ACS secondary to thromboembolism include stent placement, intracoronary thrombolysis, and thrombus aspiration.

SUBMITTER: Daoud H

PROVIDER: S-EPMC6811972 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Acute Coronary Syndrome (ACS) due to Coronary Artery Embolism in a Patient with Atrial Fibrillation.

Daoud Hussein H Abugroun Ashraf A Erramilli Shruti S Kumar Surender S

Case reports in cardiology 20191010

Acute coronary syndrome (ACS) secondary to a coronary embolism is an unusual occurrence, yet an important consideration given the difficult diagnosis. We report a case of a 69-year-old male with a medical history of paroxysmal atrial fibrillation who presented with chest pain and shortness of breath. A coronary angiogram was significant for three focal transluminal and translucent areas in the ostial, mid, and distal circumflex artery consistent with embolic disease. The patient was subsequently ...[more]

PMID: 31687218

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Project description:ObjectiveAtrial fibrillation (AF) is one of the most common complications of acute coronary syndrome (ACS) patients. Possible risk factors related to new-onset AF (NOAF) in ACS patients have been reported in some studies, and several prediction models have been established. However, the predictive power of these models was modest and lacked independent validation. The aim of this study is to define risk factors of NOAF in patients with ACS during hospitalization and to develop a prediction model and nomogram for individual risk prediction.MethodsRetrospective cohort studies were conducted. A total of 1535 eligible ACS patients from one hospital were recruited for model development. External validation was performed using an external cohort of 1635 ACS patients from another hospital. The prediction model was created using multivariable logistic regression and validated in an external cohort. The discrimination, calibration, and clinical utility of the model were evaluated, and a nomogram was constructed. A subgroup analysis was performed for unstable angina (UA) patients.ResultsDuring hospitalization, the incidence of NOAF was 8.21% and 6.12% in the training and validation cohorts, respectively. Age, admission heart rate, left atrial diameter, right atrial diameter, heart failure, brain natriuretic peptide (BNP) level, less statin use, and no percutaneous coronary intervention (PCI) were independent predictors of NOAF. The AUC was 0.891 (95% CI: 0.863-0.920) and 0.839 (95% CI: 0.796-0.883) for the training and validation cohort, respectively, and the model passed the calibration test (P > 0.05). The clinical utility evaluation shows that the model has a clinical net benefit within a certain range of the threshold probability.ConclusionA model with strong predictive power was constructed for predicting the risk of NOAF in patients with ACS during hospitalization. It might help with the identification of ACS patients at risk and early intervention of NOAF during hospitalization.

Dataset Information

Acute Coronary Syndrome (ACS) due to Coronary Artery Embolism in a Patient with Atrial Fibrillation.

Publications

Acute Coronary Syndrome (ACS) due to Coronary Artery Embolism in a Patient with Atrial Fibrillation.

Similar Datasets

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