Objective: Acute myocardial infarction due to left main coronary artery occlusion remains catastrophic and mostly fatal due to severe cardiogenic shock and arrhythmia.
Methods: We studied 13 patients undergoing coronary artery bypass grafting for acute myocardial infarction due to left main coronary artery occlusion to clarify the optimal management of these difficult patients.
Results: In-hospital mortality was 46.2% (6/13). Revascularization was achieved by catheter intervention followed by bypass surgery in 7, and bypass surgery alone in 6. Two bypass surgery patients without catheter intervention had collateral flow to the left coronary artery, with the right coronary artery dominant. The time from onset to recanalization in the survival group was significantly shorter than in the early death group.
Conclusions: Emergency intervention to preserve left ventricular function or right coronary artery dominant and collateral blood flow to left coronary arteries is important for improving the prognosis of patients with acute myocardial infarction due to left main coronary artery occlusion. If residual left main coronary artery stenosis is significant or other proximal coronary stenosis exists after catheter intervention, early coronary bypass surgery may improve long-term survival.
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http://dx.doi.org/10.1007/BF02913195 | DOI Listing |
Circ Genom Precis Med
January 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (A.A., L.S.D., E.K.O., R.K.).
Background: While universal screening for Lp(a; lipoprotein[a]) is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a; ARISE), a validated machine learning tool, to health system electronic health records to increase the yield of Lp(a) testing.
View Article and Find Full Text PDFArterioscler Thromb Vasc Biol
January 2025
Department of Pediatrics, Division of Pediatric Infectious Diseases, Guerin Children's, Cedars-Sinai Medical Center, Los Angeles, CA.(P.K.J., M.A., M.N.R.).
The intestinal microbiota influences many host biological processes, including metabolism, intestinal barrier functions, and immune responses in the gut and distant organs. Alterations in its composition have been associated with the development of inflammatory disorders and cardiovascular diseases, including Kawasaki disease (KD). KD is an acute pediatric vasculitis of unknown etiology and the leading cause of acquired heart disease in children in the United States.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, SE-182 88 Stockholm, Sweden.
Aims: A simplified version of the history, electrocardiogram, age, risk factors, troponin (HEART) score, excluding troponin, has been proposed to rule-out major adverse cardiac events (MACEs). Computerized history taking (CHT) provides a systematic and automated method to obtain information necessary to calculate the HEAR score. We aimed to evaluate the efficacy and diagnostic accuracy of CHT in calculating the HEAR score for predicting MACE.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA.
Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.
View Article and Find Full Text PDFHealth Sci Rep
January 2025
Department of Cardiac Surgery, School of Medicine Hamadan University of Medical Sciences Hamadan Iran.
Background And Aim: Coronary artery bypass grafting (CABG) is a key treatment for coronary artery disease, but accurately predicting patient survival after the procedure presents significant challenges. This study aimed to systematically review articles using machine learning techniques to predict patient survival rates and identify factors affecting these rates after CABG surgery.
Methods: From January 1, 2015, to January 20, 2024, a comprehensive literature search was conducted across PubMed, Scopus, IEEE Xplore, and Web of Science.
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