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Your search keyword '"Orlev, Amir"' showing total 11 results
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11 results on '"Orlev, Amir"'

1. Culprit Lesion Coronary Intervention Before Complete Angiography in ST-Elevation Myocardial Infarction

Author

Levi, Nir, primary, Wolff, Rafael, additional, Jubeh, Rami, additional, Shuvy, Mony, additional, Steinmetz, Yoed, additional, Perel, Nimrod, additional, Maller, Tomer, additional, Amsalem, Itshak, additional, Hitter, Rafael, additional, Asher, Elad, additional, Turyan, Anna, additional, Karmi, Mohammad, additional, Orlev, Amir, additional, Dratva, Dmitry, additional, Khoury, Zahi, additional, Hasin, Tal, additional, Wolak, Arik, additional, Glikson, Michael, additional, and Dvir, Danny, additional

Published
2024
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3. Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction.

Author

Dadon, Ziv, Rav Acha, Moshe, Orlev, Amir, Carasso, Shemy, Glikson, Michael, Gottlieb, Shmuel, and Alpert, Evan Avraham

Subjects
MEDICAL students, ARTIFICIAL intelligence, VENTRICULAR ejection fraction, PATIENT readmissions, HOSPITAL patients
Abstract

Introduction: Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making. Aim: To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes. Methods: Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020. Results: The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083–6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure. Conclusions: AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Outcomes among patients admitted for non-ST-segment myocardial infarction in the pre-pandemic and pandemic COVID-19 era: Israel Nationwide study

Author

Asher, Elad, primary, Fardman, Alexander, additional, Shmueli, Hezzy, additional, Orvin, Katia, additional, Oren, Daniel, additional, Kofman, Natalia, additional, Mohsen, Jameel, additional, Moady, Gassan, additional, Taha, Louay, additional, Rubinshtein, Ronen, additional, Azriel, Osherov, additional, Efraim, Roi, additional, Saleem, Dabbah, additional, Taieb, Philippe, additional, Ben-assa, Eyal, additional, Cohen, Tal, additional, Klempfner, Robert, additional, Orlev, Amir, additional, Beigel, Roy, additional, Segev, Amit, additional, and Matetzky, Shlomi, additional

Published
2022
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