Search

Your search keyword '"Orlev, Amir"' showing total 25 results
Start Over Author "Orlev, Amir"
25 results on '"Orlev, Amir"'

2. 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
Full Text
View/download PDF

4. 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
Full Text
View/download PDF

8. List of Contributors

Author

Abela, George S., primary, Abouzaki, Nayef A., additional, Ahmadjee, Abdulmohsin, additional, Al-Salaimeh, Ayman, additional, Aladdin, Mohammed, additional, Albuquerque, Felipe N., additional, Ariëns, Robert A.S., additional, Asher, Elad, additional, Azadani, Ali N., additional, Badimon, Lina, additional, Baker, Stephen R., additional, Banerjee, Subhash, additional, Bannish, Brittany E., additional, Bavry, Anthony A., additional, Beinart, Roy, additional, Ben-Aicha, Soumaya, additional, Brilakis, Emmanouil S., additional, Brosnan, Rhoda B., additional, Chatterjee, Arka, additional, Cioni, Gabriele, additional, Clapp, Brian, additional, Danenberg, Haim D., additional, de Jaegere, P.P.T., additional, de Marchena, Eduardo, additional, De Silva, Kalpa, additional, Dvir, Danny, additional, Elgendy, Islam Y., additional, Eliaz, Ran, additional, Ferro, Albert, additional, Finn, Aloke V., additional, Fishman, Joel E., additional, Fuksbrumer, Moshe S., additional, Ghodsizad, Ali, additional, Glikson, Michael, additional, Goldstein, Larry B., additional, Gorla, Sudheer, additional, Grupper, Avishai, additional, Guttmann, Oliver P., additional, Harris, Lindsay, additional, Havlichek, Daniel, additional, Hawk, Christopher, additional, Henry, Timothy, additional, Hirschl, David A., additional, Hoffman, Ron, additional, Humphrey, Holly, additional, Jinnouchi, Hiroyuki, additional, Jones, Gregory K., additional, Jones, Daniel A., additional, Jovin, Ion S., additional, Karacsonyi, Judit, additional, Kelley, Michael A., additional, Kereiakes, Dean J., additional, Harrison, J. Kevin, additional, Kornowski, Ran, additional, Kramer, Mordechai R., additional, Lamichhane, Madhab, additional, Landi, Antonio, additional, Layne, Kerry, additional, Leesar, Massoud A., additional, Leiderman, Karin, additional, Lewis, Neil P., additional, Ligthart, Jurgen, additional, Lishner, Michael, additional, Macias, Alejandro E., additional, Magarakis, Michael, additional, Mahmoud, Ahmed N., additional, Mahtta, Dhruv, additional, Mankad, Anit, additional, Martinez, Claudia A., additional, Mendirichaga, Rodrigo, additional, Michalets, Elizabeth, additional, Michalove, Benjamin, additional, Mitra, Subhashis, additional, Nagalla, Srikanth, additional, Napodano, Massimo, additional, O’Kane, Peter, additional, Onishi, Takayuki, additional, Onishi, Yuko, additional, Orlev, Amir, additional, Palmisano, Ada M., additional, Passacquale, Gabriella, additional, Peek, Brian T., additional, Perera, Divaka, additional, Pineda, Andres M., additional, Rao, Sunil V., additional, Rathod, Krishnaraj S., additional, Regar, Evelyn, additional, Ren, Claire, additional, Rosenschein, Uri, additional, Ryan, Matthew J., additional, Salehi, Negar, additional, Salerno, Tomas A., additional, Sandhu, Satinder K., additional, Sarembock, Ian J., additional, Sawey, Edward J., additional, Segev, Amit, additional, Shammas, Nicolas W., additional, Shen, Yu-Min, additional, Shiyovich, Arthur, additional, Shreenivas, Satya S., additional, Smith, James, additional, Smith, Elliot J., additional, Stumpf, Emily, additional, Topaz, Allyne, additional, Ungi, Imre, additional, Unterman, Avraham, additional, Vilahur, Gemma, additional, Virmani, Renu, additional, Watts, Thomas E., additional, Wood, Emily E., additional, Younis, Arwa, additional, and Zampolin, Richard L., additional

Published
2018
Full Text
View/download PDF

10. 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
Full Text
View/download PDF

12. Prevalence, Predictors, and Outcomes of Patients With ST-Elevation Myocardial Infarction and Angiographically Significant Coronary Artery Disease of Non–Infarct-Related Artery

Author

Levi, Nir, primary, Dadon, Ziv, additional, Steinmetz, Yoed, additional, Perel, Nimrod, additional, Orlev, Amir, additional, Asher, Elad, additional, Jubeh, Rami, additional, Farkash, Rivka, additional, Gottlieb, Shmuel, additional, Almagor, Yaron, additional, Dvir, Danny, additional, Glikson, Michael, additional, and Wolff, Rafael, additional

Published
2022
Full Text
View/download PDF

15. Cardiac arrhythmias amongst hospitalised Coronavirus 2019 (COVID‐19) patients: Prevalence, characterisation, and clinical algorithm to classify arrhythmic risk

Author

Rav‐Acha, Moshe, primary, Orlev, Amir, additional, Itzhaki, Itay, additional, Zimmerman, Shmuel F., additional, Fteiha, Bashar, additional, Bohm, Davina, additional, Kurd, Ramzi, additional, Samuel, Tal Y., additional, Asher, Elad, additional, Helviz, Yigal, additional, Glikson, Michael, additional, and Michowitz, Yoav, additional

Published
2021
Full Text
View/download PDF

17. Cardiac arrhythmias among hospitalized Coronavirus 2019 (COVID-19) patients: prevalence, characterization, and clinical algorithm to classify arrhythmic risk

Author

Acha, Moshe Rav, primary, Orlev, Amir, additional, Itzhaki, Itay, additional, Zimmerman, Shmuel F, additional, Fteiha, Bashar, additional, Bohm, Davina, additional, Kurd, Ramzi, additional, Samuel, Tal Y, additional, Asher, Elad, additional, Helviz, Yigal, additional, Glikson, Michael, additional, and Michowitz, Yoav, additional

Published
2020
Full Text
View/download PDF

22. QTc prolongation among hydroxychloroquine sulphate‐treated COVID‐19 patients: An observational study.

Author

Fteiha, Bashar, Karameh, Hani, Kurd, Ramzi, Ziff‐Werman, Batsheva, Feldman, Itamar, Bnaya, Alon, Einav, Sharon, Orlev, Amir, and Ben‐Chetrit, Eli

Abstract

Background: The liberal administration of hydroxychloroquine‐sulphate (HCQ) to COVID‐19 patients has raised concern regarding the risk of QTc prolongation and cardiac arrhythmias, particularly when prescribed with azithromycin. We evaluated the incidence of QTc prolongation among moderately and severely ill COVID‐19 patients treated with HCQ and of the existence of concomitant alternative causes. Methods: All COVID‐19 patients treated with HCQ (between Mar 1 and Apr 14, 2020) in a tertiary medical centre were included. Clinical characteristics and relevant risk factors were collected from the electronic medical records. Individual patient QTc intervals were determined before and after treatment with HCQ. The primary outcome measure sought was a composite end point comprised of either an increase ≥60 milliseconds (ms) in the QTc interval compared with pre‐treatment QTc, and/or a maximal QTc interval >500 ms Results: Ninety patients were included. Median age was 65 years (IQR 55‐75) and 57 (63%) were male. Thirty‐nine patients (43%) were severely or critically ill. Hypertension and obesity were common (n = 23 each, 26%). QTc prolongation evolved in 14 patients (16%). Age >65 years, congestive heart failure, severity of disease, C‐reactive protein level, hypokalaemia and furosemide treatment, were all associated with QTc prolongation. Adjusted analysis showed that QTc prolongation was five times more likely with hypokalaemia [OR 5, (95% CI, 1.3‐20)], and three times more likely with furosemide treatment [OR 3 (95% CI, 1.01‐13.7)]. Conclusion: In patients treated with HCQ, QTc prolongation was associated with the presence of traditional risk factors such as hypokalaemia and furosemide treatment. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results