Results 81 to 90 of about 1,256,738 (400)

Machine learning methods for predicting adverse drug events: A systematic review

British Journal of Clinical Pharmacology, EarlyView.
Abstract Predicting adverse drug events (ADEs) in outpatient settings is crucial for improving medication safety, identifying high‐risk patients and reducing health‐care costs. While traditional methods struggle with the complexity of health‐care data, machine learning (ML) models offer improved prediction capabilities; however, their effectiveness in ...
Niaz Chalabianloo, Fatemeh Ahmadi, Mohammad Ali Omrani, Sheikh S. Abdullah, Neda Rostamzadeh, Atefeh Jafari, Lujain Izzedin, Kamran Sedig, Flory T. Muanda   +8 more
wiley   +1 more source

Left Main Coronary Artery Hypoplasia in Elderly

Case Reports in Cardiology, 2016
Congenital anomalies of the coronary artery causing coronary occlusive disease may be of many different types. A 67-year-old woman with no coronary risk factors was referred for coronary angiography with few months’ history of angina.
Selma Kenar Tiryakioglu, Hakan Ozkan, Hakan Bahadir, Osman Tiryakioglu   +3 more
doaj   +1 more source

Coronary microvascular dysfunction equivalent to left main coronary artery disease

Türk Kardiyoloji Derneği Arşivi, 2017
Coronary microvascular dysfunction, also known as cardiac syndrome X, is a clinical syndrome presenting with typical angina and evidence of myocardial ischemia in the absence of flow-limiting stenosis on coronary angiography. Of patients undergoing
Cafer Panç, Mehmet Kocaağa, Onur Erdoğan, Remzi Sarıkaya, Sabahattin Umman   +4 more
doaj   +1 more source

Coronary angiography in patients with acute heart failure: from the KCHF registry

ESC Heart Failure, 2022
Aims Little is known about the characteristics and outcomes of patients who undergo coronary angiography during heart failure (HF) hospitalization, as well as those with coronary stenosis, and those who underwent coronary revascularization.
Yuta Seko, Takefumi Kishimori, Takao Kato, Takeshi Morimoto, Hidenori Yaku, Yasutaka Inuzuka, Yodo Tamaki, Neiko Ozasa, Masayuki Shiba, Erika Yamamoto, Yusuke Yoshikawa, Yugo Yamashita, Takeshi Kitai, Ryoji Taniguchi, Moritake Iguchi, Kazuya Nagao, Takafumi Kawai, Akihiro Komasa, Ryusuke Nishikawa, Yuichi Kawase, Takashi Morinaga, Mamoru Toyofuku, Yutaka Furukawa, Kenji Ando, Kazushige Kadota, Yukihito Sato, Koichiro Kuwahara, Takeshi Kimura, for the KCHF Study Investigators   +28 more
doaj   +1 more source

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry. [PDF]

, 2020
Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time.
Al'Aref, Subhi J, Andreini, Daniele, Baskaran, Lohendran, Bax, Jeroen J, Berman, Daniel S, Budoff, Matthew J, Cademartiri, Filippo, Chang, Hyuk-Jae, Chinnaiyan, Kavitha, Choi, Jung Hyun, Chun, Eun Ju, Conte, Edoardo, de Araújo Gonçalves, Pedro, Gottlieb, Ilan, Gransar, Heidi, Hadamitzky, Martin, Han, Donghee, Kim, Yong-Jin, Kolli, Kranthi K, Lee, Byoung Kwon, Lee, Sang-Eun, Leipsic, Jonathon A, Lin, Fay Y, Maffei, Erica, Marques, Hugo, Min, James K, Narula, Jagat, Pontone, Gianluca, Raff, Gilbert L, Samady, Habib, Shaw, Leslee J, Shin, Sangshoon, Stone, Peter, Sung, Ji Min, van Rosendael, Alexander R, Virmani, Renu   +35 more
core  

Novel X-ray imaging technology enables significant patient dose reduction in interventional cardiology while maintaining diagnostic image quality [PDF]

, 2015
Objectives: The purpose of this study was to quantify the reduction in patient radiation dose during coronary angiography (CA) by a new X-ray technology, and to assess its impact on diagnostic image quality.
Bacher, Klaus, Buytaert, Dimitri, De Pooter, Jan, Drieghe, Benny, Eloot, Liesbeth, Gijs, Thomas, Lapere, Régine, Taeymans, Yvo, Thierens, Hubert, VAN PETEGHEM, SYLVIE   +9 more
core   +1 more source

A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography [PDF]

IEEE Transactions on Medical Imaging, 2018
Various types of atherosclerotic plaque and varying grades of stenosis could lead to different management of patients with a coronary artery disease. Therefore, it is crucial to detect and classify the type of coronary artery plaque, as well as to detect
M. Zreik, R. V. van Hamersvelt, J. Wolterink, T. Leiner, M. Viergever, I. Išgum   +5 more
semanticscholar   +1 more source

A Meta‐Analysis and Simplified Nomenclature for Diagonal Coronary Artery and Ramus Intermedius Across Adult and Pediatric Hearts

Clinical Anatomy, EarlyView.
ABSTRACT Anatomical descriptions of left‐sided oblique coronary branches remain inconsistent, hindering imaging interpretation and surgical planning. To quantify the prevalence, branching patterns and morphometry of the ramus intermedius (RI) and diagonal branches, and propose a unified nomenclature.
Yuqian Dai, Sabrina Constantini, Michael J. Montalbano, Marios Loukas   +3 more
wiley   +1 more source

640-SLICE CT CORONARY ANGIOGRAPHY IN THE EARLY POSTOPERATIVE PERIOD AFTER CIRCUMFLEX ARTERY STENTING IN A PATIENT WITH ANOMALOUS ORIGIN OF THE RIGHT CORONARY ARTERY FROM THE LEFT SINUS OF VALSALVA

Вестник рентгенологии и радиологии, 2017
The paper describes a clinical observation showing the capabilities of three-dimensional dynamic 640-slice CT coronary angiography in the postoperative period after coronary artery stenting and in the presence of anomalous origin of coronary arteries ...
S. K. Ternovoy, N. S. Serova, O. S. Kondrashina, S. A. Kondrashin, V. V. Koblikov, S. A. Suchkova   +5 more
doaj   +1 more source

Quantification of atherosclerotic plaque volume in coronary arteries by computed tomographic angiography in subjects with and without diabetes. [PDF]

, 2020
BackgroundDiabetes mellitus (DM) is considered a cardiovascular risk factor. The aim of this study was to analyze the prevalence and volume of coronary artery plaque in patients with diabetes mellitus (DM) vs. those without DM.MethodsThis study recruited
An, Yun-Qiang, Budoff, Matthew J, Gao, Yang, Hou, Zhi-Hui, Li, Zhen-Nan, Lu, Bin, Yin, Wei-Hua   +6 more
core   +1 more source