Predicting Successful Chronic Total Occlusion Crossing With Primary Antegrade Wiring Using Machine Learning.

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Bibliographic Details
Title: Predicting Successful Chronic Total Occlusion Crossing With Primary Antegrade Wiring Using Machine Learning.
Authors: Rempakos A; Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA., Alexandrou M; Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA., Mutlu D; Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA., Kalyanasundaram A; The Promed Hospital, Chennai, India., Ybarra LF; London Health Sciences Centre, Western University, London, Ontario, Canada., Bagur R; London Health Sciences Centre, Western University, London, Ontario, Canada., Choi JW; Texas Health Presbyterian Hospital, Dallas, Texas, USA., Poommipanit P; University Hospitals, Case Western Reserve University, Cleveland, Ohio, USA., Khatri JJ; Cleveland Clinic, Cleveland, Ohio, USA., Young L; Cleveland Clinic, Cleveland, Ohio, USA., Davies R; WellSpan York Hospital, York, Pennsylvania, USA., Benton S; WellSpan York Hospital, York, Pennsylvania, USA., Gorgulu S; Biruni University Medical School, Istanbul, Turkey., Jaffer FA; Massachusetts General Hospital, Boston, Massachusetts, USA., Chandwaney R; Oklahoma Heart Institute, Tulsa, Oklahoma, USA., Jaber W; Emory University Hospital Midtown, Atlanta, Georgia, USA., Rinfret S; Emory University Hospital Midtown, Atlanta, Georgia, USA., Nicholson W; Emory University Hospital Midtown, Atlanta, Georgia, USA., Azzalini L; Division of Cardiology, Department of Medicine, University of Washington, Seattle, Washington, USA., Kearney KE; Division of Cardiology, Department of Medicine, University of Washington, Seattle, Washington, USA., Alaswad K; Henry Ford Cardiovascular Division, Detroit, Michigan, USA., Basir MB; Henry Ford Cardiovascular Division, Detroit, Michigan, USA., Krestyaninov O; Meshalkin Novosibirsk Research Institute, Novosibirsk, Russia., Khelimskii D; Meshalkin Novosibirsk Research Institute, Novosibirsk, Russia., Abi-Rafeh N; North Oaks Health System, Hammond, Louisiana, USA., Elguindy A; Aswan Heart Center, Magdi Yacoub Foundation, Cairo, Egypt., Goktekin O; Memorial Bahcelievler Hospital, Istanbul, Turkey., Aygul N; Selcuk University, Konya, Turkey., Rangan BV; Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA., Mastrodemos OC; Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA., Al-Ogaili A; Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA., Sandoval Y; Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA., Burke MN; Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA., Brilakis ES; Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA. Electronic address: esbrilakis@gmail.com.
Source: JACC. Cardiovascular interventions [JACC Cardiovasc Interv] 2024 Jul 22; Vol. 17 (14), pp. 1707-1716. Date of Electronic Publication: 2024 Jul 03.
Publication Type: Journal Article; Multicenter Study; Validation Study
Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 101467004 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1876-7605 (Electronic) Linking ISSN: 19368798 NLM ISO Abbreviation: JACC Cardiovasc Interv Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1876-7605
DOI:10.1016/j.jcin.2024.04.043