左主干暨冠状动脉分叉病变峰会，CBS2019 - CBS 2019

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General Information

Left Main & Coronary Bifurcation Summit (CBS) was founded in 2006 as a think tank dedicated to the treatment of left main and coronary bifurcation lesions. CBS aims to improve cardiological care by refreshing fundamental researches, clinical trials, yearly published guidelines, emerged ideas and case-based discussion.

Association and Affiliations
- Organized by
  
  Nanjing Heart Center
  
  Asian Bifurcation Club (ABC)
  
  Nanjing First Hospital, Nanjing Medical University
- Co-organized by
  
  Chinese Society of Cardiology (CSC)
  
  Capacity Building and Continuing Education Center,National Health and Family Planning Commission (CBCEC)
  
  American Society for Cardiovascular Angiography and Interventions (SCAI)
  
  Asian Heart Society (AHS)
Contact Us
- Registration Administration of Domestic Representatives
  
  Hongjuan Peng : +86-13913895477
  
  Haimei Xu +86-13914736846
  
  cbsnj@cbsmd.cn
- Registration Administration of Overseas Representatives
  
  Ling Lin : +86 (25) 52271398
  
  +86-13951884596
  
  cbs@cbsmd.cn
- Management of Challenging Cases
  
  Yingying Zhao :+86-13815408517
  
  sub@cbsmd.cn
Address

CBS2019 Congress Secretariat, Nanjing First Hospital, Building No.9 68# Changle Road, Nanjing, China

210006
FACULTY
FACULTY

General Chairman
Congress Chairman
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Education Center

Education Center

• Online Education (For more customized user interface with self-defined collection of research papers and education resources.)

• CBS Education Resource

• CBSMD Scientific Library
SCIENTIFIC LIBRARY

SCIENTIFIC LIBRARY

Research Paper

> Acute Coronary Syndrom

> ASCVD Prevention

> Bifurcation Stenting

> Cardio-Oncology

> Congestive Heart Failure

> DAPT Duration

> Drug Coated Balloon

> Fractional Flow Reserve

> IVUS Guidance

> Optical Coherence Tomography

> Pulmonary Hypertension

> Rotational Atherectomy

> Shear Stress

> Stenting Left Main

> Transcatheter Aortic Valve Replacement

> Percutaneous LAA Occlusion

> Mitral/Tricuspid Valvular Disease

> Other Relevant Articles

Recommended Paper

Management of Acute Myocardial Infarction During the COVID-19 Pandemic

COVID-19 and Thrombotic or Thromboembolic Disease: Implications for Prevention, Antithrombotic Therapy, and Follow-up

CSC Expert Consensus on Principles of Clinical Management of Patients with Severe Emergent Cardiovascular Diseases during the COVID-19 Epidemic

ACC Clinical Bulletin Focuses on Cardiac Implications of Coronavirus (COVID-19)

HOME | SCIENTIFIC LIBRARY | Congestive Heart Failure

Congestive Heart Failure

SPECT and PET in ischemic heart failure Novel percutaneous interventional therapies in heart failure with preserved ejection fraction: an integrative review Nitrosative stress drives heart failure with preserved ejection fraction Can We Use the Intrinsic Left Ventricular Delay (QLV) to Optimize the Pacing Configuration for Cardiac Resynchronization Therapy With a Quadripolar Left Ventricular Lead? Progression of Device-Detected Subclinical Atrial Fibrillation and the Risk of Heart Failure HFpEF: From Mechanisms to Therapies Heart Failure With Preserved Ejection Fraction in the Young Functional Impairment and Medication Burden in Adults With Heart Failure

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Review Article2020 Jul 16;229:1-17.

JOURNAL：Am Heart J . Article Link

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure

CR Olsen, RJ Mentz, KJ Anstrom et al. Keywords: machine learning; artificial intelligence;

ABSTRACT

Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.

Abstract

Recommended Article

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure

ABSTRACT