Predicting risk of sudden cardiac death in patients with cardiac sarcoidosis using multimodality imaging and personalized heart modeling in a multivariable classifier-Reference-Cited by-同舟云学术

Predicting risk of sudden cardiac death in patients with cardiac sarcoidosis using multimodality imaging and personalized heart modeling in a multivariable classifier

Published:2021-07-30 Issue:31 Volume:7 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Shade Julie K.¹²^ORCID,Prakosa Adityo¹²^ORCID,Popescu Dan M.²³^ORCID,Yu Rebecca¹²^ORCID,Okada David R.⁴^ORCID,Chrispin Jonathan⁴^ORCID,Trayanova Natalia A.¹²⁴^ORCID

Affiliation:

1. Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.

2. Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.

3. Department of Applied Math and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.

4. Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 733 North Broadway, Baltimore, MD 21205, USA.

Abstract

Combining mechanistic modeling and machine learning, a multivariable sudden cardiac death predictor outperforms clinical metrics.

Funder

National Institutes of Health

National Science Foundation Graduate Research Fellowship

Johns Hopkins University Undergraduate Research Fellowship

Johns Hopkins University Robert E Meyerhoff Professorship

Lowenstein Foundation

Publisher

American Association for the Advancement of Science (AAAS)