A deep learning model to guide personalized mechanical circulatory support use in cardiogenic shock patients undergoing PCI

Authors

Amit P Amin, Rush University
Richard G Bach, Washington University School of Medicine in St. Louis
Darren C Tsang, Rush University
Weili S Zheng, Rush University
Raed A Qarajeh, Rush University
Emmanouil S Brilakis, Abbott Northwestern Hospital
Hemant Kulkarni, M&H Research, LLC

Journal

JACC: Advances

Publication Date

1-1-2026

Volume

5

Issue

1

First Page

102379

Document Type

Open Access Publication

DOI

10.1016/j.jacadv.2025.102379

Rights and Permissions

Amin AP, Bach RG, Tsang DC, Zheng WS, Qarajeh RA, Brilakis ES, Kulkarni H. A Deep Learning Model to Guide Personalized Mechanical Circulatory Support Use in Cardiogenic Shock Patients Undergoing PCI. JACC Adv. 2026 Jan;5(1):102379. doi: 10.1016/j.jacadv.2025.102379. © 2026 Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Recommended Citation

Amin, Amit P; Bach, Richard G; Tsang, Darren C; Zheng, Weili S; Qarajeh, Raed A; Brilakis, Emmanouil S; and Kulkarni, Hemant, "A deep learning model to guide personalized mechanical circulatory support use in cardiogenic shock patients undergoing PCI." JACC: Advances. 5, 1. 102379 (2026).
https://digitalcommons.wustl.edu/oa_4/6309

Department

ICTS (Institute of Clinical and Translational Sciences)

Additional Links

Supplemental material is available for this article at publisher site.