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11/24/2022 8:01:57 AM | Browse: 12 | Download: 0
Publication Name World Journal of Cardiology
Manuscript ID 78107
Country/Territory United States
2022-06-15 18:59
Peer-Review Started
2022-06-15 19:02
To Make the First Decision
Return for Revision
2022-08-01 09:30
2022-09-18 01:14
Second Decision
2022-10-18 03:28
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2022-10-18 16:57
Articles in Press
2022-10-18 16:57
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript
2022-11-17 14:30
Publish the Manuscript Online
2022-11-24 08:01
ISSN 1949-8462 (online)
Open Access This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Copyright © The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website http://www.wjgnet.com
Category Cardiac and Cardiovascular Systems
Manuscript Type Retrospective Cohort Study
Article Title Risk stratification of patients who present with chest pain and have normal troponins using a machine learning model
Manuscript Source Unsolicited Manuscript
All Author List Muhammad Shafiq, Diego Robles Mazzotti and Cheryl Gibson
Author(s) ORCID Number
Muhammad Shafiq http://orcid.org/0000-0002-0717-1764
Diego Robles Mazzotti http://orcid.org/0000-0003-3924-9199
Cheryl Gibson http://orcid.org/0000-0002-4025-8845
Funding Agency and Grant Number
Corresponding Author Muhammad Shafiq, MD, Assistant Professor, Division of General and Geriatric Medicine, Department of Internal Medicine, University of Kansas Medical Center, 4000 Cambridge Street, 6040 Delp & Mail Stop 1020, Kansas City, KS 66160, United States. mshafiq@kumc.edu
Key Words Machine learning; Chest pain; Risk stratification; Risk factors; Cardiac stress test; Cardiac catheterization
Core Tip For patients with chest pain, current stratification tools result in unwarranted investigations due to low (13.0%-17.5%) positive predictive values (PPVs). This retrospective cohort study aimed to create a machine learning model (MLM) for risk stratification of patients with chest pain with a better PPV. Demographics, coronary artery disease history, hypertension, hyperlipidemia, diabetes mellitus, chronic kidney disease, obesity, and smoking were the covariates. The XGBoost MLM achieved a PPV of 24.33% for an abnormal cardiac stress test, which is better than current stratification tools. This model highlights the potential use of MLMs in clinical decision-making.
Publish Date 2022-11-24 08:01
Citation Shafiq M, Mazzotti DR, Gibson C. Risk stratification of patients who present with chest pain and have normal troponins using a machine learning model. World J Cardiol 2022; 14(10): 565-575
URL https://www.wjgnet.com/1949-8462/full/v14/i11/565.htm
DOI https://dx.doi.org/10.4330/wjc.v14.i11.565
Full Article (PDF) WJC-14-565.pdf
Full Article (Word) WJC-14-565.docx
Manuscript File 78107_Auto_Edited-LM.docx
Answering Reviewers 78107-Answering reviewers.pdf
Audio Core Tip 78107-Audio core tip.mp3
Biostatistics Review Certificate 78107-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form 78107-Conflict-of-interest statement.pdf
Copyright License Agreement 78107-Copyright license agreement.pdf
Signed Informed Consent Form(s) or Document(s) 78107-Informed consent statement.pdf
Institutional Review Board Approval Form or Document 78107-Institutional review board statement.pdf
Supplementary Material 78107-Supplementary material.pdf
Peer-review Report 78107-Peer-review(s).pdf
Scientific Misconduct Check 78107-Bing-Wang JJ-2.png
Scientific Editor Work List 78107-Scientific editor work list.pdf