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Articles Published Processes
11/24/2022 8:01:57 AM | Browse: 392 | Download: 928
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Received |
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2022-06-15 18:59 |
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Peer-Review Started |
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2022-06-15 19:02 |
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To Make the First Decision |
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Return for Revision |
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2022-08-01 09:30 |
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Revised |
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2022-09-18 01:14 |
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Second Decision |
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2022-10-18 03:28 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2022-10-18 16:57 |
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Articles in Press |
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2022-10-18 16:57 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2022-11-17 14:30 |
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Publish the Manuscript Online |
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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
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Cardiac & 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
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Muhammad Shafiq, Diego Robles Mazzotti and Cheryl Gibson |
| ORCID |
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| Funding Agency and Grant Number |
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| 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 |
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