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(11): 565-575 [PMID: 36483764 DOI: 10.4330/wjc.v14.i11.565]
Corresponding Author of This Article
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
Research Domain of This Article
Cardiac & Cardiovascular Systems
Article-Type of This Article
Retrospective Cohort Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
World J Cardiol. Nov 26, 2022; 14(11): 565-575 Published online Nov 26, 2022. doi: 10.4330/wjc.v14.i11.565
Risk stratification of patients who present with chest pain and have normal troponins using a machine learning model
Muhammad Shafiq, Diego Robles Mazzotti, Cheryl Gibson
Muhammad Shafiq, Cheryl Gibson, Division of General and Geriatric Medicine, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS 66160, United States
Diego Robles Mazzotti, Division of Medical Informatics & Division of Pulmonary Critical Care and Sleep Medicine, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS 66160, United States
Author contributions: Shafiq M was involved in all aspects of this study, including but not limited to study design, data collection, data analyses, and writing of the abstract and manuscript; Mazzotti DR was involved in study design, data collection, and data analyses; Gibson CA assisted in writing the abstract and manuscript.
Institutional review board statement: Institutional Review Board approval was not required because the data was de-identified.
Informed consent statement: In accordance with the retrospective design of the study and de-identified data, no informed consent was required.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: All relevant data have been provided in this article. No additional data are available.
STROBE statement: The authors have read the STROBE Statement – a checklist of items, and the manuscript was prepared and revised according to the STROBE Statement – a checklist of items.
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: https://creativecommons.org/Licenses/by-nc/4.0/
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
Received: June 15, 2022 Peer-review started: June 15, 2022 First decision: August 1, 2022 Revised: September 18, 2022 Accepted: October 18, 2022 Article in press: October 18, 2022 Published online: November 26, 2022 Processing time: 161 Days and 13 Hours
ARTICLE HIGHLIGHTS
Research background
Risk stratification tools exist for patients presenting with chest pain to the emergency room and have achieved the recommended negative predictive value (NPV) of 99%. However, the current stratification tools result in unnecessary clinical interventions due to a low positive predictive value (PPV).
Research motivation
Healthcare costs are astronomical in the United States, including for patients who present with chest pain. These costs emphasize the need for a better stratification tool to safely identify patients who present with chest pain for early discharge without unnecessary testing.
Research objectives
This study aimed to create a machine learning model (MLM) for risk stratification of chest pain patients with a better PPV while maintaining an NPV of 99%.
Research methods
This retrospective cohort study used demographics, coronary artery disease history, hypertension, hyperlipidemia, diabetes mellitus, chronic kidney disease, obesity, and smoking as the covariates for the prediction of an abnormal cardiac stress test (CST). Binomial regression (BR), random forest, and XGBoost MLMs were used for prediction. Bootstrapping was used for the internal validation of the prediction models.
Research results
The XGBoost MLM had the best PPV of 24.33%, with an NPV of 91.34% for abnormal CST. The BR MLM had a PPV of 21.34% and an NPV of 92.55%. The random forest MLM had a PPV of 20.55% and an NPV of 90.24%.
Research conclusions
The XGBoost MLM provided a better PPV than currently used stratification tools (24.33% vs 13.00%-17.50%). Though the NPV from the XGBoost MLM remained lower than the recommended value of 99%, it highlights the potential use of MLMs in clinical decision-making.
Research perspectives
Data sharing and external validation of the MLMs will be crucial for their recognition and widespread adoption.
