Retrospective Cohort Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
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
Abstract
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, due to low positive predictive value (PPV), current stratification tools result in unwarranted investigations such as serial laboratory tests and cardiac stress tests (CSTs).

AIM

To create a machine learning model (MLM) for risk stratification of chest pain with a better PPV.

METHODS

This retrospective cohort study used de-identified hospital data from January 2016 until November 2021. Inclusion criteria were patients aged > 21 years who presented to the ER, had at least two serum troponins measured, were subsequently admitted to the hospital, and had a CST within 4 d of presentation. Exclusion criteria were elevated troponin value (> 0.05 ng/mL) and missing values for body mass index. The primary outcome was abnormal CST. Demographics, coronary artery disease (CAD) history, hypertension, hyperlipidemia, diabetes mellitus, chronic kidney disease, obesity, and smoking were evaluated as potential risk factors for abnormal CST. Patients were also categorized into a high-risk group (CAD history or more than two risk factors) and a low-risk group (all other patients) for comparison. Bivariate analysis was performed using a χ2 test or Fisher’s exact test. Age was compared by t test. Binomial regression (BR), random forest, and XGBoost MLMs were used for prediction. Bootstrapping was used for the internal validation of prediction models. BR was also used for inference. Alpha criterion was set at 0.05 for all statistical tests. R software was used for statistical analysis.

RESULTS

The final cohort of the study included 2328 patients, of which 245 (10.52%) patients had abnormal CST. When adjusted for covariates in the BR model, male sex [risk ratio (RR) = 1.52, 95% confidence interval (CI): 1.2-1.94, P < 0.001)], CAD history (RR = 4.46, 95%CI: 3.08-6.72, P < 0.001), and hyperlipidemia (RR = 3.87, 95%CI: 2.12-8.12, P < 0.001) remained statistically significant. Incidence of abnormal CST was 12.2% in the high-risk group and 2.3% in the low-risk group (RR = 5.31, 95%CI: 2.75-10.24, P < 0.001). The XGBoost model had the best PPV of 24.33%, with an NPV of 91.34% for abnormal CST.

CONCLUSION

The XGBoost MLM achieved a PPV of 24.33% for an abnormal CST, which is better than current stratification tools (13.00%-17.50%). This highlights the beneficial potential of MLMs in clinical decision-making.

Keywords: 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.