Wesley Chorney, John Hinchion
Abstract
Post-operative outcomes of cardiovascular surgery vary greatly among patients for a variety of reasons. While the specific reasons are often multifactorial and complex, certain machine learning methods are promising ways to both estimate mortality after surgery and elucidate important factors linked with mortality.
Introduction
The post-operative outcomes of cardiovascular surgery vary greatly, ranging from full recoveries to debilitating complications and death. Accurate understanding of the risks and benefits of surgery is therefore useful to both patients and physicians — for patients, this facilitates informed consent.
Materials and methods
Data used in this manuscript are from the MIMIC-IV dataset [20,21], available freely from PhysioNet [22]. The dataset was accessed on 2025-04-12, and no information that could identify individual participants was accessed prior to, during, or after this date.
Results
Each model is trained and tested using stratified 5-fold cross validation. Data from the training fold was downsampled in order to achieve a more balanced training dataset, and was tested on the imbalanced test fold.
Discussion
RDW and poor post-operative outcomes, particularly mortality, are well-known to be correlated in both non-cardiac [33,34] and cardiac [35] surgery. This is well reflected in the model, as both the minimum measured and maximum measured pre-operative RDW have large positive coefficients.
Conclusion
Pre-operative laboratory values are commonly recorded prior to surgery. However, whether they are being fully utilized with respect to gauging whether a patient is suitable for surgery is unclear. While the STS score and the EuroSCORE II do look at some of these values, we demonstrated that simple machine learning models could be used to generate a prediction of all-cause one-year mortality post-cardiovascular surgery.
Citation: Chorney W, Hinchion J (2026) Risk assessment in cardiac surgery: Exploring machine learning and laboratory indices as adjunctive tools. PLoS One 21(2): e0335289. https://doi.org/10.1371/journal.pone.0335289
Editor: Redoy Ranjan, James Cook University Hospital, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
Received: October 8, 2025; Accepted: January 4, 2026; Published: February 5, 2026
Copyright: © 2026 Chorney, Hinchion. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data cannot be shared publicly because it is derived from the MIMIC-IV dataset, which requires training before access. Access to the dataset is granted via PhysioNet for researchers who successfully complete the training. The data may be accessed from https://physionet.org/content/mimiciv/3.1/.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
