Retrospective Cohort Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Feb 27, 2024; 16(2): 193-210
Published online Feb 27, 2024. doi: 10.4254/wjh.v16.i2.193
Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study
Jonathan Soldera, Leandro Luis Corso, Matheus Machado Rech, Vinícius Remus Ballotin, Lucas Goldmann Bigarella, Fernanda Tomé, Nathalia Moraes, Rafael Sartori Balbinot, Santiago Rodriguez, Ajacio Bandeira de Mello Brandão, Bruno Hochhegger
Jonathan Soldera, Post Graduate Program at Acute Medicine and Gastroenterology, University of South Wales, Cardiff CF37 1DL, United Kingdom
Jonathan Soldera, Bruno Hochhegger, Postgraduate Program in Pathology, Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre 90050-170, Brazil
Leandro Luis Corso, Fernanda Tomé, Nathalia Moraes, Department of Engineering, Universidade de Caxias do Sul, Caxias do Sul 95070-560, Brazil
Matheus Machado Rech, Vinícius Remus Ballotin, Lucas Goldmann Bigarella, Rafael Sartori Balbinot, School of Medicine, Universidade de Caxias do Sul, Caxias do Sul 95070-560, Brazil
Santiago Rodriguez, Ajacio Bandeira de Mello Brandão, Postgraduate Program in Hepatology, Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre 90050-170, Brazil
Co-first authors: Jonathan Soldera and Leandro Luis Corso.
Author contributions: Soldera J, Corso LL, Rech MM, Tomé F, and Moraes N substantially contributed to the conception and design of the work, data collection, and drafting of the manuscript; Corso LL; Rech MM are credited with the development of the algorithm upon which the machine learning model relies; Ballotin VR, Bigarella LG, Balbinot RS, and Rodriguez S substantially contributed to data collection and critical revision of the manuscript; Brandão ABM and Hochhegger B were responsible for supervision, manuscript revision, and additional writing; and all authors have reviewed and approved the final version and agreed to be accountable for the work’s integrity.
Institutional review board statement: The study was reviewed and approved for publication by our Institutional Reviewer under protocol no. 07793412.2.3001.5345.
Informed consent statement: The Ethics Committee waived the need for informed consent for this study since it solely utilized data from medical charts without direct patient contact.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The original anonymous dataset is available on request from the corresponding author at jonathansoldera@gmail.com. The code for implementation of the reported pipeline on the present dataset, including data preprocessing, feature engineering, model development, hypermeter optimization, and model assessment, is provided in the GitHub repository, publicly and freely available through the following link: https://github.com/matheus-rech/ML.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-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: Jonathan Soldera, MD, PhD, Instructor, Post Graduate Program at Acute Medicine and Gastroenterology, University of South Wales, Llantwit Rd, Pontypridd, Cardiff CF37 1DL, United Kingdom. jonathansoldera@gmail.com
Received: November 2, 2023
Peer-review started: November 2, 2023
First decision: December 1, 2023
Revised: December 27, 2023
Accepted: February 4, 2024
Article in press: February 4, 2024
Published online: February 27, 2024
Processing time: 116 Days and 14.9 Hours
ARTICLE HIGHLIGHTS
Research background

The landscape of liver transplant (LT) candidates has evolved, with an aging and increasingly morbid population, often linked to metabolic-associated fatty liver disease (MAFLD). MAFLD’s rise as a cause of cirrhosis raises concerns about a subsequent increase in major adverse cardiovascular events (MACE) post-LT, a critical complication negatively impacting prognosis. This study is prompted by the growing incidence of post-LT MACE, particularly within the first 6 months, and the complex interplay of traditional and nontraditional cardiovascular risk factors in this vulnerable population. The prevalence shift toward MAFLD as a leading indication for LT necessitates a thorough pre-LT cardiac assessment, demanding a reconsideration of existing noninvasive strategies’ reliability. The pressing need for an alternative approach to predict post-LT MACE accurately propels the exploration of machine learning as a transformative tool to navigate the challenges posed by conventional models.

Research motivation

Motivating this research is the imperative to address the limitations of current cardiovascular risk stratification models for LT candidates, especially those with end-stage liver disease. Traditional models exhibit constraints related to assumptions of linear relationships and limited variables, leading to unreliable predictions. The inadequacy of existing noninvasive strategies and the absence of effective models for accurate cardiovascular risk stratification in LT candidates underscore the urgency for a paradigm shift. The study is driven by the aspiration to introduce machine learning as an innovative and more effective approach, leveraging its capacity to discern intricate patterns and relationships within datasets. The ultimate goal is to revolutionize risk prediction, enabling clinicians to identify high-risk individuals with precision, thus optimizing patient care strategies.

Research objectives

The primary objective of this study is to assess the feasibility and accuracy of implementing a machine learning model to predict MACE post-LT. Focusing on a specific regional cohort, the study aims to revolutionize risk assessment by moving beyond the limitations of conventional statistical models. Realizing this objective involves scrutinizing the potential of machine learning techniques to forecast post-LT MACE with enhanced precision. By leveraging advanced computational models, the research seeks to provide a comprehensive evaluation of the predictive capabilities, enabling the early identification of individuals at elevated risk. The ultimate significance lies in facilitating early intervention strategies and refining patient care in the context of the evolving landscape of LT candidates.

Research methods

This retrospective cohort study, approved by the Research Ethics Committee, delves into the cardiovascular risks following LT. Employing a comprehensive approach, medical records from Irmandade Santa Casa de Misericórdia de Porto Alegre were scrutinized for patients undergoing their first LT between 2001 and 2011 due to cirrhosis. Rigorous inclusion and exclusion criteria were applied, focusing on patients above 18 years of age with complete records, cardiac evaluation pre-LT, and no retransplantation. Data encompassed pre-LT, perioperative, and post-LT periods, with the primary outcome being in-hospital MACE. Statistical analyses, including frequency, means, standard deviation, Pearson’s χ2 test, and linear model analysis of variance, were executed using R software. The study introduces a machine learning paradigm, leveraging the XGBoost model, known for handling imbalanced datasets. Feature engineering involved a two-step imputation process, incorporating patient demographics, medical history, and cardiac evaluations. Model training incorporated regularization and early-stop techniques, aiming to prevent overfitting. Hyperparameter optimization using the Optuna package and performance evaluation metrics, including area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve, ensured robustness. Calibration, model explanation through Shapley additive explanations values, and adherence to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement further enriched the methodological rigor, ultimately culminating in web deployment and code availability for transparency and accessibility.

Research results

The study involved 662 LT patients, with 82 exclusions based on specific criteria. The final dataset included 537 samples, with 23 in-hospital MACE cases. The XGBoost model demonstrated substantial predictive capability, achieving an AUROC of 0.89. Precision, recall, and F1-score for the negative class were 0.89, 0.80, and 0.84, respectively. The overall incidence of MACE was 4.46%, with observed rates for stroke, new-onset heart failure, severe arrhythmia, and myocardial infarction. The model achieved optimal calibration using the isotonic method with a Brier score of 0.100. Feature importance analysis revealed key predictors, including negative noninvasive cardiac stress testing, use of a nonselective beta-blocker, direct bilirubin levels, blood type O, and dynamic alterations on myocardial perfusion scintigraphy. The findings contribute a valuable machine learning model for predicting post-LT MACE, offering insights into specific risk factors and enhancing precision in identifying at-risk individuals. Remaining challenges involve addressing potential variability in feature impact across patients and further validation in diverse cohorts.

Research conclusions

This study pioneers a novel approach in assessing in-hospital post-LT MACE. The research introduces a machine learning-based risk stratification model, surpassing the predictive performance of existing models, particularly demonstrating an impressive area under the curve of 0.89 using the XGBoost model. The optimized clinical model considers recipient-related factors and provides valuable insights into predicting MACE, crucial for addressing the leading cause of post-LT mortality. The use of machine learning techniques, specifically XGBoost, brings substantial improvements over traditional models, enhancing risk stratification accuracy. This study highlights the importance of comprehensive pre-LT evaluation, considering a wide array of cardiovascular risk factors.

Research perspectives

Future research should focus on refining and expanding the machine learning model’s application, considering external validation in diverse patient populations and healthcare settings. Addressing ethical implications and ensuring transparency in model application are imperative for integrating machine learning predictions into clinical practice. The study suggests the need for continued exploration into the biological significance of identified predictors, such as the intriguing correlation between blood type O and reduced MACE risk. The model’s implementation in a user-friendly MACE prediction calculator opens avenues for prospective impact assessment, counseling, shared decision-making, and risk reduction strategies in the growing landscape of LT procedures. External validation and application in various transplantation-capable centers will enhance understanding of the model’s broader utility.