Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine
Jonathan Soldera1,2, Leandro Luis Corso3, Matheus Machado Rech4
1Post Graduate Program at Acute Medicine and Gastroenterology, University of South Wales, Cardiff CF37 1DL, United Kingdom.
Insights
A machine learning model accurately predicts major adverse cardiovascular events (MACE) after liver transplants (LT). This tool helps assess patient risk, improving clinical practice for liver transplant recipients.
Area of Science:
- Cardiovascular Medicine
- Hepatology
- Machine Learning in Healthcare
Background:
- Liver transplant (LT) recipients are increasingly older and sicker.
- Major adverse cardiovascular events (MACE) and associated mortality are rising post-LT.
- Traditional noninvasive cardiac stress testing has limitations in cirrhotic patients.
Purpose of the Study:
- To evaluate the feasibility and accuracy of a machine learning model for predicting post-LT MACE.
- To develop a predictive tool for major adverse cardiovascular events in liver transplant recipients.
- To assess the performance of an extreme gradient boosting model in a regional cohort.
Main Methods:
- Retrospective cohort study of 575 LT patients.
- Developed an extreme gradient boosting (XGBoost) model using 83 features.
- Addressed missing data using k-nearest neighbor imputation; model performance assessed by AUROC and Brier score.
Main Results:
- The XGBoost model achieved an AUROC of 0.89 and excellent calibration (Brier score 0.07).
- Key predictors included negative cardiac stress tests, beta-blocker use, bilirubin levels, and blood type.
- The model demonstrated high accuracy (0.84), precision (0.85), recall (0.80), and F1-score (0.79).
Conclusions:
- The XGBoost model is feasible and accurate for predicting post-LT MACE.
- The model integrates cardiovascular and hepatic variables for robust risk assessment.
- The study emphasizes the model's potential as a reliable clinical tool for post-LT MACE prediction.
Background:
Liver transplant (LT) patients have become older and sicker. The rate of post-LT major adverse cardiovascular events (MACE) has increased, and this in turn raises 30-d post-LT mortality. Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients.
Aim:
To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort.
Methods:
This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center. We developed a predictive model for post-LT MACE (defined as a composite outcome of stroke, new-onset heart failure, severe arrhythmia, and myocardial infarction) using the extreme gradient boosting (XGBoost) machine learning model. We addressed missing data (below 20%) for relevant variables using the k-nearest neighbor imputation method, calculating the mean from the ten nearest neighbors for each case. The modeling dataset included 83 features, encompassing patient and laboratory data, cirrhosis complications, and pre-LT cardiac assessments. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). We also employed Shapley additive explanations (SHAP) to interpret feature impacts. The dataset was split into training (75%) and testing (25%) sets. Calibration was evaluated using the Brier score. We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting. Scikit-learn and SHAP in Python 3 were used for all analyses. The supplementary material includes code for model development and a user-friendly online MACE prediction calculator.
Results:
Of the 537 included patients, 23 (4.46%) developed in-hospital MACE, with a mean age at transplantation of 52.9 years. The majority, 66.1%, were male. The XGBoost model achieved an impressive AUROC of 0.89 during the training stage. This model exhibited accuracy, precision, recall, and F1-score values of 0.84, 0.85, 0.80, and 0.79, respectively. Calibration, as assessed by the Brier score, indicated excellent model calibration with a score of 0.07. Furthermore, SHAP values highlighted the significance of certain variables in predicting postoperative MACE, with negative noninvasive cardiac stress testing, use of nonselective beta-blockers, direct bilirubin levels, blood type O, and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level. These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE, making it a valuable tool for clinical practice.
Conclusion:
Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE, using both cardiovascular and hepatic variables. The model demonstrated impressive performance, aligning with literature findings, and exhibited excellent calibration. Notably, our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data, reinforcing the model's value as a reliable tool for predicting post-LT MACE in clinical practice.
