Development and validation of a machine learning model for predicting mortality risk in veno-arterial extracorporeal membrane oxygenation patients

This study aims to develop and validate a machine learning-based mortality risk prediction model for V-A ECMO patients to improve the precision of clinical decision-making. This multicenter retrospective cohort study included 280 patients receiving V-A ECMO from the Second Affiliated Hospital of Gua...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 41581 - 14
Main Authors: Gao, Hanming, Huang, Xiaolin, Zhou, Kaihuan, Ling, Yicong, Chen, Yin, Mou, Chenglin, Li, Shuanglei, Lu, Junyu
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 24.11.2025
Nature Publishing Group
Nature Portfolio
Subjects:
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Accuracy
Adult
Age
Albumin
Calibration
Cardiac arrest
Clinical decision making
Decision making
Diabetes
Diabetes mellitus
Extracorporeal membrane oxygenation
Extracorporeal Membrane Oxygenation - methods
Extracorporeal Membrane Oxygenation - mortality
Feature selection
Female
Health risks
Heart attacks
Heart failure
Hospital Mortality
Humanities and Social Sciences
Humans
Lactic acid
Learning algorithms
Logistic Models
Logistic regression
Machine Learning
Male
Medical prognosis
Middle Aged
Missing data
Mortality
Mortality risk
Mortality risk prediction
multidisciplinary
Myocarditis
Neural networks
Oxygenation
Patients
Prediction models
Regression analysis
Retrospective Studies
Risk Assessment
Science
Science (multidisciplinary)
Sepsis
Standard scores
Variables
Venoarterial extracorporeal membrane oxygenation
Ventilation
Ventilators
Mortality risk prediction
Logistic regression
Machine learning
Venoarterial extracorporeal membrane oxygenation
ISSN:2045-2322, 2045-2322
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Summary:This study aims to develop and validate a machine learning-based mortality risk prediction model for V-A ECMO patients to improve the precision of clinical decision-making. This multicenter retrospective cohort study included 280 patients receiving V-A ECMO from the Second Affiliated Hospital of Guangxi Medical University, Yulin First People’s Hospital, and the MIMIC-IV database. The data from the Second Affiliated Hospital of Guangxi Medical University and the MIMIC-IV database were merged and randomly divided in a 7:3 ratio into a training set and an internal validation set, respectively. The dataset from Yulin First People’s Hospital was reserved as an external validation cohort. The primary study outcome was defined as in-hospital mortality.Feature selection was conducted using Lasso regression, followed by the development of six machine learning models: Logistic Regression, Random Forest (RF), Deep Neural Network (DNN), Support Vector Machine (SVM), LightGBM, and CatBoost. Model performance was assessed using the Area Under the Curve (AUC), accuracy, sensitivity, specificity, and F1 score. Model validation was performed through calibration and decision curve analysis. Feature importance was evaluated using SHAP, and subgroup analysis was conducted to assess the model’s applicability across different clinical scenarios. In internal validation, the Logistic Regression model performed the best, with an AUC of 0.86 (95% CI: 0.77–0.93), accuracy of 0.76, sensitivity of 0.73, specificity of 0.79, and an F1 score of 0.73. It outperformed other models (RF: AUC = 0.79, DNN: AUC = 0.78, SVM: AUC = 0.76, LightGBM: AUC = 0.71, CatBoost: AUC = 0.77). External validation yielded consistent results, with the Logistic Regression model’s AUC at 0.75 (95% CI: 0.56–0.92), accuracy of 0.69, sensitivity of 0.64, specificity of 0.73, and an F1 score of 0.66. Calibration curve analysis revealed that the Logistic Regression model had the lowest Brier score (0.1496), indicating the most reliable predicted probabilities. Decision curve analysis demonstrated that the model provided the highest net benefit across most decision thresholds. SHAP analysis identified lactate, age, and albumin as key predictors of mortality, with lactate and age positively correlated, and albumin negatively correlated. Subgroup analysis revealed better performance in the cardiac arrest group (AUC = 0.81), non-sepsis group (AUC = 0.75), and non-diabetes group (AUC = 0.78). The Logistic Regression-based mortality risk prediction model for V-A ECMO patients demonstrated comparable or even favorable performance to more complex machine learning models, with the advantage of higher interpretability.By explicitly incorporating lactate, age, and albumin as the principal predictors, this model facilitates precise risk stratification and provides practical support for clinical decision-making in ECMO management.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-25423-4