Prediction of low cardiac output syndrome in patients following non-isolated coronary artery bypass grafting surgery using machine learning

Q: What is the clinical evidence from this study?

Study design: Cohort. Population: Low cardiac output syndrome (LCOS) following non-isolated coronary artery bypass grafting (CABG) (n=378). Intervention: Extreme gradient boosting (XGB) machine learning model vs. Logistic regression (LR) and other machine learning models. Primary outcome: Prediction of low cardiac output syndrome (LCOS) in the testing cohort (AUC 0.868, 95% CI 0.799-0.936).

Q: What does this research mean for the field?

An extreme gradient boosting (XGB) machine learning model accurately predicts low cardiac output syndrome following non-isolated coronary artery bypass grafting, achieving an AUC of 0.868 in testing. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Emmanuel Delali Kofi Fiagbey; J. Wang; S. Saad; Tianling Feng; Zikomo Gaudence Kipanga; Daniel Kofi Nyame; Edwin Sospeter Ndoli; Etima Jeremiah Iweh; Weifeng Yao; Liang Hong; Jianjun Zou

doi:10.37349/ec.2026.1012111

June 17, 2026Exploration of CardiologyOpen Access

Prediction of low cardiac output syndrome in patients following non-isolated coronary artery bypass grafting surgery using machine learning

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Key result

XGB machine learning predicts low cardiac output syndrome after non-isolated CABG with ~0.87 AUC.

AUC 0.868
95% CI 0.799-0.936
n=378

Why the study?

Timely detection and intervention of low cardiac output syndrome (LCOS) are essential, but no model had been established to predict its onset following non-isolated CABG surgery.

Can machine learning models accurately predict low cardiac output syndrome after non-isolated CABG?

Key Points

To develop and validate a machine-learning model to predict the onset of low cardiac output syndrome (LCOS) following non-isolated coronary artery bypass grafting (CABG).
Retrospectively assessed N=378 patients who underwent non-isolated CABG at Nanjing First Hospital, China.
Developed prediction models using five algorithms (logistic regression, random forest, extreme gradient boosting [XGB], light gradient boosting machine, and support vector machine) following LASSO feature selection.
Assessed model discrimination and clinical utility with area under the receiver operating characteristic curve (AUC), 10-fold cross-validation, Brier scores, decision curve analysis, and SHapley Additive exPlanations (SHAP).
In the total cohort, 121 of 378 patients (32.0%) developed LCOS following surgery.
In testing, the XGB model showed the best performance with an AUC of 0.868 (95% CI: 0.799–0.936), specificity of 0.785, accuracy of 0.781, and precision of 0.614.
SHAP feature importance analysis identified ejection fraction, left ventricular end-systolic diameter, and lactate levels as the three most influential predictors of postoperative LCOS.

Study Design

Type

Cohort (n=378)

Multicenter

No

Structured PICO

Can machine learning models accurately predict low cardiac output syndrome after non-isolated CABG?

P

Population

378 adult patients (median age 68, 29.9% female) who underwent non-isolated coronary artery bypass grafting surgery, retrospectively assessed for the development of low cardiac output syndrome between 1 and 48 hours postoperatively.

E

Exposure

Machine-learning-based prediction models (L2 regularized logistic regression, random forest, extreme gradient boosting [XGB], light gradient boosting machine, and support vector machine)

O

Outcome

Prediction of low cardiac output syndrome (LCOS) onset post non-isolated CABG

Main Result

Effect estimate: AUC 0.868 (95% CI 0.799-0.936)

Absolute Event Rate: 0.868% vs 0.867%

Limitations

Single-center retrospective study with a relatively modest sample size limiting generalizability
Potential introduction of heterogeneity within the LCOS cohort due to multiple diagnostic criteria
Model's calibration in extremely high-risk patients required cautious interpretation
Possible overestimation of the model's discriminatory performance due to the dual role of lactate as both a predictor and a component of the outcome definition
Risk of overfitting despite regularization strategies

Implication

An extreme gradient boosting machine learning model can effectively predict low cardiac output syndrome after non-isolated CABG, potentially aiding in early risk stratification.

Abstract

Aim: Low cardiac output syndrome (LCOS) may be improvable; hence, timely detection and intervention are essential. However, no model has been established for the prediction of LCOS onset post non-isolated coronary artery bypass grafting (CABG) surgery. Therefore, this study aimed to develop a machine-learning-based model to predict LCOS after non-isolated CABG. Methods: A total of 378 patients who underwent non-isolated CABG at Nanjing First Hospital, China, were retrospectively assessed. Five algorithms [L2 regularized logistic regression (LR), random forest (RF) classifier, extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and support vector machine (SVM)] were employed. Model performance and clinical utility were evaluated using area under the curve (AUC), 10-fold cross-validation, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used to assess the model’s interpretability. A web calculator was developed. Results: XGB showed superior performance and calibration (AUC: 0.933, 95% CI: 0.903–0.962; Brier score of 0.107), with excellent specificity (0.865), accuracy (0.860), and precision (0.753). In testing, XGB maintained excellent discrimination (AUC: 0.868, 95% CI: 0.799–0.936), best specificity (0.785), accuracy (0.781), and precision (0.614). DCA confirmed clinical usefulness. SHAP analysis identified the ejection fraction, left ventricular end-systolic diameter, and lactate levels as the most influential predictors. The web calculator is accessible via https://lcos-cabg-xgb-model.streamlit.app/ Conclusions: The developed web-based XGB model effectively predicts LCOS after non-isolated CABG, aiding early risk stratification and detection.

Discussion

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Cite This Study

Fiagbey et al. (2026) conducted a cohort in Low cardiac output syndrome (LCOS) following non-isolated coronary artery bypass grafting (CABG) (n=378). Extreme gradient boosting (XGB) machine learning model vs. Logistic regression (LR) and other machine learning models was evaluated on Prediction of low cardiac output syndrome (LCOS) in the testing cohort (AUC 0.868, 95% CI 0.799-0.936). The extreme gradient boosting (XGB) machine learning model effectively predicted low cardiac output syndrome after non-isolated CABG surgery, achieving an AUC of 0.868 in the testing cohort.

synapsesocial.com/papers/6a33cfe614a9c556e667795a https://doi.org/10.37349/ec.2026.1012111

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Prediction of low cardiac output syndrome in patients following non-isolated coronary artery bypass grafting surgery using machine learning

Key Points

Study Design

Structured PICO

Main Result

Limitations

Implication

Abstract

Discussion

Cite This Study

Prediction of low cardiac output syndrome in patients following non-isolated coronary artery bypass grafting surgery using machine learning

Key Points

Study Design

Structured PICO

Main Result

Limitations

Implication

Abstract

Discussion

Cite This Study

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