Prediction of low cardiac output syndrome in patients following cardiac surgery using machine learning

Abstract

Background: This study aimed to develop machine learning models to predict Low Cardiac Output Syndrome (LCOS) in patients following cardiac surgery using machine learning algorithms.

Methods: The clinical data of cardiac surgery patients in Nanjing First Hospital between June 2019 and November 2020 were retrospectively extracted from the electronic medical records. Six conventional machine learning algorithms, including logistic regression, support vector machine, decision tree, random forest, extreme gradient boosting and light gradient boosting machine, were employed to construct the LCOS predictive models with all predictive features (full models) and selected predictive features (reduced models). The discrimination of these models was evaluated by the area under the receiver operating characteristic curve (AUC) and the calibration of the models was assessed by the calibration curve. Shapley Additive explanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to interpret the predictive models.

Results: Data from 1,585 patients [982 (62.0%) were male, aged 18 to 88, 212 (13.4%) with LCOS] were employed to train and validate the LCOS models. Among the full models, the RF model (AUC: 0.909, 95% CI: 0.875-0.943; Sensitivity: 0.849, 95% CI: 0.724-0.933; Specificity: 0.835, 95% CI: 0.796-0.869) and the XGB model (AUC: 0.897, 95% CI: 0.859-0.935; Sensitivity: 0.830, 95% CI: 0.702-0.919; Specificity: 0.809, 95% CI: 0.768-0.845) exhibited well predictive power for LCOS. Eleven predictive features including left ventricular ejection fraction (LVEF), first post-operative blood lactate (Lac), left ventricular diastolic diameter (LVDd), cumulative time of mean artery blood pressure (MABP) lower than 65 mmHg (MABP < 65 time), hypertension history, platelets level (PLT), age, blood creatinine (Cr), total area under curve above threshold central venous pressure (CVP) 12 mmHg and 16 mmHg, and blood loss during operation were used to build the reduced models. Among the reduced models, RF model (AUC: 0.895, 95% CI: 0.857-0.933; Sensitivity: 0.830, 95% CI: 0.702-0.919; Specificity: 0.806, 95% CI: 0.765-0.843) revealed the best performance. SHAP and LIME plot showed that LVEF, Lac, LVDd and MABP < 65 time significantly contributed to the prediction model.

Conclusion: In this study, we successfully developed several machine learning models to predict LCOS after surgery, which may avail to risk stratification, early detection and management of LCOS after cardiac surgery.

Keywords: cardiac surgery; low cardiac output syndrome; machine learning; predictive model; risk stratification.

FIGURE 1

Comparison of AUCs among different machine learning models. LR, logistic regression; DT, decision tree; RF, random forest classifier; LGB, light gradient boosting machine; XGB, extreme gradient boosting machine; SVM, support vector machine.

FIGURE 2

The calibration curves and the Brier score of different machine learning models. LR, logistic regression; DT, decision tree; RF, random forest classifier; LGB, light gradient boosting machine; XGB, extreme gradient boosting machine; SVM, support vector machine.

FIGURE 3

SHAP summary plot of the reduced RF model. The plot showed the importance of each variable (A) and the specific distribution between variables and Shapely value (B) using SHAP algorithm.

FIGURE 4

SHAP dependence plot of the reduced RF model. ECHO, echocardiography; LVDd, left ventricular diastolic diameter; LVPWT, left ventricular posterior wall thickness; LVEF, left ventricular ejection fraction; WBC, white blood cell count; NEU, neutrophil properties; Cr, blood creatinine; BUN, blood urea nitrogen; AST, aspartate aminotransferase; Lac, blood lactate; CPB, cardiopulmonary bypass; AO, aortic occlusion; CABG, coronary artery bypass graft; MABP < 65 time, cumulative time of mean artery blood pressure lower than 65 mmHg; MABP < 60 time, cumulative time of mean artery blood pressure lower than 60 mmHg; MABP < 55 time, cumulative time of mean artery blood pressure lower than 55 mmHg; MABP < 50 time, cumulative time of mean artery blood pressure lower than 50 mmHg; MABP_AUT_65, total area under curve below threshold mean artery blood pressure 65 mmHg; MABP_AUT_60, total area under curve below threshold mean artery blood pressure 60 mmHg; MABP_AUT_55, total area under curve below threshold mean artery blood pressure 55 mmHg; MABP_AUT_50, total area under curve below threshold mean artery blood pressure 50 mmHg; MABP_TWA_65, time weighted average mean artery blood pressure below threshold 65 mmHg; MABP_TWA_60, time weighted average mean artery blood pressure below threshold 60 mmHg; MABP_TWA_55, time weighted average mean artery blood pressure below threshold 55 mmHg; MABP_TWA_50, time weighted average mean artery blood pressure below threshold 50 mmHg; CVP > 12 time, cumulative time of central venous pressure upper than 12 mmHg; CVP > 16 time, cumulative time of central venous pressure upper than 16 mmHg; CVP > 20 time, cumulative time of central venous pressure upper than 20 mmHg; CVP_AUT_12, total area under curve above threshold central venous pressure 12 mmHg; CVP_AUT_16, total area under curve above threshold central venous pressure 16 mmHg; CVP_AUT_20, total area under curve above threshold central venous pressure 20 mmHg; CVP_TWA_12, time weighted average central venous pressure above threshold 12 mmHg; CVP_TWA_16, time weighted average central venous pressure above threshold 16 mmHg; CVP_TWA_20, time weighted average central venous pressure above threshold 20 mmHg; LCOS, low cardiac output syndrome.

FIGURE 5

LIME plot for individual case explanation on two random patients for the test set of the reduced RF model. LIME plot included one patient with LCOS (A) and one patient without LCOS (B), explained by LIME algorithm. ECHO, echocardiography; LVDd, left ventricular diastolic diameter; LVPWT, left ventricular posterior wall thickness; LVEF, left ventricular ejection fraction; WBC, white blood cell count; NEU, neutrophil properties; Cr, blood creatinine; BUN, blood urea nitrogen; AST, aspartate aminotransferase; Lac, blood lactate; CPB, cardiopulmonary bypass; AO, aortic occlusion; CABG, coronary artery bypass graft; MABP < 65 time, cumulative time of mean artery blood pressure lower than 65 mmHg; MABP < 60 time, cumulative time of mean artery blood pressure lower than 60 mmHg; MABP < 55 time, cumulative time of mean artery blood pressure lower than 55 mmHg; MABP < 50 time, cumulative time of mean artery blood pressure lower than 50 mmHg; MABP_AUT_65, total area under curve below threshold mean artery blood pressure 65 mmHg; MABP_AUT_60, total area under curve below threshold mean artery blood pressure 60 mmHg; MABP_AUT_55, total area under curve below threshold mean artery blood pressure 55 mmHg; MABP_AUT_50, total area under curve below threshold mean artery blood pressure 50 mmHg; MABP_TWA_65, time weighted average mean artery blood pressure below threshold 65 mmHg; MABP_TWA_60, time weighted average mean artery blood pressure below threshold 60 mmHg; MABP_TWA_55, time weighted average mean artery blood pressure below threshold 55 mmHg; MABP_TWA_50, time weighted average mean artery blood pressure below threshold 50 mmHg; CVP > 12 time, cumulative time of central venous pressure upper than 12 mmHg; CVP > 16 time, cumulative time of central venous pressure upper than 16 mmHg; CVP > 20 time, cumulative time of central venous pressure upper than 20 mmHg; CVP_AUT_12, total area under curve above threshold central venous pressure 12 mmHg; CVP_AUT_16, total area under curve above threshold central venous pressure 16 mmHg; CVP_AUT_20, total area under curve above threshold central venous pressure 20 mmHg; CVP_TWA_12, time weighted average central venous pressure above threshold 12 mmHg; CVP_TWA_16, time weighted average central venous pressure above threshold 16 mmHg; CVP_TWA_20, time weighted average central venous pressure above threshold 20 mmHg; LCOS, low cardiac output syndrome.