A multimodal stacked ensemble model for cardiac output prediction utilizing cardiorespiratory interactions during general anesthesia

Abstract

This study examined the possibility of estimating cardiac output (CO) using a multimodal stacking model that utilizes cardiopulmonary interactions during general anesthesia and outlined a retrospective application of machine learning regression model to a pre-collected dataset. The data of 469 adult patients (obtained from VitalDB) with normal pulmonary function tests who underwent general anesthesia were analyzed. The hemodynamic data in this study included non-invasive blood pressure, plethysmographic heart rate, and SpO₂. CO was recorded using Vigileo and EV1000 (pulse contour technique devices). Respiratory data included mechanical ventilation parameters and end-tidal CO₂ levels. A generalized linear regression model was used as the metalearner for the multimodal stacking ensemble method. Random forest, generalized linear regression, gradient boosting machine, and XGBoost were used as base learners. A Bland-Altman plot revealed that the multimodal stacked ensemble model for CO prediction from 327 patients had a bias of - 0.001 L/min and - 0.271% when calculating the percentage of difference using the EV1000 device. Agreement of model CO prediction and measured Vigileo CO in 142 patients reported a bias of - 0.01 and - 0.333%. Overall, this model predicts CO compared to data obtained by the pulse contour technique CO monitors with good agreement.

Figure 1

Data flow of multimodal stacking ensemble learning framework for cardiac output prediction during general anesthesia. Machine learning algorithms: Generalized linear model (GLM), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost).

Figure 2

(a) and (d) show a scatterplot with spatial kernel density estimation. In addition, they show a regression line between multimodal stacked ensemble model CO prediction and measured CO from EV1000 and Vigileo devices. (b), (c), (e) and (f) show descriptive statistics for the Bland–Altman analysis of agreement between model CO prediction and measured CO from the device.

Figure 3

(a) The two-way interaction (Vint) represents the unnormalized Friedman's H-statistic between variables, depicted by connecting lines in the RF model for predicting the CO. The stronger the interaction, the thicker and darker the indigo line. The node's size and green intensity indicate the variable's importance (Vimp). (b) Contributions of explanatory variables to the RF model, measured in mean squared error "%IncMSE". (c) The table matrix presents the numerical values of the unnormalized Friedman’s H-statistic, indicating the interacting variables within the RF model for predicting the CO.

Figure 4

Partial dependence plots for variables in the multimodal stacking ensemble model for CO measured by Vigileo monitoring device. Partial Dependence Multimodel Plot gives a graphical depiction of the distributed random forest (DRF), gradient boosting machine (GBM), generalized linear model (GLM), and extreme gradient boosting (XGBoost). The effect of a variable is measured as the change in the mean cardiac output. HR plethysmographic heart rate, NIBP SBP systolic non-invasive blood pressure.

Figure 5

Partial dependence plots for variables in the multimodal stacking ensemble model for CO measured by Vigileo monitoring device. Partial dependence multimodel plot gives a graphical depiction of the distributed random forest (DRF), gradient boosting machine (GBM), generalized linear model (GLM), and extreme gradient boosting (XGBoost). The effect of a variable is measured as the change in the mean cardiac output. NIBP-DBP diastolic non-invasive blood pressure, FiO2 fraction of inspired oxygen, SPO2 oxygen saturation, EtCO2 infrared spectrometry capnography, which measures end-tidal CO2, PIP peak inspiratory pressure, PEEP positive end-expiratory pressure.

Figure 6

Partial dependence plots for variables in the multimodal stacking ensemble model for CO measured by Vigileo monitoring device. Partial dependence multimodel plot gives a graphical depiction of the distributed random forest (DRF), gradient boosting machine (GBM), generalized linear model (GLM), and extreme gradient boosting (XGBoost). The effect of a variable is measured as the change in the mean cardiac output. RR respiratory rate based on capnography, TV expiratory tidal volume, Vm expiratory minute volume.