Machine learning algorithm to estimate cardiac output based on non-invasive arterial blood pressure measurements

Abstract

Cardiac output (CO), a vital hemodynamic parameter that reflects the blood volume pumped by the heart per minute, is crucial for determining tissue oxygen delivery and the heart's ability to meet the body's demands. Researchers have developed various methods to measure cardiac output, including thermodilution using pulmonary artery catheters (PAC), also called Swan-Ganz catheters, the gold standard for cardiac output measurements. Such an approach involves an invasive procedure associated with complications, and it requires specialized equipment and expertise, limiting its use to critically ill patients undergoing operations in intensive care units (ICUs). An alternative, less invasive way to estimate CO is by analyzing arterial blood pressure (ABP) waveforms. However, the relationship between cardiac output and blood pressure is unknown. This study uses machine learning and feature engineering techniques to discover the relationship between CO and ABP. We applied the sparse identification non-linear dynamics (SINDy) algorithm to discover features that significantly contribute to the relationship between CO and ABP. Additionally, we investigated the optimum number of cardiac cycles required for feature extraction to achieve the best performance providing insights into the temporal dynamics of CO estimation. The proposed approach achieved clinically acceptable performance regarding radial limits of agreement and bias. Further, the proposed approach was validated on an external dataset and achieved comparable performance. Finally, the learned model was interpreted as a differential equation describing the blood flow where CO acts as an external force to the system. All materials used in this study, including code, model, raw data, processed data, and extracted features, are available on GitHub to facilitate further development.