Work

Cardiac Output Estimation in the Intensive Care Unit

Year: 2025

Type: article

Abstract: BACKGROUND: Cardiac output (CO) is a quintessential property of the cardiovascular system, one whose estimation is vital to patient care in critical illness. The most common techniques for assessing CO, thermodilution (TD) and the estimated Fick (eFick) approximation, force tradeoffs that motivate a need for new methods. OBJECTIVES: The purpose of this study was to novel CO estimators to fill key gaps in critical care medicine. METHODS: Machine learning was used to estimate CO from physiology measurements made during routine clinical care in the intensive care unit (ICU) or cardiac catheterization lab. Models were trained and validated using a curated set of 13,172 ground-truth measurements of TD-CO from 4,825 patients. Model performance was evaluated using regression metrics, trajectory analysis, classification accuracy, and ΔCO tracking. RESULTS: of 0.65 relative to a ground-truth determined by the direct Fick technique with measured oxygen consumption. CONCLUSIONS: CO models that account for dynamic physiology in ICU patients were more accurate than widely used eFick models and more versatile than TD. The performance of these models combined with their adaptation to vascular access, broad applicability, ease of use, and ease of deployment should enable them to benefit patients across diverse ICU settings.

Source: JACC Advances

DOI: 10.1016/j.jacadv.2025.101663