Authors: Rinehart J, et al.
Anesthesia & Analgesia. August 29, 2025. doi:10.1213/ANE.0000000000007704
This article introduces a new statistical tool—error field concordance analysis—for comparing cardiac output measurement methods in perioperative and intensive care settings. Traditional approaches like 4-quadrant plots and polar plots have limitations: the former struggles to distinguish tight versus loose concordance, while the latter requires complex data transformations and inadequately identifies discordance.
Error field concordance analysis uses a color-coded Cartesian plane to assess agreement, weighing the magnitude of concordance and calculating a concordance angle. In simulations, it outperformed both 4-quadrant and polar plots, distinguishing strong concordance, weak concordance, noise, and discordance without excluding data. Importantly, it avoids artificial inflation of concordance scores when little true change is present. A supporting Python package is available via PIP for practical application.
What You Should Know
• Provides a simple, visual, and quantitative way to evaluate agreement between cardiac output monitoring methods.
• Eliminates key weaknesses of 4-quadrant and polar plots, especially regarding noise and discordance.
• Makes use of color-coded error fields for intuitive interpretation.
• Supported by an open-source Python package for clinicians and researchers.
Practice Implication
Error field concordance analysis may become a preferred method for assessing agreement between hemodynamic monitoring devices. It simplifies interpretation, reduces bias from exclusion zones, and enhances detection of clinically important discordance, making it a valuable addition to perioperative and critical care research.
References
Rinehart J, et al. Anesth Analg. 2025. doi:10.1213/ANE.0000000000007704
Thank you Anesthesia & Analgesia for allowing us to use this article.