Common Microcirculatory Framework for Monitoring Integrated Microcirculation

Abstract

Wide variation in magnitudes, units, and ranges of the microcirculatory variables brings hindrance in describing and evaluating the integrated microcirculatory function of tissues. We designed to establish common microcirculatory framework that contains microhemodynamic and microcirculatory oxygen parameters. To integrate microcirculatory information, demo microcirculatory permutations were generated by a computer algorithm based on microcirculatory characteristics. Four dimensionless methods (Z-score, Min-max, L2, and median scaling) were applied to transform microcirculatory data set into the dimensionless form. Three-dimensional (3-D) common microcirculatory framework was constructed and visualized by using Python and Apache ECharts. The performance of the four dimensionless methods in the pre-processing of multiple microcirculatory variables and the establishment of the common microcirculatory framework were compared. Microhemodynamic and microcirculatory oxygen parameters were embedded in the common microcirculatory framework. After processing by Min-max normalization, the transformed multiple microcirculatory values remained positive with fixed range mapping within [0, 1] and maintained the identity property of microcirculation both of microhemodynamic and microcirculatory oxygen variables in the common microcirculatory framework. Conclusively, Min-max normalization displays preferable integration efficiency, compatibility, and adaptability in the establishment of the 3-D visualized multiparametric common microcirculatory framework.