• Teiji Sawa, Mao Kinoshita, Atsushi Kainuma, Koichi Akiyama, Yoshifumi Naito, Hideya Kato, Fumimasa Amaya, and Keiji Shigemi.
• Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, 602-8566, Japan. anesth@koto.kpu-m.ac.jp.
• J Clin Monit Comput. 2017 Oct 1; 31 (5): 927-941.

AbstractArterial pulse waveform analysis (APWA) with a semi-invasive cardiac output monitoring device is popular in perioperative hemodynamic and fluid management. However, in APWA, evaluation of hemodynamic data is not well discussed. In this study, we analyzed how we visually interpret hemodynamic data, including stroke volume variation (SVV) and stroke volume (SV) derived from APWA. We performed arithmetic estimation of the SVV-SV relationship and applied measured values to this estimation. We then collected measured values in six anesthesia cases, including three liver transplantations and three other types of surgeries, to apply them to this SVV-SVI (stroke volume variation index) plot. Arithmetic analysis showed that the relationship between SVV and SV can be drawn as hyperbolic curves. Plotting SVV-SV values in the semi-logarithmic scale showed linear correlations, and the slopes of the linear regression lines theoretically represented average mean cardiac contractility. In clinical measurements in APWA, plotting SVV and SVI values in the linear scale and the semi-logarithmic scale showed the correlations represented by hyperbolic curves and linear regression lines. The plots approximately shifted on the rectangular hyperbolic curves, depending on blood loss and blood transfusion. Arithmetic estimation is close to real measurement of the SVV-SV interaction in hyperbolic curves. In APWA, using SVV as an index of preload and the cardiac index or SVI derived from arterial pressure-based cardiac output as an index of cardiac function, is likely to be appropriate for categorizing hemodynamic stages as a substitute for Forrester subsets.

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