Evidence map›Paper›PMID 27492429›Full record

ArticleJournal of clinical monitoring and computing2017

Effective evaluation of arterial pulse waveform analysis by two-dimensional stroke volume variation-stroke volume index plots.

Teiji Sawa, Mao Kinoshita, Atsushi Kainuma, Koichi Akiyama, Yoshifumi Naito, Hideya Kato, Fumimasa Amaya, Keiji Shigemi

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Article in Journal of clinical monitoring and computing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Teiji SawaDepartment of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, 602-8566, Japan. anesth@koto.kpu-m.ac.jp.
Mao KinoshitaDepartment of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, 602-8566, Japan.
Atsushi KainumaDepartment of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, 602-8566, Japan.
Koichi AkiyamaDepartment of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, 602-8566, Japan.
Yoshifumi NaitoDepartment of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, 602-8566, Japan.
Hideya KatoDepartment of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, 602-8566, Japan.
Fumimasa AmayaDepartment of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, 602-8566, Japan.
Keiji ShigemiDepartment of Anesthesiology and Reanimatology, School of Medicine, University of Fukui, Fukui, 910-1193, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Arterial 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.

Indexed as

Fluid TherapyHemodynamicsStroke VolumeAdultAgedAnesthesiaArterial PressureArteriesBlood PressureCardiac OutputFemaleHumansLinear ModelsMaleMiddle AgedMonitoring, PhysiologicFloTrac/Vigileo systemFluid responsivenessGoal-directed therapyHemodynamics

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.