Lala Raushan C B, Homes Ryan A P, Lipman Jeffrey, Midwinter Mark J
Faculty of Medicine, The University of Queensland, St Lucia, Australia.
Traumatic Injury Sciences Group, The University of Queensland, St Lucia, Australia.
Intensive Care Med Exp. 2023 Dec 8;11(1):88. doi: 10.1186/s40635-023-00572-w.
Direct assessment of microcirculatory function remains a critical care research tool but approaches for analysis of microcirculatory videomicroscopy clips are shifting from manual to automated algorithms, with a view to clinical application in the intensive care unit. Automated analysis software associated with current sidestream darkfield videomicroscopy systems is demonstrably unreliable; therefore, semi-automated analysis of captured clips should be undertaken in older generations of software. We present a method for capture of microcirculatory clips using current version videomicroscope hardware and resizing of clips to allow compatibility with legacy analysis software. The interobserver reliability of this novel approach is examined, in addition to a comparison of this approach with the current generation of automated analysis software.
Resizing microcirculatory clips did not significantly change image quality. Assessment of bias between observers for manual analysis of resized clips; and between manually analysed clips and automated software analysis was undertaken by Bland-Altman analysis. Bias was demonstrated for all parameters for manual analysis of resized clips (total vessel density = 6.8, perfused vessel density = 6.3, proportion of perfused vessels = - 8.79, microvascular flow index = - 0.08). Marked bias between manual analysis and automated analysis was also evident (total vessel density = 16.6, perfused vessel density = 16.0, proportion of perfused vessels = 1.8). The difference between manual and automated analysis was linearly related to the magnitude of the measured parameter.
Poor reliability of automated analysis is a significant hurdle for clinical translation of microcirculatory monitoring. The method presented here allows capture of microcirculatory clips using current hardware that are backwards compatible with older versions of manual analysis software. We conclude that this approach is appropriate for research applications in the intensive care unit, however the time delay to results limits utility for clinical translation.
微循环功能的直接评估仍然是一种重症监护研究工具,但微循环视频显微镜片段的分析方法正从手动算法转向自动化算法,以期在重症监护病房中得到临床应用。与当前侧流暗视野视频显微镜系统相关的自动化分析软件明显不可靠;因此,应使用旧版本软件对捕获的片段进行半自动分析。我们提出了一种使用当前版本视频显微镜硬件捕获微循环片段并调整片段大小以使其与传统分析软件兼容的方法。除了将这种方法与当前一代自动化分析软件进行比较之外,还检验了这种新方法的观察者间可靠性。
调整微循环片段大小并未显著改变图像质量。通过Bland-Altman分析对观察者之间对调整大小后的片段进行手动分析的偏差;以及手动分析的片段与自动化软件分析之间的偏差进行了评估。对调整大小后的片段进行手动分析的所有参数均显示出偏差(总血管密度 = 6.8,灌注血管密度 = 6.3,灌注血管比例 = -8.79,微血管血流指数 = -0.08)。手动分析与自动化分析之间也存在明显偏差(总血管密度 = 16.6,灌注血管密度 = 16.0,灌注血管比例 = 1.8)。手动分析与自动化分析之间的差异与测量参数的大小呈线性相关。
自动化分析的可靠性较差是微循环监测临床转化的一个重大障碍。此处介绍的方法允许使用当前硬件捕获与旧版本手动分析软件向后兼容的微循环片段。我们得出结论,这种方法适用于重症监护病房的研究应用,然而结果延迟限制了其临床转化的效用。