Real-time automated analysis of videos of the microvasculature is an essential step in the development of research protocols and clinical algorithms that incorporate point-of-care microvascular analysis. In response to the call for validation studies of available automated analysis software by the European Society of Intensive Care Medicine, and building on a previous validation study in sheep, we report the first human validation study of AVA 4. Two retrospective perioperative datasets of human microcirculation videos (P1 and P2) and one prospective healthy volunteer dataset (V1) were used in this validation study. Video quality was assessed using the modified Microcirculation Image Quality Selection (MIQS) score. Videos were initially analyzed with (1) AVA software 3.2 by two experienced investigators using the gold standard semi-automated method, followed by an analysis with (2) AVA automated software 4.1. Microvascular variables measured were perfused vessel density (PVD), total vessel density (TVD), and proportion of perfused vessels (PPV). Bland–Altman analysis and intraclass correlation coefficients (ICC) were used to measure agreement between the two methods. Each method’s ability to discriminate between microcirculatory states before and after induction of general anesthesia was assessed using paired t-tests. Fifty-two videos from P1, 128 videos from P2 and 26 videos from V1 met inclusion criteria for analysis. Correlational analysis and Bland–Altman analysis revealed poor agreement and no correlation between AVA 4.1 and AVA 3.2. Following the induction of general anesthesia, TVD and PVD measured using AVA 3.2 increased significantly for P1 (p < 0.05) and P2 (p < 0.05). However, these changes could not be replicated with the data generated by AVA 4.1. AVA 4.1 is not a suitable tool for research or clinical purposes at this time. Future validation studies of automated microvascular flow analysis software should aim to measure the new software’s agreement with the gold standard, its ability to discriminate between clinical states and the quality thresholds at which its performance becomes unacceptable.

中文翻译：

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评估自动微血管血流分析软件AVA 4：一项验证研究

对微脉管系统视频进行实时自动化分析是开发结合了即时医疗微血管分析的研究方案和临床算法的必不可少的步骤。为响应欧洲重症监护医学会对可用的自动分析软件进行的验证研究的呼吁，并基于先前在绵羊中进行的验证研究，我们报告了首例AVA 4的人体验证研究。人体微循环的两个回顾性围手术期数据集视频（P1和P2）和一个前瞻性健康志愿者数据集（V1）用于此验证研究。使用修改后的微循环图像质量选择（MIQS）评分评估视频质量。最初使用（1）AVA软件3分析了视频。2由两名经验丰富的研究人员使用黄金标准半自动方法进行，然后使用（2）AVA自动软件4.1进行分析。测得的微血管变量为灌注血管密度（PVD），总血管密度（TVD）和灌注血管比例（PPV）。使用Bland–Altman分析和类内相关系数（ICC）来衡量两种方法之间的一致性。使用配对t检验评估每种方法在全身麻醉诱导之前和之后区分微循环状态的能力。来自P1的52个视频，来自P2的128个视频和来自V1的26个视频符合纳入标准进行分析。相关分析和Bland-Altman分析显示，AVA 4.1和AVA 3.2之间的一致性较差，并且没有相关性。全身麻醉后，对于P1（p <0.05）和P2（p <0.05），使用AVA 3.2测量的TVD和PVD显着增加。但是，这些更改无法与AVA 4.1生成的数据一起复制。目前，AVA 4.1尚不适合用于研究或临床目的。自动化微血管血流分析软件的未来验证研究应旨在衡量该新软件与黄金标准的协议，其区分临床状态和性能无法接受的质量阈值的能力。