Carsetti Andrea, Aya Hollmann D, Pierantozzi Silvia, Bazurro Simone, Donati Abele, Rhodes Andrew, Cecconi Maurizio
Department of Intensive Care Medicine, St George's University Hospitals NHS Foundation Trust, Blackshaw Road, London, SW17 0QT, UK.
Department of Biomedical Sciences and Public Health, Università Politecnica delle Marche, Ancona, Italy.
J Clin Monit Comput. 2017 Aug;31(4):669-676. doi: 10.1007/s10877-016-9928-3. Epub 2016 Sep 1.
Analysis of the microcirculation is currently performed offline, is time consuming and operator dependent. The aim of this study was to assess the ability and efficiency of the automatic analysis software CytoCamTools 1.7.12 (CC) to measure microvascular parameters in comparison with Automated Vascular Analysis (AVA) software 3.2. 22 patients admitted to the cardiothoracic intensive care unit following cardiac surgery were prospectively enrolled. Sublingual microcirculatory videos were analysed using AVA and CC software. The total vessel density (TVD) for small vessels, perfused vessel density (PVD) and proportion of perfused vessels (PPV) were calculated. Blood flow was assessed using the microvascular flow index (MFI) for AVA software and the averaged perfused speed indicator (APSI) for the CC software. The duration of the analysis was also recorded. Eighty-four videos from 22 patients were analysed. The bias between TVD-CC and TVD-AVA was 2.20 mm/mm (95 % CI 1.37-3.03) with limits of agreement (LOA) of -4.39 (95 % CI -5.66 to -3.16) and 8.79 (95 % CI 7.50-10.01) mm/mm. The percentage error (PE) for TVD was ±32.2 %. TVD was positively correlated between CC and AVA (r = 0.74, p < 0.001). The bias between PVD-CC and PVD-AVA was 6.54 mm/mm (95 % CI 5.60-7.48) with LOA of -4.25 (95 % CI -8.48 to -0.02) and 17.34 (95 % CI 13.11-21.57) mm/mm. The PE for PVD was ±61.2 %. PVD was positively correlated between CC and AVA (r = 0.66, p < 0.001). The median PPV-AVA was significantly higher than the median PPV-CC [97.39 % (95.25, 100 %) vs. 81.65 % (61.97, 88.99), p < 0.0001]. MFI categories cannot estimate or predict APSI values (p = 0.45). The time required for the analysis was shorter with CC than with AVA system [2'42″ (2'12″, 3'31″) vs. 16'12″ (13'38″, 17'57″), p < 0.001]. TVD is comparable between the two softwares, although faster with CC software. The values for PVD and PPV are not interchangeable given the different approach to assess microcirculatory flow.
目前，微循环分析是离线进行的，耗时且依赖操作人员。本研究的目的是评估自动分析软件CytoCamTools 1.7.12（CC）与自动血管分析（AVA）软件3.2相比测量微血管参数的能力和效率。前瞻性纳入22例心脏手术后入住心胸重症监护病房的患者。使用AVA和CC软件分析舌下微循环视频。计算小血管的总血管密度（TVD）、灌注血管密度（PVD）和灌注血管比例（PPV）。使用AVA软件的微血管血流指数（MFI）和CC软件的平均灌注速度指标（APSI）评估血流。还记录了分析持续时间。分析了22例患者的84个视频。TVD-CC与TVD-AVA之间的偏差为2.20 mm/mm（95%CI 1.37-3.03），一致性界限（LOA）为-4.39（95%CI -5.66至-3.16）和8.79（95%CI 7.50-10.01）mm/mm。TVD的百分比误差（PE）为±32.2%。TVD在CC和AVA之间呈正相关（r = 0.74，p < 0.001）。PVD-CC与PVD-AVA之间的偏差为6.54 mm/mm（95%CI 5.60-7.48），LOA为-4.25（95%CI -8.48至-0.02）和17.34（95%CI 13.11-21.57）mm/mm。PVD的PE为±61.2%。PVD在CC和AVA之间呈正相关（r = 0.66，p < 0.001）。PPV-AVA的中位数显著高于PPV-CC的中位数[97.39%（95.25，100%）对81.65%（61.97，88.99），p < 0.0001]。MFI类别无法估计或预测APSI值（p = 0.45）。CC分析所需时间比AVA系统短[2分42秒（2分12秒，3分31秒）对16分12秒（13分38秒，17分57秒），p < 0.001]。尽管CC软件更快，但两种软件的TVD具有可比性。鉴于评估微循环血流的方法不同，PVD和PPV的值不可互换。
