• Philip-Helge Arnemann, Michael Hessler, Tim Kampmeier, Andrea Morelli, Hugo Karel Van Aken, Martin Westphal, Sebastian Rehberg, and Christian Ertmer.
• Department of Anaesthesiology, Intensive Care and Pain Therapy, Muenster University Hospital, Albert-Schweitzer-Campus 1, Building A1, 49149, Muenster, Germany. arnemann@anit.uni-muenster.de.
• Intensive Care Med Exp. 2016 Dec 1; 4 (1): 37.

BackgroundLife-threatening diseases of critically ill patients are known to derange microcirculation. Automatic analysis of microcirculation would provide a bedside diagnostic tool for microcirculatory disorders and allow immediate therapeutic decisions based upon microcirculation analysis.MethodsAfter induction of general anaesthesia and instrumentation for haemodynamic monitoring, haemorrhagic shock was induced in ten female sheep by stepwise blood withdrawal of 3 × 10 mL per kilogram body weight. Before and after the induction of haemorrhagic shock, haemodynamic variables, samples for blood gas analysis, and videos of conjunctival microcirculation were obtained by incident dark field illumination microscopy. Microcirculatory videos were analysed (1) manually with AVA software version 3.2 by an experienced user and (2) automatically by AVA software version 4.2 for total vessel density (TVD), perfused vessel density (PVD) and proportion of perfused vessels (PPV). Correlation between the two analysis methods was examined by intraclass correlation coefficient and Bland-Altman analysis.ResultsThe induction of haemorrhagic shock decreased the mean arterial pressure (from 87 ± 11 to 40 ± 7 mmHg; p < 0.001); stroke volume index (from 38 ± 14 to 20 ± 5 ml·m-2; p = 0.001) and cardiac index (from 2.9 ± 0.9 to 1.8 ± 0.5 L·min-1·m-2; p < 0.001) and increased the heart rate (from 72 ± 9 to 87 ± 11 bpm; p < 0.001) and lactate concentration (from 0.9 ± 0.3 to 2.0 ± 0.6 mmol·L-1; p = 0.001). Manual analysis showed no change in TVD (17.8 ± 4.2 to 17.8 ± 3.8 mm*mm-2; p = 0.993), whereas PVD (from 15.6 ± 4.6 to 11.5 ± 6.5 mm*mm-2; p = 0.041) and PPV (from 85.9 ± 11.8 to 62.7 ± 29.6%; p = 0.017) decreased significantly. Automatic analysis was not able to identify these changes. Correlation analysis showed a poor correlation between the analysis methods and a wide spread of values in Bland-Altman analysis.ConclusionsAs characteristic changes in microcirculation during ovine haemorrhagic shock were not detected by automatic analysis and correlation between automatic and manual analyses (current gold standard) was poor, the use of the investigated software for automatic analysis of microcirculation cannot be recommended in its current version at least in the investigated model. Further improvements in automatic vessel detection are needed before its routine use.

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