Functional haemodynamic monitoring: The value of SVV as measured by the LiDCORapid™ in predicting fluid responsiveness in high risk vascular surgical patients

BackgroundThere is growing evidence that optimal peri-operative fluid management in high-risk surgical patients improves their post-operative outcome. Functional haemodynamic parameters such as SVV (stroke volume variation), PPV (pulse pressure variation) and SPV (systolic pressure variation) have been shown to be superior to CVP (central venous pressure) and ΔCVP in predicting fluid responsiveness. The aim of this study was to determine the accuracy and threshold values of these dynamic parameters using the minimally invasive LiDCORapid™ in high-risk surgical patients.MethodsFluid therapy in 70 patients undergoing vascular surgery was based on parameters derived from the LiDCORapid™. All patients received TIVA (total intravenous anaesthesia) and were ventilated with ≥7 ml/kg tidal volume. Haemodynamic data before and after fluid boluses were recorded and analyzed retrospectively. ΔSVI (stroke volume index) ≥10% was determined to be a positive response to a fluid challenge. The AUROCs (area under the receiver–operator curves) and confidence intervals thereof were used to assess the ability of each parameter to predict fluid responsiveness.Results32/43 fluid challenges were positive (74.4%). The correlation coefficients between baseline SVV, PPV, and SPV with ΔSVI were: 0.27 (p = 0.08), 0.18 (p = 0.25) and −0.01 (p = 0.96) whilst the AUROCs were 0.75 (0.57–0.93), 0.67 (0.48–0.85) and 0.58 (0.35–0.81) respectively. The best cut-off for SVV using Youden’s index was 13.5%, with J = 0.48. At this level, the LR+ (positive likelihood ratio) = 2.74, LR− (negative likelihood ratio) = 0.34 and the DOR (diagnostic odds ratio) = 8.06.ConclusionOnly the SVV was an adequate predictor of fluid responsiveness in this cohort of high risk surgical patients. Whereas PPV and SPV may be obtained from the arterial trace, estimation of the SVV requires a cardiac output monitor which is able to convert an arterial pressure trace into an estimation of stroke volume.