Open Access 01-06-2008 | Research
Assessment of stroke volume variation for prediction of fluid responsiveness using the modified FloTrac™ and PiCCOplus™ system
Published in: Critical Care | Issue 3/2008
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Introduction
Stroke volume variation (SVV) has repeatedly been shown to be a reliable predictor of fluid responsiveness. Various devices allow automated clinical assessment of SVV. The aim of the present study was to compare prediction of fluid responsiveness using SVV, as determined by the FloTrac™/Vigileo™ system and the PiCCOplus™ system.
Methods
In patients who had undergone elective cardiac surgery, SVVFloTrac was determined via radial FloTrac sensor, and SVVPiCCO and pulse pressure variation were assessed via a femoral PiCCO catheter. Stroke volume was assessed by transpulmonary thermodilution. All variables were recorded before and after a volume shift induced by a change in body positioning (from 30° head-up position to 30° head-down position). Pearson correlation, t-test, and Bland-Altman analysis were performed. Area under the curve was determined by plotting receiver operating characteristic curves for changes in stroke volume in excess of 25%. P < 0.05 was considered statistically significant.
Results
Body positioning resulted in a significant increase in stroke volume; SVVFloTrac and SVVPiCCO decreased significantly. Correlations of SVVFloTrac and SVVPiCCO with change in stroke volume were similar. There was no significant difference between the areas under the curve for SVVFloTrac and SVVPiCCO; the optimal threshold values given by the receiver operating characteristic curves were 9.6% for SVVFloTrac (sensitivity 91% and specificity 83%) and 12.1% for SVVPiCCO (sensitivity 87% and specificity 76%). There was a clinically acceptable agreement and strong correlation between SVVFloTrac and SVVPiCCO.
Conclusion
SVVs assessed using the FloTrac™/Vigileo™ and the PiCCOplus™ systems exhibited similar performances in terms of predicting fluid responsiveness. In comparison with SVVPiCCO, SVVFloTrac has a lower threshold value.