Dynamic indices do not predict volume responsiveness in routine clinical practice.
SourceBritish Journal of Anaesthesia, 108, 3, (2012), pp. 395-401
1 maart 2012
Article / Letter to editor
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British Journal of Anaesthesia
SubjectIGMD 7: Iron metabolism N4i 1: Pathogenesis and modulation of inflammation; N4i 1: Pathogenesis and modulation of inflammation
BACKGROUND: Dynamic indices, including pulse pressure, systolic pressure, and stroke volume variation (PPV, SPV, and SVV), are accurate predictors of fluid responsiveness under strict conditions, for example, controlled mechanical ventilation using conventional tidal volumes (TVs) in the absence of cardiac arrhythmias. However, in routine clinical practice, these prerequisites are not always met. We evaluated the effect of regularly used ventilator settings, different calculation methods, and the presence of cardiac arrhythmias on the ability of dynamic indices to predict fluid responsiveness in sedated, mechanically ventilated patients. METHODS: We prospectively evaluated 47 fluid challenges in 29 consecutive cardiac surgery patients. Patients were divided into different groups based on TV. Dynamic indices were calculated in various ways: calculation over 30 s, breath-by-breath (with and without excluding arrhythmias), and with correction for TV. RESULTS: The predictive value was optimal in the group ventilated with TVs >7 ml kg(-1) with correction for TV, calculated breath-by-breath, and with exclusion of arrhythmias [area under the curve (AUC)=0.95, 0.93, and 0.90 for PPV, SPV, and SVV, respectively]. Including patients ventilated with lower TVs decreased the predictive value of all dynamic indices, while calculating dynamic indices over 30 s and not excluding cardiac arrhythmias further reduced the AUC to 0.51, 0.63, and 0.51 for PPV, SPV, and SVV, respectively. CONCLUSIONS: PPV, SPV, and SVV are the only reliable predictors of fluid responsiveness under strict conditions. In routine clinical practice, factors including low TV, cardiac arrhythmias, and the calculation method can substantially reduce their predictive value.
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