Measurement error of pulse pressure variation.

Dynamic preload parameters are used to guide perioperative fluid management. However, reported cut-off values vary and the presence of a gray zone complicates clinical decision making. Measurement error, intrinsic to the calculation of pulse pressure variation (PPV) has not been studied but could contribute to this level of uncertainty. The purpose of this study was to quantify and compare measurement errors associated with PPV calculations. Hemodynamic data of patients undergoing liver transplantation were extracted from the open-access VitalDatabase. Three algorithms were applied to calculate PPV based on 1 min observation periods. For each method, different durations of sampling periods were assessed. Best Linear Unbiased Prediction was determined as the reference PPV-value for each observation period. A Bayesian model was used to determine bias and precision of each method and to simulate the uncertainty of measured PPV-values. All methods were associated with measurement error. The range of differential and proportional bias were [- 0.04%, 1.64%] and [0.92%, 1.17%] respectively. Heteroscedasticity influenced by sampling period was detected in all methods. This resulted in a predicted range of reference PPV-values for a measured PPV of 12% of [10.2%, 13.9%] and [10.3%, 15.1%] for two selected methods. The predicted range in reference PPV-value changes for a measured absolute change of 1% was [- 1.3%, 3.3%] and [- 1.9%, 4%] for these two methods. We showed that all methods that calculate PPV come with varying degrees of uncertainty. Accounting for bias and precision may have important implications for the interpretation of measured PPV-values or PPV-changes.

New algorithm to quantify cardiopulmonary interaction in patients with atrial fibrillation: a proof-of-concept study.

BACKGROUND: Traditional formulas to calculate pulse pressure variation (PPV) cannot be used in patients with atrial fibrillation (AF). We have developed a new algorithm that accounts for arrhythmia-induced pulse pressure changes, allowing us to isolate and quantify ventilation-induced pulse pressure variation (VPPV). The robustness of the algorithm was tested in patients subjected to altered loading conditions. We investigated whether changes in VPPV imposed by passive leg raising (PLR) were proportional to the pre-PLR values. METHODS: Consenting patients with active AF scheduled for an ablation of the pulmonary vein under general anaesthesia and mechanical ventilation were included. Loading conditions were altered by PLR. ECG and invasive pressure data were acquired during 60 s periods before and after PLR. A generalised additive model was constructed for each patient on each observation period. The impact of AF was modelled on the two preceding RR intervals of each beat (RR0 and RR-1). The impact of ventilation and the long-term pulse pressure trends were modelled as separate splines. Ventilation-induced pulse pressure variation was defined as the percentage of the maximal change in pulse pressure during the ventilation cycle. RESULTS: Nine patients were studied. The predictive abilities of the models had a median r2 of 0.92 (inter-quartile range: 89.2-94.2). Pre-PLR VPPV ranged from 0.1% to 27.9%. After PLR, VPPV decreased to 0-11.3% (P<0.014). The relation between the Pre-PLR values and the magnitude of the changes imposed by the PLR was statistically significant (P<0.001). CONCLUSIONS: Our algorithm enables quantification of VPPV in patients with AF with the ability to detect changing loading conditions.

Dynamic filling parameters in patients with atrial fibrillation: differentiating rhythm induced from ventilation-induced variations in pulse pressure.

In patients with sinus rhythm, the magnitude of mechanical ventilation (MV)-induced changes in pulse pressure (PP) is known to predict the effect of fluid loading on cardiac output. This approach, however, is not applicable in patients with atrial fibrillation (AF). We propose a method to isolate this effect of MV from the rhythm-induced chaotic changes in PP in patients with AF. In 10 patients undergoing pulmonary vein ablation for treatment of AF under general anesthesia, ECG and PP waveforms were analyzed during apnea (T1) and during MV at tidal volumes of 8 ml/kg (T2) and 12 ml/kg (T3), respectively. In a first step, three mathematical models were compared in their ability to predict individual PP at T1. The best-fitting model was then selected as the reference to quantify the effects of MV on PP in these patients. A local polynomial regression model based on two preceding RR intervals (LOC2) was found to be superior over the quadratic models to predict PP. LOC2 was therefore selected to quantify variations in PP induced by MV. During T2 and T3, magnitude of PP deviations was related with the amplitude of tidal volume [mean bias error (SD) of -5 (6) and -8 (7) mmHg for T2 and T3, respectively; P = 0.003 repeated-measures ANOVA]. We conclude that LOC2 most accurately predicted rhythm-induced variations in PP. MV-induced deviations in PP can be quantified and may therefore provide a method to study cardiopulmonary interactions in the presence of arrhythmia.

Ventilation-induced plethysmographic variations predict fluid responsiveness in ventilated postoperative cardiac surgery patients.

BACKGROUND: It has been shown that ventilation-induced pulse pressure variation (PPV) is a better variable than central venous pressure (CVP) or pulmonary artery occlusion pressure (PAOP) for predicting cardiac output changes after fluid administration. The plethysmographic wave form measured with a fingertip pulse is very similar to the arterial blood pressure curve. METHODS: We investigated whether this widely used, noninvasive instrument could predict fluid responsiveness by conducting an observational study in 32 patients who had undergone cardiac surgery. We compared PPV, CVP, PAOP, diastolic pulmonary artery pressure, and ventilation-induced plethysmographic variation (VPV) for predicting the cardiac output change after the administration of 500 mL 6% hydroxyethylstarch. RESULTS: We found a good correlation between cardiac output changes and both PPV and VPV (P < 0.05). Receiver operating characteristic analysis revealed an area under the curve of 0.937 for PPV and 0.892 for VPV. The optimal thresholds were a variation of 11.3% for both PPV and VPV in predicting a 15% increase in cardiac output. CONCLUSION: This study shows that VPV, like PPV, is a more reliable predictor of fluid responsiveness than CVP and PAOP.