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Predicting Fluid Responsiveness in Children

A Systematic Review

Gan, Heng, MBBCh, MRCPCH, FRCA*†; Cannesson, Maxime, MD, PhD; Chandler, John R., MBBCh, FCARCSI, FDSRDS§; Ansermino, J. Mark, MBBCh, MSc (Inf), FFA (SA), FRCPC*†

doi: 10.1213/ANE.0b013e3182a9557e
Pediatric Anesthesiology: Research Report
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BACKGROUND: Administration of fluid to improve cardiac output is the mainstay of hemodynamic resuscitation. Not all patients respond to fluid therapy, and excessive fluid administration is harmful. Predicting fluid responsiveness can be challenging, particularly in children. Numerous hemodynamic variables have been proposed as predictors of fluid responsiveness. Dynamic variables based on the heart–lung interaction appear to be excellent predictors of fluid responsiveness in adults, but there is no consensus on their usefulness in children.

METHODS: We systematically reviewed the current evidence for predictors of fluid responsiveness in children. A systematic search was performed using PubMed (1947–2013) and EMBASE (1974–2013). Search terms included fluid, volume, response, respond, challenge, bolus, load, predict, and guide. Results were limited to studies involving pediatric subjects (infant, child, and adolescent). Extraction of data was performed independently by 2 authors using predefined data fields, including study quality indicators. Any variable with an area under the receiver operating characteristic curve that was significantly above 0.5 was considered predictive.

RESULTS: Twelve studies involving 501 fluid boluses in 438 pediatric patients (age range 1 day to 17.8 years) were included. Twenty-four variables were investigated. The only variable shown in multiple studies to be predictive was respiratory variation in aortic blood flow peak velocity (5 studies). Stroke volume index, stroke distance variation, and change in cardiac index (and stroke volume) induced by passive leg raising were found to be predictive in single studies only. Static variables based on heart rate, systolic arterial blood pressure, preload (central venous pressure, pulmonary artery occlusion pressure), thermodilution (global end diastolic volume index), ultrasound dilution (active circulation volume, central blood volume, total end diastolic volume, total ejection fraction), echocardiography (left ventricular end diastolic area), and Doppler (stroke volume index, corrected flow time) did not predict fluid responsiveness in children. Dynamic variables based on arterial blood pressure (systolic pressure variation, pulse pressure variation and stroke volume variation, difference between maximal or minimal systolic arterial blood pressure and systolic pressure at end-expiratory pause) and plethysmography (pulse oximeter plethysmograph amplitude variation) were also not predictive. There were contradicting results for plethymograph variation index and inferior vena cava diameter variation.

CONCLUSIONS: Respiratory variation in aortic blood flow peak velocity was the only variable shown to predict fluid responsiveness in children. Static variables did not predict fluid responsiveness in children, which was consistent with evidence in adults. Dynamic variables based on arterial blood pressure did not predict fluid responsiveness in children, but the evidence for dynamic variables based on plethysmography was inconclusive.

From the *Department of Anesthesiology, Pharmacology, and Therapeutics, University of British Columbia; Department of Anesthesia, BC Children’s Hospital, Vancouver, Canada; Department of Anesthesiology and Perioperative Care, University of California, Irvine, School of Medicine, Irvine, California; and §Department of Anaesthesia, University College London Trust, London, United Kingdom.

Accepted for publication August 9, 2013.

Funding: Not funded.

Conflict of Interest: See Disclosures at the end of the article.

Reprints will not be available from the authors.

Address correspondence to Heng Gan, MBBCh, MRCPCH, FRCA, Department of Anesthesia, Room V3-317, 950 W. 28th Ave., Vancouver, British Columbia V5Z 4H4 Canada. Address e-mail to heng.gan@gmail.com.

The goal of hemodynamic resuscitation is to achieve adequate oxygen delivery by maintaining adequate cardiac output and perfusion pressure. This is typically attempted initially with intravascular volume expansion. However, in pediatric studies examining fluid responsiveness, only 40% to 69% of children responded to intravascular volume expansion.1–9 Appropriate early fluid resuscitation improves survival,10,11 but excessive fluid therapy increases mortality.12–14 Clinical assessment of hemodynamic status based on physical examination and routine monitoring is inadequate, particularly in children.15–17 These findings underline the need for more reliable predictors of fluid responsiveness.

Numerous hemodynamic variables have been proposed as predictors of fluid responsiveness. Static variables are based on a single observation in time. This includes clinical observations such as heart rate and arterial blood pressure, preload pressures such as central venous pressure (CVP) and pulmonary artery occlusion pressure, and preload volume estimates from thermodilution and ultrasound dilution.

Dynamic variables reflect the variation in preload induced by mechanical ventilation. With positive pressure ventilation, the vena cava blood flow is impeded during inspiration, causing a decrease in venous return and pulmonary artery blood flow. This effect on venous return can be quantified as inferior vena cava diameter variation (ΔIVCD). Approximately 3 heart beats later (pulmonary transit time), this decrease in blood flow is transmitted to the left heart, resulting in a decrease in left ventricular end diastolic volume and consequently stroke volume.18 This ventilation-induced variation in stroke volume is then observed downstream as variation in aortic blood flow, arterial blood pressure, and plethysmographic waveform amplitude. The degree of variation observed is larger in hypovolemia19,20 when the heart is functioning at the steep portion of the Frank–Starling relationship. Dynamic variables quantify this variation as the percentage difference between the maximal and the minimal measured value in a single breath, indexed to either the maximum, mean, or midpoint value. For example, one of the earliest dynamic variables, systolic pressure variation (SPV) is calculated as:

SPV (%) = (SAPmax − SPBmin)/[(SAPmax + SAPmin)/2] × 100

where SAP is systolic blood pressure. An average is usually taken over a period of time or a number of respiratory cycles.

Many of these hemodynamic variables have been consistently shown to be the best predictors of fluid responsiveness in adults. However, in the pediatric population, the predictive value of these variables remains controversial. We systematically reviewed the current evidence for the value of these static and dynamic variables on predicting fluid responsiveness in children.

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METHODS

Selection of Studies

Published studies evaluating the predictive factors of fluid responsiveness in pediatric patients in the perioperative and critical care settings were included. Studies were identified by electronic searches of PubMed (from 1947) and EMBASE (from 1974) databases. The final search was performed on 2 July, 2013. Results were limited to studies involving pediatric subjects by using MeSH terms (PubMed) or by setting search limits (EMBASE). Search terms included fluid, volume, response, respond, challenge, bolus, load, predict, and guide. Study selection was performed independently by HG and JMA. Figure 1 illustrates the full search strategy.

Figure 1

Figure 1

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Data Extraction

Using a data extraction spreadsheet, 1 review author (HG) extracted data from included studies, and a second author (JMA) checked the extracted data. Information was extracted from each included study on:

  • Characteristics of trial participants (age, weight, relevant clinical diagnosis, vasoactive therapy)
  • Characteristics of study (clinical setting, ventilation, fluid bolus type, volume, and duration)
  • Reference standard used (definition of response, method of assessment)
  • Variables tested (method of assessment, area under receiver operating characteristic [ROC] curve)
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Summary Measures

The areas under the ROC curves and their 95% confidence intervals (CIs) were the primary measures for comparison. Standard deviations (SDs) and 95% CI, when not quoted, were reconstructed from P values if available. Any variable with an area under the ROC curve that was significantly above 0.5 (i.e. the lower limit of the 95% CI was above 0.5) was considered predictive. Any variable with a 95% CI overlapping 0.5 is no better than chance and was considered not predictive. No meta-analysis was done due to the limited number of studies and diverse study characteristics.

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Risk of Bias

Using an 18-item assessment tool (Appendix), which incorporated the recommendations from Quality Assessment of Diagnostic Accuracy Studies (QUADAS)21 and the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy,22 the quality and risk of bias of individual studies were assessed by consensus based on joint review by 2 authors (HG and JMA).

To assess for publication bias, we created funnel plots of the area under the ROC curve against sample size for each variable that was investigated by 5 or more studies. The funnel plot is based on the fact that precision increases with sample size. Results from smaller studies will scatter widely at the bottom of the graph, with the spread narrowing among larger studies. The funnel plots were assessed visually for symmetry. In the absence of bias the plot will resemble a symmetrical inverted funnel.23 To formally test for asymmetry, we applied Egger regression tests on secondary funnel plots of log (area under ROC/[1-area under ROC]) against precision, using a significance level of 0.1 because of small sample size.23

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RESULTS

Study Characteristics

We included 12 studies, totaling 501 fluid boluses in 438 pediatric patients (age range 1.0 day to 17.8 years). All but 29 patients’ lungs were mechanically ventilated. Six studies (156 patients) standardized tidal volume at 10 mL/kg. Positive end-expiratory pressure, where reported, ranged from 0 to 5 cm H2O. Three studies used crystalloid as fluid bolus, 6 studies used colloid, 2 studies used a mixture of fluids, and 1 study did not report type of fluid given. The hemodynamic response to intravascular volume expansion was defined by an increase in stroke volume in 10 studies, cardiac output in 1 study and arterial blood pressure in another. Cardiac diagnoses were present in 176 patients (40%) from 6 studies. Vasoactive drugs were administered during study measurements to 92 patients (21%) from 5 studies. All potential predictors studied and their abbreviations are listed in Table 1. Study characteristics are detailed in Table 2.

Table 1

Table 1

Table 2

Table 2

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Static Variables

Twelve static variables were included in this review, although only heart rate and CVP were investigated by >1 study. The area under the ROC curve and its 95% CI for each static variable is illustrated in Figure 2.

Figure 2

Figure 2

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Clinical and Preload Variables

The area under the ROC curve for heart rate was not significantly >0.5 in 3 pediatric studies.6,8 Similarly, the area under ROC for SAP was not significantly >0.5 in 1 study.24

In eight studies that investigated the predictive value of CVP and 1 study the predictive value of pulmonary artery occlusion pressure, none of the areas under the ROC curves were significantly >0.5.2,5,7–9,24–26

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Thermodilution and Ultrasound Dilution

In 1 pediatric study of 26 children with atrial or ventricular septal defects (ASD/VSD), the area under the ROC curve for global end diastolic volume index was not significantly >0.5.7

One pediatric intensive care unit (PICU) study, using ultrasound dilution to measure stroke volume, investigated using measurements derived from ultrasound dilution (total end diastolic volume, active circulation volume, central blood volume, and total ejection fraction) to predict fluid responsiveness.9 The study found that the 95% CI for areas under the ROC curve for these variables overlapped 0.5.

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Echocardiography and Doppler

The use of left ventricular end diastolic area measured by transthoracic echocardiography to predict fluid responsiveness was investigated in 1 pediatric study and was found not to predict fluid responsiveness.6 Stroke volume index (SVI) predicted fluid responsiveness (95% CI lower limit 0.80) in 1 perioperative study involving 50 children younger than 6 months.3 One pediatric study found that corrected flow time (FTc) predicted fluid responsiveness in general PICU patients (95% CI lower limit 0.58) but not in postoperative cardiac patients.25

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Dynamic Variables

Ten dynamic variables were included in this review. The area under the ROC curve and its 95% CI for each dynamic variable is illustrated as a forest plot in Figure 3.

Figure 3

Figure 3

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Arterial Pressure

Four pediatric studies found that SPV did not accurately predict fluid responsiveness in children.1,9,24,26 Of the 7 studies investigating the use of pulse pressure variation (PPV) to predict fluid responsiveness in children, 6 had negative results.1,5–7,9,24,26 In contradiction, 1 study, involving 26 children aged 0 to 2 years, found that PPV predicted fluid responsiveness both before (95% CI lower limit 0.59) and after (95% CI lower limit 0.58) ASD/VSD closure.7 Three studies investigating the use of stroke volume variation (SVV) in children did not find SVV predictive of fluid responsiveness,7,9,26 except in 1 study where SVV was predictive (95% CI lower limit 0.56) of fluid responsiveness in children after surgical repair of ASD/VSD.7 One study investigated the difference between minimal and maximal SAP and SAP at end-expiratory pause (ΔDown and ΔUp, respectively) and found neither had areas under the ROC curve that was significantly >0.5.24

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Plethysmography

Two pediatric studies involving a total of 49 patients found that pulse oximeter plethysmograph amplitude variation (ΔPOP) did not predict fluid responsiveness in children.5,6

Two pediatric studies found plethysmograph variability index (PVI) predictive (95% CI lower limit 0.58 and 0.77) of fluid responsiveness.8,24 Two other pediatric studies, however, did not find PVI to predict fluid responsiveness in children.5,6

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Echocardiography and Doppler

Five studies investigated the use of respiratory variation in aortic blood flow peak velocity (ΔVpeak) to predict fluid responsiveness in children in the operating room6,7,24 and the intensive care unit1,2 and found that ΔVpeak predicted fluid responsiveness in children (95% CI lower limits 0.64, 0.61, 0.66, 0.82, 0.73, and 0.73). One study found stroke distance variation (ΔVTI) measured using transesophageal echocardiography a predictor of fluid responsiveness (95% CI lower limit 0.65) in children with congenital heart disease.7 ΔIVCD was found to predict fluid responsiveness in children in a study in the PICU (95% CI lower limit 0.69)2 but not in another study in the operating room (95% CI lower limit 0.16).24

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Passive Leg Raising

One study investigated using hemodynamic changes induced by passive leg raising (PLR) to predict fluid responsiveness in 40 PICU patients, the most which were spontaneously breathing.4 The study demonstrated that changes in cardiac index and stroke volume induced by PLR (ΔCIPLR and ΔSVPLR, respectively) predicted fluid responsiveness (95% CI lower limits 0.55 and 0.59, respectively). However, after multivariate analysis using logistic regression analysis, the study concluded that only ΔCIPLR significantly correlated with fluid responsiveness.

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Risk of Bias

Table 3 summarizes the assessment of quality and risk of bias of individual studies. Twenty-one of 216 items were initially assessed differently but were resolved by consensus. The quality of the most studies was high with low risk of bias, except for the 2 included abstracts,5,9 which lacked detailed description of study methodology. Most studies could not avoid incorporation bias because the same modality (e.g., transthoracic echocardiography) was used both as the reference and as the index variable. None of the studies established cutoff values prospectively because they were using area under the ROC curve as effect measure. Five studies tested for observer variation in echocardiography and Doppler measurements.

Table 3

Table 3

Funnel plots of the area under the ROC curve against sample size were constructed for CVP, PPV, and ΔVpeak (Fig. 4), the only variables investigated by 5 or more studies. The funnel plot for PPV appeared symmetrical, but the funnel plot for CVP and ΔVpeak appeared asymmetrical. However, Egger regression test for symmetry showed nonsignificant P values (0.46, 0.33, and 0.17, respectively for CVP, PPV, and ΔVpeak), suggesting low risk of publication bias.

Figure 4

Figure 4

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APPENDIX

Table

Table

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DISCUSSION

Static Variables

The studies included in this review failed to demonstrate any predictive value in heart rate, CVP, pulmonary artery occlusion pressure, left ventricular end diastolic area, global end diastolic volume index, and ultrasound dilution measurements (total end diastolic volume, active circulation volume, central blood volume, and total ejection fraction). The only static variables that potentially have predictive values are SVI and FTc. SVI was found to be predictive of fluid responsiveness in 1 study of 50 pediatric patients undergoing general surgery.3 There are, however, no other pediatric studies to support this result.

FTc is derived from transesophageal Doppler measurement of blood flow in the descending thoracic aorta. Flowtime is measured from the beginning to end of the aortic velocity waveform. FTc controls for varying heart rate using the equation FTc = Flowtime/√cycletime.

FTc is influenced by preload as well as contractility and systemic vascular resistance.27,28 In individual patients, FTc may increase with fluid administration and increased stroke volume and has been used to guide intraoperative fluid management in adults.29 Studies in adults using FTc to predict fluid responsiveness are inconclusive.30,31 The only pediatric study found that FTc predicted fluid responsiveness in general PICU patients but not in cardiac patients who had comparatively lower cardiac index and higher (and more variable) systemic vascular resistance.25

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Dynamic Variables

Dynamic preload variables are those which reflect the cyclical changes in left ventricular stroke volume induced by positive pressure ventilation. Several systematic reviews and meta-analyses suggest that in adults, dynamic variables based on arterial blood pressure (SPV, PPV, and SVV) and plethysmography (ΔPOP and PVI) all have excellent predictive value.32–36 The evidence for this in children is disappointingly poor. Dynamic parameters extracted from the arterial pressure waveform (SPV, PPV, SVV, ΔDown, and ΔUp) on the whole do not predict fluid responsiveness in children. PPV and SVV have only been demonstrated in single studies to predict fluid responsiveness in children with congenital heart disease.7,8 There were contradicting results for the predictive value of dynamic parameters based on the plethysmographic waveform. Studies agreed ΔPOP did not predict fluid responsiveness in children, but there were conflicting results for PVI. Only ΔVpeak appeared to predict fluid responsiveness in children. We explore a number of possible explanations.

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Thoracic/Lung Compliance and Ventilation

Children have higher chest wall and lung compliance.37 The variation in intrathoracic pressure with normal tidal volume ventilation may not cause significant circulatory changes in children. In adults, a tidal volume of at least 8 mL/kg is required.38 Most of the pediatric studies in this review used a tidal volume of 10 mL/kg, although some either did not report or did not control tidal volumes used. Four studies investigating ΔVpeak demonstrated that there is indeed measurable variation in aortic blood flow in children with ventilation tidal volumes of 10 mL/kg.2,6,7,24

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Arterial Blood Pressure, Vascular Compliance, and Cardiac Compliance

SPV and PPV are dynamic preload indicators based on arterial blood pressure. They quantify the magnitude of change induced by positive pressure ventilation in SAP and pulse pressure (PP), respectively. SPV is calculated using the maximal and minimal SAP values measured in a single respiratory cycle:

SPV (%) = (SAPmax − SPBmin)/[(SAPmax + SAPmin)/2] × 100

PPV is calculated with PP using an analogous formula. An average is taken either over 3 consecutive respiratory cycles or over 30 seconds.7,39,40

Respiratory-induced changes in cardiac stroke volume centrally are measured as changes in the arterial blood pressure peripherally. Children have a more compliant arterial tree than adults.41,42 The magnitude of the change in arterial blood pressure induced by ventilation is smaller in a more compliant arterial vascular system because the peripherally measured PP is dependent on arterial compliance.43 This may explain why dynamic variables based on arterial blood pressure in general do not predict fluid responsiveness in children.1,5,6,9 The picture is less clear in children with intracardiac shunts who may have reduced arterial compliance due to sympathetic upregulation.5,44 Renner et al.7 found PPV to predict fluid responsiveness in a study of children before and after ASD or VSD closure. However, when PPV was examined in children after transcatheter closure of intracardiac shunts, it was not a predictive value of fluid responsiveness.5

Animal studies suggest that immature ventricles are less compliant and have less steep Starling curves.45 A recent study demonstrated PPV values that were lower in immature pigs compared with adult pigs with the same degree of hypovolemia.46 Reduced cardiac compliance may be a possible explanation why PPV and SPV do not predict fluid responsiveness as well in children compared with adults.

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Flow Variables

The most convincing predictor was ΔVpeak, a direct ultrasound measurement of variations in aortic blood flow induced by small reversible changes in preload due to ventilatory-induced changes in venous return. Unlike variables based on arterial blood pressure or plethysmographic measurements, flow measurements are not affected by arterial compliance or changes in arterial tone.47 ΔVpeak is measured using Doppler echocardiography, which requires a skilled operator.

This may limit its utility in routine clinical practice, despite being a reliable predictor of fluid responsiveness.

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Plethysmographic Variables

The plethysmographic waveform is the quantity of infrared light detected by the oximeter photo detector, which varies during the cardiac cycle. The plethysmographic waveform amplitude is measured beat to beat as the vertical distance between peak and preceding valley in the output waveform. ΔPOP is then calculated using a formula analogous to that of SPV and PPV. The plethysmographic gain factor is held constant during recording so that the waveform amplitude is not modified by automatic gain adjustment.39,48 Similar to the calculation of PPV, an average is usually taken over 3 consecutive respiratory cycles.39,48

PVI (Masimo Corp., Irvine, CA) is an algorithm for automated and continuous representation of the respiratory variations in the plethysmographic waveform amplitude, using the perfusion index (PI) as a surrogate. PVI is the maximum change in PI over a time interval that includes at least 1 complete respiratory cycle:

PVI = [(PImax − PImin)/PImax] × 100

The plethysmographic waveform reflects the amount of infrared light detected by the pulse oximeter sensor, which is dependent on the blood volume in the tissue where the oximeter sensor is placed. The blood volume depends on vascular distensibility and stroke volume.49 Since vascular distensibility is considered unchanged during the course of 1 mechanical breath, respiratory-induced changes in the arterial pulse pressure become the main determinants of PVI and ΔPOP. Indeed, ΔPOP has been shown to be correlated to SPV50 and PPV39 in adults, and to PPV in children.51 Furthermore, the increased impedance to venous return during a positive pressure inspiration may increase the volume of venous blood, thus exaggerating the decrease in plethysmographic wave amplitude.49 PVI and ΔPOP should therefore be expected to be more sensitive to ventilator-induced changes than SPV or PPV. In adult studies, PVI and ΔPOP predicted fluid responsiveness, both in the perioperative32,48,52–54 and critical care settings.55,56 Disappointingly, in children, ΔPOP did not predict fluid responsiveness. PVI was found to predict fluid responsiveness in 2 pediatric studies8,24 but not in 2 other pediatric studies.5,6 This contradicting result is difficult to explain. The studies all involved patients of similar ages, and their lungs were ventilated at tidal volumes of 10 mL/kg. The 2 positive studies used colloid fluid boluses, and the 2 negative studies used crystalloid, but this should not influence the results, particularly since all 4 studies similarly defined fluid responsiveness as an increase in stroke volume measured using echocardiography. One of the positive studies was in children with congenital heart disease,8 which may be explained by reduced vascular compliance due to sympathetic upregulation.5,44 However, this does not explain the other positive study in children undergoing neurosurgery.24

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Passive Leg Raising

ΔCIPLR appeared to be an excellent predictor of fluid responsiveness in children, which is consistent with findings in adult studies.57 In adults, PLR induces an “autotransfusion” of approximately 150 mL.58 Children have a lower leg-length to body-length ratio than adults,59 so the effect of PLR may be smaller. A major advantage of PLR-induced changes in cardiac output as a predictor of fluid responsiveness is that its accuracy seemed unaffected by ventilation mode, underlying cardiac rhythm, and technique of measurements.57 ΔCIPLR is also completely reversible,4,60,61 and therefore, any detrimental effects of unnecessary fluid administration are only minimal and temporary.

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Limitations

A number of different thresholds were used for fluid responsiveness. The most common definition of fluid responsiveness was change in stroke volume of >15% as measured by transesophageal or transthoracic echocardiography. This seemed reasonable, as 15% is more than the expected error of echocardiographic measurements and is generally considered clinically relevant. Other studies used a 10% increase as threshold, or measured stroke volume differently, using transesophageal Doppler, thermodilution or ultrasound dilution. An increase in mean arterial blood pressure or cardiac output was also used to define fluid responsiveness, which in our opinion was inappropriate. An increase in arterial blood pressure or cardiac output would not necessarily reflect increased stroke volume. All studies measured fluid responsiveness within 10 minutes of a fluid bolus, except for 2 studies which did not report a time interval.

There was no uniformity in the type, volume, and rate of fluid bolus across the studies. A slow small volume bolus of crystalloid would have a different impact on intravascular volume expansion compared with a rapid large-volume bolus of colloid. However, the proportion of subjects with a positive fluid response was consistently approximately 50% across the studies.

Most of the studies had subjects with a median age younger than 3 years, but the ranges did vary, and some included subjects in their late teens. It is difficult to interpret the validity of pooled results from subjects of diverse weights and physiology (e.g., vascular compliance, stroke volume). One study included 2 separate age groups, but their results did not differ significantly between the age groups.6

All except 1 study involved fewer than 50 fluid boluses. Most variables were only included in single studies. Only CVP, PPV, SPV, PVI, and ΔVpeak were investigated by 4 or more studies.

Seven studies included patients receiving vasoactive infusions, which would have affected vascular compliance. Eighteen of 26 subjects in the Renner et al.7 study received enoximone and epinephrine after VSD closure. Two of the 5 studies supporting ΔVpeak as a predictor of fluid responsiveness included patients receiving norepinephrine, milrinone, or dopamine.1,2

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Conclusions

ΔVpeak was the only variable to reliably predict fluid responsiveness in children. Most static variables did not predict fluid responsiveness in children, which was consistent with findings in adults. However, in contrast to adults, dynamic variables based on arterial blood pressure also did not predict fluid responsiveness in children. The evidence for dynamic variables based on plethysmography was inconclusive. Children have different lung, vascular, and cardiac compliances compared with adults. Research is needed to explain how these affect the ability of dynamic variables to predict fluid responsiveness in children.

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RECUSE NOTE

Maxime Cannesson is the Section Editor for Technology, Computing, and Simulation for the Journal. This manuscript was handled by Peter Davis, Section Editor for Pediatric Anesthesiology, and Dr. Cannesson was not involved in any way with the editorial process or decision.

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DISCLOSURES

Name: Heng Gan, MBBCh, MRCPCH, FRCA.

Contribution: This author helped design the study, collect and analyze the data, and prepare this manuscript.

Attestation: This author approved the final manuscript.

Conflicts of Interest: The author has no conflicts of interest to declare.

Name: Maxime Cannesson, MD, PhD.

Contribution: This author helped design the study and prepare this manuscript.

Attestation: This author approved the final manuscript.

Conflicts of Interest: Sironis is a biomedical company I cofounded in 2010 to develop closed-loop fluid management systems and noninvasive hemodynamic monitoring tools. I hold 37% equity interest in Sironis. During the past 5 years, I have consulted and/or have prepared CME materials for Covidien, Draeger, Philips Medical System, Edwards Lifesciences, Fresenius Kabi, Masimo Corp., and ConMed. My department has received research fundings from Edwards Lifesciences and Masimo Corp. to support clinical studies for which I act as principal investigator.

Name: John R. Chandler, MBBCh, FCARCSI, FDSRDS.

Contribution: This author helped prepare this manuscript.

Attestation: This author approved the final manuscript.

Conflicts of Interest: The author has no conflicts of interest to declare.

Name: J. Mark Ansermino, MBBCh, MSc (Inf), FFA (SA), FRCPC.

Contribution: This author helped design the study, analyze the data, and prepare this manuscript.

Attestation: This author approved the final manuscript.

Conflicts of Interest: The author has no conflicts of interest to declare.

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ACKNOWLEDGMENTS

Partial salary support for HG was received from a Canadian Institutes of Health Research Grant to JMA. The authors wish to thank Dorothy Myers (manuscript presentation) and Guohai Zhou (statistical analysis) for their contributions.

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