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 Table of Contents  
REVIEW ARTICLE
Year : 2019  |  Volume : 32  |  Issue : 2  |  Page : 397-404

Fluid responsiveness in hemodynamically unstable patients: a systematic review


1 Department of Anesthesia, Faculty of Medicine, Menoufia University, Shebeen El-Kom, Egypt
2 Department of Anesthesia, Faculty of Medicine, Zagazig University, Zagazig, Egypt

Date of Submission14-Jan-2018
Date of Acceptance18-Feb-2018
Date of Web Publication25-Jun-2019

Correspondence Address:
Abdallah NA Khattab
El-Gomohorya Street, Menoufia 11711
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/mmj.mmj_8_18

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  Abstract 

Objective
To evaluate the usefulness of different hemodynamic measurements in the detection of fluid responsiveness (FR) among patients with hemodynamic instability.
Backgrounds
Close hemodynamic monitoring of unstable patients' response to volume expansion is a central component of intensive care. Previous reports demonstrated that only 50% of patients respond to fluid administration with an increase in stroke volume or cardiac output.
Data sources
A computer literature search of PubMed, Scopus, and Cochrane Central was conducted.
Study selection
Records were screened for eligible studies according to the predetermined inclusion criteria.
Data extraction
Data were extracted and synthesized using standardized tables.
Data synthesis
Data were synthesized qualitatively, and we did not perform a quantitative data analysis.
Findings
The present review included 21 studies of moderate to high quality. All included studies found that CVP does neither correlate with intravascular volume nor accurately predict the FR. The included studies showed significant heterogeneity regarding the predictive value of pulse pressure variation and stroke volume variation. Passive leg raising-induced changes in cardiac output or related parameters were highly predictive for FR.
Conclusion
The current literature suggests that the static measures are not reliable indicators of FR. Dynamic indices are of good predictive value regarding FR. In addition, passive leg raising appears to be the most useful test for predicting FR in hemodynamically unstable adults.

Keywords: cardiac output, fluid therapy, stroke volume


How to cite this article:
Atallah HA, Gaballah KM, Khattab AN. Fluid responsiveness in hemodynamically unstable patients: a systematic review. Menoufia Med J 2019;32:397-404

How to cite this URL:
Atallah HA, Gaballah KM, Khattab AN. Fluid responsiveness in hemodynamically unstable patients: a systematic review. Menoufia Med J [serial online] 2019 [cited 2019 Sep 15];32:397-404. Available from: http://www.mmj.eg.net/text.asp?2019/32/2/397/260925




  Introduction Top


Patients with hemodynamic instability are at increased risk of serious consequences, including multiorgan failure and death[1]. Hemodynamic instability occurs mainly because of cardiovascular collapse, with clinically significant decrease in the blood pressure and tissue hypoperfusion[2]. Although the acute circulatory failure may develop because of wide variety of causes, such as cardiogenic, hypovolemic, obstructive, or distributive (septic) cardiovascular insufficiency, restoration of the body volume status is critical regardless of the cause[3]. Fluid resuscitation remains the cornerstone of resuscitation in the critically ill and injured patient. Although inadequate resuscitation would compromise patients' status, the current body of literature suggests that volume overload is associated with increased risk of unfavorable clinical outcomes[1].

Therefore, close hemodynamic monitoring of unstable patients' response to volume expansion is a central component of intensive care. Fluid responsiveness (FR) is defined as the ability to restore normal stroke volume (SV) or cardiac output (CO) in response to fluid administration. Previous reports demonstrated that only 50% of patients respond to fluid administration with an increase in SV or CO[2]. Such findings reflect the critical role of proper discrimination between the fluid responders and nonresponders[3]. Specific hemodynamic variables are commonly measured, and their values are often used in clinical decision making. However, there is a growing body of evidence that questions the clinical utility of some of the commonly used measures[1].

Central venous pressure (CVP) is one of the most commonly used static measures for prediction of FR. However, Marik and colleagues reported a weak association between CVP and blood volume, and CVP exhibited poor predictive value for FR as well[4]. The usefulness of parameters measured using the pulmonary artery catheter showed no benefit in patient outcome in a number of clinical trials as well[3]. Dynamic measures, such as systolic pressure and pulse pressure variations, were proposed to accurately monitor the changes in the preload and subsequently accurate estimation of FR. However, the accuracy of dynamic measures is limited in patients with spontaneous breathing; in addition, dynamic parameters were reported to poorly detect FR in patients with tidal volumes below 8 ml/kg or patients with low lung compliance[5].

Functional hemodynamic measures for the assessment of patients' responsiveness to cardiovascular therapies have been proposed recently. The changes of these measurements in relation to the effect of treatment can have a great clinical utility. One of the emerging functional monitoring techniques is the fluid challenge; a volume challenge is widely considered as a test to identify those who are preload responsive[6]. However, fluid challenge may delay the primary therapy, and it can exaggerate the pre-existing pulmonary edema. Therefore, several surrogate methods of creating reversible or transient volume challenges, including breathing and passive leg raising (PLR), have been advocated[5].

Therefore, we performed the present systematic review to evaluate the usefulness of different hemodynamic measurements in the detection of FR among patients with hemodynamic instability.


  Patients and Methods Top


Inclusion and exclusion criteria for study selection

We used the following inclusion criteria: studies that were randomized controlled trials or prospective studies; studies on patients with hemodynamic instability defined as refractory hypotension, signs of organ hypoperfusion, or both; studies that included sample size of more than 20 patients; and studies reporting the accuracy of hemodynamic measurements in the detection of FR.

Studies were excluded if they were in non-English language or were theses or conference abstracts. In case of multiple reports, we analyzed data from the most complete data set.

Search strategy

We searched the following medical electronic databases: PubMed, Cochrane Central Register of Controlled Trials, Scopus, and Web of science from the inception to December 2017 using the following keywords: 'Fluid Therapy' (Mesh) AND 'Hemodynamic Monitoring'. Retrieved citations were downloaded, and duplicates of retrieved records were removed using EndNote version 7 (EndNote Spring Garden Street, Fourth Floor, Philadelphia).

Selection of studies

The authors screened the title and abstract of retrieved records for eligibility. We retrieved the full texts of potentially eligible abstracts, and they were screened for eligibility to be included in the present systematic review.

Data extraction

The authors extracted the raw data independently from each included study using a standardized online data extraction form. The extracted data included the following: (a) characteristics of study design, (b) characteristics of study population, (c) risk of bias domains, and (d) study outcomes: the diagnostic accuracy of different hemodynamic measures for FR.

Assessment of risk of bias in included studies

The authors independently assessed the quality of each included study in strict accordance with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool criteria. The QUADAS-2 is a tool for the quality assessment of diagnostic accuracy studies, which includes the following domains: patient selection, index test, reference standard, and flow and timing.


  Results Top


We retrieved 769 unique citations following databases screening; from which, 97 citations were retained for full-text screening. Finally, 21 studies (static measures = 6; dynamic measures = 15) were included in the present review with 949 patients. [Figure 1] shows the PRISMA flow chart.
Figure 1: Shows the PRISMA flow chart.

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The quality of the included studies was from medium to high according to the QUADAS-2. [Figure 2] shows the summary of the quality assessment.
Figure 2: Shows the summary of the quality assessment.

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Among the 21 included studies, 16 studies were prospective cohorts, four studies were nonrandomized interventional trials, and one study was retrospective review. The sample size of the included studies ranged from 23 to 102 patients. Thirteen studies included sedated, mechanically ventilated patients who required volume resuscitations, whereas the remaining studies included spontaneously breathing ICU patients. FR was defined as an increase in CO of at least 10 or 15% in the included studies; the prevalence of FR ranged from 42 to 56% among the included patients.

The most commonly reported cause of hemodynamic instability was sepsis, which was followed by nonseptic systemic inflammatory response syndrome. Overall, 66% of the patients were treated with vasopressors at inclusion. In addition, the physical findings in the hemodynamically unstable patients were oliguria, hypotension, and tachycardia.

Six included studies reported the accuracy of static measurements in predicting the FR. CVP was measured in all included studies, and the reported best cutoff value was 7–9 mmHg. Interestingly, all included studies found that CVP does neither correlate with intravascular volume nor accurately predict the FR. Heenen et al.[7] evaluated the pulmonary artery occluded pressure (PAOP) and right atrial pressure as well. They reported that inspiratory changes in right atrial pressure failed to predict the response to volume expansion. Another study by Osman et al.[8] reported that cardiac filling pressures are poor predictors of FR in patients with sepsis.

On the contrary, Muller et al.[9] conducted a nonrandomized trial on 35 mechanically ventilated patients and concluded that both intrathoracic blood volume index and CVP are able to predict FR in critically ill patients with acute circulatory failure [Table 1].
Table 1: Demographics and clinical characteristics of the included studies that reported static measurements

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As mentioned previously, 13 included studies assessed the accuracy of a large number of dynamic measurements for prediction of FR, from which only three studies included spontaneously breathing patients. The sample size of the included studies ranged from 31 to 102 patients, and the included studies assessed the accuracy of the following measures:

Eight included studies assessed the accuracy of pulse pressure variation (PPV) in either mechanically ventilated or spontaneously breathing patients[13]. Studies that included mechanically ventilated patients used low (<7 ml/kg) or high (>7 ml/kg) tidal volumes, as the accuracy of PPV varies according to the tidal volume used in positive pressure ventilation. The mean cutoff for PPV ranged from 8 to 12%. The included studies showed significant heterogeneity regarding the predictive value of PPV; some included studies reported that the PPV is a reliable predictor of FR at low tidal volume only, whereas other reports showed that PPV is a good predictor regardless of the tidal volume [Table 2].
Table 2: Demographics and clinical characteristics of the included studies that reported dynamic measurements

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Six included studies assessed the accuracy of stroke volume variation (SVV) for predicting the FR. The mean cutoff for SVV ranged from 10 to 13%. The included studies reported that the SVV is an accurate predictor for FR; however, these results should be interpreted cautiously owing to the small sample size within the included studies [Table 2].

Charbonneau et al.[21] compared the predictive value of the echocardiographic indices based on respiratory variations of superior vena cava and inferior vena cava diameters (ΔSVC and ΔIVC, respectively) among mechanically ventilated patients with sepsis. They found that ΔSVC was better than ΔIVC in predicting FR; however, the sensitivity and specificity values of ΔSVC and ΔIVC for predicting FR were lower than previously published studies. Another report showed a high accuracy of ΔIVC in detecting FR in mechanically ventilated patients with septic shock[14]. Airapetian et al.[22] showed that vena cava size and respiratory variability do not predict FR in spontaneously breathing patients.

Feissel et al.[16] assessed the predictive value of respiratory changes in the amplitude of the Δ plethysmographic pulse wave in mechanically ventilated patients with sepsis. Interestingly, they reported that Δ plethysmographic pulse wave is as accurate as PPV for predicting FR in mechanically ventilated patients with sepsis with a cutoff value of 14%. On the contrary, Lakhal et al.[17] reported that the Δrespiratory pulse pressure variation performance was poor during protective mechanical ventilation for early acute respiratory distress syndrome.

Ignacio et al.[18] reported that the respiratory variations in brachial artery peak velocity could be a feasible tool for the noninvasive assessment of FR in patients with mechanical ventilator support and acute circulatory failure. However, the other report by Ignacio et al.[24] showed that the changes in partial end-tidal CO2 pressure correlated significantly with FR in mechanically ventilated patients with acute circulatory failure. Finally, Trepte and colleagues assessed the predictive value of the automated respiratory systolic variation test (RSVT) in patients after major surgery. They found that the automated RSVT appears to be as accurate as established dynamic indicators of preload PPV and SVV in patients after major surgery [Table 2][19].

Four included studies assessed the predictive value of change in a number of measures in response to PLR[5],[6],[23],[25],[26]. PLR-induced changes in Δ PP and Δ CO were highly predictive of FR among sedated, mechanically ventilated patients. In addition, Doppler measurements during PLR, such as SVV and PPV, were predictive of FR in spontaneously breathing patients with suspected central hypovolemia. On the contrary, the changes in aortic blood flow induced by PLR predict preload responsiveness in ventilated patients, whereas with arrhythmias and spontaneous breathing activity, respiratory variations of arterial pulse pressure poorly predict preload responsiveness [Table 2].


  Discussion Top


There is a consensus in the published literature about the importance of accurate prediction of FR in hemodynamically unstable patients. In the present systematic review, we included 21 diagnostic test accuracy studies that measured the accuracy of different parameters for the prediction of FR. Our findings showed that the current static parameters, mainly CVP and PAOP, poorly predict the FR in either mechanically ventilated or spontaneously breathing patients. On the contrary, dynamic indices that rely on respiratory variations showed a better prediction of FR, and ΔPP, ΔSV, and ΔRSVT were reported to accurately predict patient response to volume expansion. However, other measures need further investigations. In addition, PLR-induced change in CO or SV showed high predictive values for FR. There is a growing body of evidence questioning the clinical utility of the currently available static measures for the prediction of FR. Static measures, such as CVP and PAOP, were reported to be largely ineffective in a number of clinical trials[20]. This limited value can be attributed to the limitations associated with the use of static measures. They reflect the cardiac filling, which represents intramural pressure; however, preload is determined by transmural pressure. Moreover, they are assumed to reflect the preload; however, the response of a patient to fluids depends on both preload and cardiac contractility, which varies between patients[4]. In concordance with our findings, Bentzer et al.[3] conducted a meta-analysis to assess the predictors of FR in hemodynamically unstable patients, and they reported that CVP study had a modest predictive value for FR, and it poorly predicts patients' response to volume expansion. In contrary to static measures, a number of dynamic measures, as PPV and SSV, were reported to adequately predict FR. The dynamic indices are based on monitoring the change in SV, or one of its derivatives, after inducing a change in the preload while the patients are on positive pressure breathing[3]. Patients who are likely to respond to fluid administration will show larger variations in pulse pressure or SV during the respiratory cycle than patients who are less likely to be FR[4]. Zhang et al.[27] conducted a meta-analysis to investigate the diagnostic value of SVV in predicting FR. They reported that SVV was correlated with FR, with a pooled correlation coefficient of 0.718, and SVV yielded a diagnostic odds ratio, sensitivity, and specificity of 18.4, 0.81, and 0.80, respectively. Similarly, Desgranges et al.[28] included six studies in their meta-analysis to evaluate the diagnostic accuracy of respiratory variation in aortic blood flow peak velocity for the prediction of FR in mechanically ventilated children. They concluded that the aortic blood flow peak velocity is an accurate predictor of FR in children under mechanical ventilation; however, the optimal cutoff value remains uncertain. Another report showed that the pulse oxymetry plethysmographic waveform amplitude and the Pleth Variability Index are good predictors of FR in mechanically ventilated adults[29]. However, dynamic indices have certain limitations. Failure of dynamic methods in detecting FR was reported in patients with spontaneous breathing as well as patients on pressure support ventilation[3]. However, our findings do not support the mentioned limitations, as the included studies that enrolled spontaneously breathing patients showed a good predictive value of dynamic measures. Dynamic parameters were reported to poorly detect FR with tidal volumes below 8 ml/kg and patients with low airway driving pressure below 20 cm/H2O[13]. The included studies, which included mechanically ventilated patients, used low (<7 ml/kg) or high (>7 ml/kg) tidal volumes, as the accuracy of PPV varies according to the tidal volume used in positive pressure ventilation. Moreover, dynamic methods for the detection of FR were reported to be of low value in cases of reduced lung compliance, elevated pulmonary artery pressure, and cardiac abnormality[4]. To avoid fluid overload, volume challenge was proposed to predict who would response to fluid therapy. Fluid challenge is done by administration of a fluid bolus (either extrinsically or intrinsically) and monitoring the resulting effect on SV or CO. PLR creates a transient increase in the preload which is self-volume challenge and a reversible fluid challenge[4]. The published literature shows a large number of different measurement techniques and outcome variables on PLR. In their systematic review and meta-analysis, Cherpanath et al.[30] assessed the diagnostic performance of PLR in different clinical settings and included 23 studies. They reported that PLR retains a high diagnostic performance in various clinical settings and patient groups. The predictive value of a change in pulse pressure on PLR is inferior to a PLR-induced change in a flow variable. Another study reported that changes in CO or related parameters following PLR are useful in a wide range of patients, and variations in pulse pressure or inferior vena cava diameter with positive pressure ventilation have reasonable accuracy in selected subgroups of patients[3].


  Conclusion Top


The current literature suggests that the static measures are not a reliable indicator of FR. Dynamic indices are of good predictive value regarding FR and are likely to predict FR even in spontaneously breathing patients. In addition, PLR appears to be the most useful test for predicting FR in hemodynamically unstable adults and retains a high diagnostic performance in various clinical settings and patient groups.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Antonelli M, Levy M, Andrews PJD, Chastre J, Hudson LD, Manthous C, et al. Hemodynamic monitoring in shock and implications for management. International Consensus Conference, Paris, France, 27–28 April 2006. Intensive Care Med 2007; 33:575–590.  Back to cited text no. 1
    
2.
Michard F, Teboul JL. Predicting fluid responsiveness in ICU patients a critical analysis of the evidence. Chest 2002; 121:2000–2008.  Back to cited text no. 2
    
3.
Bentzer P, Griesdale DE, Boyd J, MacLean K, Sirounis D, Ayas NT. Will this hemodynamically unstable patient respond to a bolus of intravenous fluids? JAMA 2016; 316:1298–1309.  Back to cited text no. 3
    
4.
Hasanin A. Fluid responsiveness in acute circulatory failure. J Intensive Care 2015; 3:50.  Back to cited text no. 4
    
5.
Monge García MI, Cano AG, Romero MG, Pintado RM, Madueño VP, Díaz Monrové JC. Non-invasive assessment of fluid responsiveness by changes in partial end-tidal CO 2 pressure during a passive leg-raising maneuver. Ann Intensive Care 2012; 2:2–9.  Back to cited text no. 5
    
6.
Monnet X, Rienzo M, Osman D, Anguel N, Richard C, Pinsky MR, et al. Passive leg raising predicts fluid responsiveness in the critically ill. Crit Care Med 2006; 34:1402–1407.  Back to cited text no. 6
    
7.
Heenen S, De Backer D, Vincent JL. How can the response to volume expansion in patients with spontaneous respiratory movements be predicted? Crit Care 2006; 10:1–7.  Back to cited text no. 7
    
8.
Osman D, Ridel C, Ray P, Monnet X, Anguel N, Richard C, et al. Cardiac filling pressures are not appropriate to predict hemodynamic response to volume challenge. Crit Care Med 2007; 35:64–68.  Back to cited text no. 8
    
9.
Muller L, Louart G, Bengler C, Fabbro-Peray P, Carr J, Ripart J, et al. The intrathoracic blood volume index as an indicator of fluid responsiveness in critically ill patients with acute circulatory failure: a comparison with central venous pressure. Anesth Analg 2008; 107:607–613.  Back to cited text no. 9
    
10.
Muller L, Louart G, Teboul JL, Mahamat A, Polge A, Bertinchant JP, et al. Could B-type Natriuretic Peptide (BNP) plasma concentration be useful to predict fluid in critically ill patients with acute circulatory failure? Ann Fr Anesth Reanim 2009; 28:531–536.  Back to cited text no. 10
    
11.
Muller L, Louart G, Bousquet PJ, Candela D, Zoric L, de La Coussaye JE, et al. The influence of the airway driving pressure on pulsed pressure variation as a predictor of fluid responsiveness. Spring Garden Street, Fourth Floor: Philadelphia, USA; Intensive Care Med 2010; 36:496-503. doi: 10.1007/s00134-009-1686-y.  Back to cited text no. 11
    
12.
Hanson J, Lam SWK, Alam S, Pattnaik R, Mahanta KC, Uddin Hasan M, et al. The reliability of the physical examination to guide fluid therapy in adults with severe falciparum malaria: an observational study. Intensive Care Med 2013; 12:1.  Back to cited text no. 12
    
13.
Charron C, Fessenmeyer C, Cosson C, Mazoit JX, Hebert JL, Benhamou D, et al. The influence of tidal volume on the dynamic variables of fluid responsiveness in critically ill patients. Anesth Analg 2006; 102:1511–1517.  Back to cited text no. 13
    
14.
Feissel M. The respiratory variation in inferior vena cava diameter as a guide to fluid therapy. Intensive Care Med 2004; l: 1834–1837.  Back to cited text no. 14
    
15.
De Backer D, Vincent J. Pulse pressure variations to predict fluid responsiveness : influence of tidal volume. Intensive Care Med 2005; i: 517–523.  Back to cited text no. 15
    
16.
Feissel M, Teboul J, Merlani P, Badie J, Faller J, Bendjelid K. Plethysmographic dynamic indices predict fluid responsiveness in septic ventilated patients. Intensive Care Med 2007; 33:993–999.  Back to cited text no. 16
    
17.
Lakhal K, Ehrmann S, Benzekri-Lefèvre D, Runge I, Legras A, Dequin P-F, et al. Respiratory pulse pressure variation fails to predict fluid responsiveness in acute respiratory distress syndrome. Crit Care 2011; 15:R85.  Back to cited text no. 17
    
18.
Ignacio M, García M, Cano AG, Carlos J, Monrové D. Brachial artery peak velocity variation to predict fluid responsiveness in mechanically ventilated patients. Crit Care 2009; 13:1–9.  Back to cited text no. 18
    
19.
Trepte CJC, Eichhorn V, Haas SA, Stahl K, Schmid F, Nitzschke R, et al. Comparison of an automated respiratory systolic variation test with dynamic preload indicators to predict fluid responsiveness after major surgery. Br J Anaesth 111:736–742.  Back to cited text no. 19
    
20.
Cecconi M, Monti G, Hamilton MA, Puntis M, Dawson D, Tuccillo ML, et al. Efficacy of functional hemodynamic parameters in predicting fluid responsiveness with pulse power analysis in surgical patients. Minerva Anestesiol 2012; 78:527–533.  Back to cited text no. 20
    
21.
Charbonneau H, Riu B, Faron M, Mari A, Kurrek MM, Ruiz J, et al. Predicting preload responsiveness using simultaneous recordings of inferior and superior vena cavae diameters. Crit Care 2014; 18:473.  Back to cited text no. 21
    
22.
Airapetian N, Maizel J, Alyamani O, Mahjoub Y, Lorne E, Levrard M, et al. Does inferior vena cava respiratory variability predict fluid responsiveness in spontaneously breathing patients? Crit Care 2015; 19:400.  Back to cited text no. 22
    
23.
Lakhal K, Benzekri-lefe D, Wolff M. Central venous pressure measurements improve the accuracy of leg raising-induced change in pulse pressure to predict fluid responsiveness. Intensive Care Med 2010; 36:940–948.  Back to cited text no. 23
    
24.
Ignacio M, García M, Cano AG, Romero MG, Pintado RM, Madueño VP, et al. Non-invasive assessment of fluid responsiveness by changes in partial end-tidal CO2 pressure during a passive leg-raising maneuver. Ann Intensive Care 2012; 2:9.  Back to cited text no. 24
    
25.
Maizel J. Diagnosis of central hypovolemia by using passive leg raising. Intensive Care Med 2007; 33:1133–1138.  Back to cited text no. 25
    
26.
Biais M, Vidil L, Sarrabay P, Cottenceau V, Revel P, Sztark F. Changes in stroke volume induced by passive leg raising in spontaneously breathing patients: comparison between echocardiography and VigileoTM/FloTracTM device. Crit Care 2009; 13:R195.  Back to cited text no. 26
    
27.
Zhang Z, Lu B, Sheng X, Jin N. Accuracy of stroke volume variation in predicting fluid responsiveness: a systematic review and meta-analysis. J Anesth 2011; 25:904–916.  Back to cited text no. 27
    
28.
Desgranges FP, Desebbe O, Pereira de Souza Neto E, Raphael D, Chassard D. Respiratory variation in aortic blood flow peak velocity to predict fluid responsiveness in mechanically ventilated children: a systematic review and meta-analysis. Paediatr Anaesth 2016; 26:37–47.  Back to cited text no. 28
    
29.
Sandroni C, Cavallaro F, Marano C, Falcone C, De Santis P, Antonelli M. Accuracy of plethysmographic indices as predictors of fluid responsiveness in mechanically ventilated adults: a systematic review and meta-analysis. Intensive Care Med 2012; 38:1429–1437.  Back to cited text no. 29
    
30.
Cherpanath TGV, Hirsch A, Geerts BF, Lagrand WK, Leeflang MM, Schultz MJ, et al. Predicting fluid responsiveness by passive leg raising: a systematic review and meta-analysis of 23 clinical trials. Crit Care Med 2016; 44:981–991.  Back to cited text no. 30
    


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