PRISMA Checklist

Section and Topic Item # Checklist item Location where item is reported
TITLE  
Title 1 Identify the report as a systematic review. Title Page
ABSTRACT  
Abstract 2 See the PRISMA 2020 for Abstracts checklist. Abstract
INTRODUCTION  
Rationale 3 Describe the rationale for the review in the context of existing knowledge. Introduction
Objectives 4 Provide an explicit statement of the objective(s) or question(s) the review addresses. Introduction
METHODS  
Eligibility criteria 5 Specify the inclusion and exclusion criteria for the review and how studies were grouped for the syntheses. Inclusion criteria and trial selection
Information sources 6 Specify all databases, registers, websites, organisations, reference lists and other sources searched or consulted to identify studies. Specify the date when each source was last searched or consulted. Search strategy
Search strategy 7 Present the full search strategies for all databases, registers and websites, including any filters and limits used. Search strategy
Selection process 8 Specify the methods used to decide whether a study met the inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process. Inclusion criteria and trial selection
Data collection process 9 Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of automation tools used in the process. Data extraction and outcome assessment
Data items 10a List and define all outcomes for which data were sought. Specify whether all results that were compatible with each outcome domain in each study were sought (e.g. for all measures, time points, analyses), and if not, the methods used to decide which results to collect. Data extraction and outcome assessment
10b List and define all other variables for which data were sought (e.g. participant and intervention characteristics, funding sources). Describe any assumptions made about any missing or unclear information. Data extraction and outcome assessment
Study risk of bias assessment 11 Specify the methods used to assess risk of bias in the included studies, including details of the tool(s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process. BIAS assessment
Effect measures 12 Specify for each outcome the effect measure(s) (e.g. risk ratio, mean difference) used in the synthesis or presentation of results. Data extraction and outcome assessment
Synthesis methods 13a Describe the processes used to decide which studies were eligible for each synthesis (e.g. tabulating the study intervention characteristics and comparing against the planned groups for each synthesis (item #5)). Statistical analysis
13b Describe any methods required to prepare the data for presentation or synthesis, such as handling of missing summary statistics, or data conversions. Data extraction and outcome assessment
13c Describe any methods used to tabulate or visually display results of individual studies and syntheses. Statistical analysis
13d Describe any methods used to synthesize results and provide a rationale for the choice(s). If meta-analysis was performed, describe the model(s), method(s) to identify the presence and extent of statistical heterogeneity, and software package(s) used. Statistical analysis
13e Describe any methods used to explore possible causes of heterogeneity among study results (e.g. subgroup analysis, meta-regression). Statistical analysis
13f Describe any sensitivity analyses conducted to assess robustness of the synthesized results. Statistical analysis
Reporting bias assessment 14 Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases). BIAS assessment
Certainty assessment 15 Describe any methods used to assess certainty (or confidence) in the body of evidence for an outcome. BIAS assessment
RESULTS  
Study selection 16a Describe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram. Characteristics of the Studies
16b Cite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded. Table D2
Study characteristics 17 Cite each included study and present its characteristics. Characteristics of the Studies; Table D3
Risk of bias in studies 18 Present assessments of risk of bias for each included study. Table D5
Results of individual studies 19 For all outcomes, present, for each study: (a) summary statistics for each group (where appropriate) and (b) an effect estimate and its precision (e.g. confidence/credible interval), ideally using structured tables or plots. Figure 2; Table 2
Results of syntheses 20a For each synthesis, briefly summarise the characteristics and risk of bias among contributing studies. Meta-analysis
20b Present results of all statistical syntheses conducted. If meta-analysis was done, present for each the summary estimate and its precision (e.g. confidence/credible interval) and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect. Meta-analysis
20c Present results of all investigations of possible causes of heterogeneity among study results. Subgroup Analysis; Meta-Regression
20d Present results of all sensitivity analyses conducted to assess the robustness of the synthesized results. Figure D1
Reporting biases 21 Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed. Table 3; Figures D2-D6
Certainty of evidence 22 Present assessments of certainty (or confidence) in the body of evidence for each outcome assessed. BIAS assessment; Table D6
DISCUSSION  
Discussion 23a Provide a general interpretation of the results in the context of other evidence. Discussion
23b Discuss any limitations of the evidence included in the review. Discussion
23c Discuss any limitations of the review processes used. Discussion
23d Discuss implications of the results for practice, policy, and future research. Discussion
OTHER INFORMATION  
Registration and protocol 24a Provide registration information for the review, including register name and registration number, or state that the review was not registered. Materials and methods
24b Indicate where the review protocol can be accessed, or state that a protocol was not prepared. Materials and methods
24c Describe and explain any amendments to information provided at registration or in the protocol. Materials and methods
Support 25 Describe sources of financial or non-financial support for the review, and the role of the funders or sponsors in the review. Meta-analysis
Competing interests 26 Declare any competing interests of review authors. Meta-analysis
Availability of data, code and other materials 27 Report which of the following are publicly available and where they can be found: template data collection forms; data extracted from included studies; data used for all analyses; analytic code; any other materials used in the review. Meta-analysis
Table D1. PRISMA Checklist

 

Excluded Publications with Reasons for Exclusion

Reason for Exclusion Study
No CVP Assessment
  1. Xue Y.M., Zeng L.J., Chen D.W., et al. [Cephalic artery peak velocity variation during passive leg raising can predict fluid responsiveness in mechanically ventilated severe sepsis patients with spontaneous breathing]. Zhonghua Yi Xue Za Zhi. 2018; 98(31): 2476–80. DOI: 10.3760/cma.j.issn.0376-2491.2018.31.005
  2. Yonis H., Bitker L., Aublanc M., et al. Change in cardiac output during Trendelenburg maneuver is a reliable predictor of fluid responsiveness in patients with acute respiratory distress syndrome in the prone position under protective ventilation. Crit Care. 2017; 21(1): 295. DOI: 10.1186/s13054-017-1881-0
  3. Kim N., Shim J.-K., Choi H.G., et al. Comparison of positive end-expiratory pressure-induced increase in central venous pressure and passive leg raising to predict fluid responsiveness in patients with atrial fibrillation. Br J Anaesth. 2016; 116(3): 350–6. DOI: 10.1093/bja/aev359
  4. Desebbe O., Vallier S., Gergelé L., et al. Diagnostic accuracy of the peripheral venous pressure variation induced by an alveolar recruitment maneuver to predict fluid responsiveness during high-risk abdominal surgery. BMC Anesthesiol. 2023; 23(1): 249. DOI: 10.1186/s12871-023-02194-x
  5. Wilkman E., Kuitunen A., Pettilä V., et al. Fluid responsiveness predicted by elevation of PEEP in patients with septic shock. Acta Anaesthesiol Scand. 2014; 58(1): 27–35. DOI: 10.1111/aas.12229
  6. Huang H.-B., Xu B., Liu G.-Y., et al. N-terminal pro-B-type natriuretic peptide for predicting fluid challenge in patients with septic shock. Ann Transl Med. 2019; 7(12): 264. DOI: 10.21037/atm.2019.05.60
  7. Dong Z.-Z., Fang Q., Zheng X., et al. Passive leg raising as an indicator of fluid responsiveness in patients with severe sepsis. World J Emerg Med. 2012; 3(3): 191–6. DOI: 10.5847/wjem.j.issn.1920-8642.2012.03.006
  8. Giraud R., Siegenthaler N., Gayet-Ageron A., et al. ScvO(2) as a marker to define fluid responsiveness. J Trauma. 2011; 70(4): 802–7. DOI: 10.1097/TA.0b013e3181e7d649
  9. Vallier S., Bouchet J.-B., Desebbe O., et al. Slope analysis for the prediction of fluid responsiveness by a stepwise PEEP elevation recruitment maneuver in mechanically ventilated patients. BMC Anesthesiol. 2022; 22(1): 4. DOI: 10.1186/s12871-021-01544-x
  10. Cherpanath T.G. V., Geerts B.F., Maas J.J., et al. Ventilator-induced central venous pressure variation can predict fluid responsiveness in post-operative cardiac surgery patients. Acta Anaesthesiol Scand. 2016; 60(10): 1395–403. DOI: 10.1111/aas.12811
Inappropriate Gold Standard (Not a Fluid Challenge Test)
  1. Pişkin Ö., Öz İ.İ. Accuracy of pleth variability index compared with inferior vena cava diameter to predict fluid responsiveness in mechanically ventilated patients. Medicine (Baltimore). 2017; 96(47): e8889. DOI: 10.1097/MD.0000000000008889
  2. Broch O., Bein B., Gruenewald M., et al. Accuracy of the pleth variability index to predict fluid responsiveness depends on the perfusion index. Acta Anaesthesiol Scand. 2011; 55(6): 686–93. DOI: 10.1111/j.1399-6576.2011.02435.x
  3. Hofer C.K., Senn A., Weibel L., et al. Assessment of stroke volume variation for prediction of fluid responsiveness using the modified FloTrac and PiCCOplus system. Crit Care. 2008; 12(3): R82. DOI: 10.1186/cc6933
  4. Geerts B.F., Aarts L.P.H.J., Groeneveld A.B., et al. Predicting cardiac output responses to passive leg raising by a PEEP-induced increase in central venous pressure, in cardiac surgery patients. Br J Anaesth. 2011; 107(2): 150–6. DOI: 10.1093/bja/aer125
  5. Cannesson M., Slieker J., Desebbe O., et al. Prediction of fluid responsiveness using respiratory variations in left ventricular stroke area by transoesophageal echocardiographic automated border detection in mechanically ventilated patients. Crit Care. 2006; 10(6): R171. DOI: 10.1186/cc5123
  6. Desebbe O., Boucau C., Farhat F., et al. The ability of pleth variability index to predict the hemodynamic effects of positive end-expiratory pressure in mechanically ventilated patients under general anesthesia. Anesth Analg. 2010; 110(3): 792–8. DOI: 10.1213/ANE.0b013e3181cd6d06
  7. Broch O., Renner J., Gruenewald M., et al. Variation of left ventricular outflow tract velocity and global end-diastolic volume index reliably predict fluid responsiveness in cardiac surgery patients. J Crit Care. 2012; 27(3): 325.e7–13. DOI: 10.1016/j.jcrc.2011.07.073
Non-Cardiac Parameters in Fluid Challenge Test
  1. Ismail M.T., El-Iraky A.A., Ibrahim E.E.-D.A., et al. Comparison of inferior vena cava collapsibility and central venous pressure in assessing volume status in shocked patients. African J Emerg Med Rev Africaine La Med d’urgence. 2022; 12(3): 165–71. DOI: 10.1016/j.afjem.2022.04.005
  2. Westphal G.A., Silva E., Gonçalves A.R., et al. Pulse oximetry wave variation as a noninvasive tool to assess volume status in cardiac surgery. Clinics (Sao Paulo). 2009; 64(4): 337–43. DOI: 10.1590/s1807-59322009000400012
Retrospective Study
  1. Keller G., Sinavsky K., Desebbe O., et al. Combination of continuous pulse pressure variation monitoring and cardiac filling pressure to predict fluid responsiveness. J Clin Monit Comput. 2012; 26(6): 401–5. DOI: 10.1007/s10877-012-9365-x
  2. Velissaris D., Pierrakos C., Scolletta S., et al. High mixed venous oxygen saturation levels do not exclude fluid responsiveness in critically ill septic patients. Crit Care. 2011; 15(4): R177. DOI: 10.1186/cc10326
Table D2. Excluded Publications with Reasons for Exclusion

 

Additional Characteristics and Outcomes

№ Study Sample size (N. of obser-s) N. of patients Patient Cohort GS parameter Infusion Solution Test: cut-off Infusion volume Type GS Method of determination GS: cut-off Mean age (years) Male, % BMI, kg/m2 APACHE II, mean. MV, N Type of breathing AUROC (mean±SD) Se Sp
1 Albano BBP (2021) [1] 101 101 C. ICU ∆SV или ∆CI (%) Cryst. 6,0 8 ml/kg FC TPTD 0,15 55,8 74,3 ND ND 101 MV 0,40 ± 0,56 0,83 0,51
2 Angappan S (2015) [2] 45 45 Shock ∆CI (%) Coll. ND 500 FC PPWA 0,15 45,2 80,0 ND ND 45 MV 0,56 ± 0,50 ND ND
3 Baker AK (2013) [3] 25 25 Shock ∆SV (%) Coll. ND 500 FC EchoCG 0,15 60,3 68,0 ND ND 25 MV 0,66 ± 0,55 ND ND
4 Barbier C (2004) [4] 20 20 Shock ∆CI (%) Coll. 12 7 ml/kg FC EchoCG 0,15 63,0 75,0 ND ND 20 MV 0,57 ± 0,57 0,90 0,30
5 Berg JM (2021) [5] 56 56 CS ∆SVI (%) Cryst. ND 5 ml/kg FC TPTD 0,15 ND ND ND ND 56 MV 0,48 ± 0,97 ND ND
6 Berkenstadt H (2001) [6] 140 15 NCS ∆SV (%) Coll. ND 100 mini-FC TPTD 0,05 55,0 40,0 ND ND 140 MV 0,49 ± 0,58 ND ND
7 Biais M (2008) [7] 35 35 NC. ICU ∆CO (%) Coll. 3,0 20 ml * ИМТ FC EchoCG 0,15 51,0 65,7 23,0 ND 35 MV 0,64 ± 0,49 ND ND
8 Botros JM (2023) [8] 48 48 NCS ∆SVI (%) Cryst. ND 6 ml/kg FC EchoCG 0,1 53,1 21,0 26,2 ND 48 MV 0,41 ± 0,59 ND ND
9 Bubenek-Turconi ŞI (2019) [9] 266 40 CS ∆CI (%) Coll. ND 500 FC PPWA 0,11 63,0 77,5 ND ND 266 MV 0,53 ± 0,54 0,55 0,61
10 Cannesson M (2007) [10] 25 25 CS ∆CI (%) Coll. ND 500 FC TPTD 0,15 69,0 72,0 ND ND 25 MV 0,57 ± 0,12 ND ND
11 Cannesson M (2008)-1 [11] 25 25 CS ∆CI (%) Coll. 3,5 500 FC TPTD 0,15 65,0 60,0 ND ND 25 MV 0,75 ± 0,11 0,77 0,63
12 Cannesson M (2008)-2 [12] 25 25 CS ∆CI (%) Coll. 12,5 500 FC TPTD 0,15 65,0 64,0 ND ND 25 MV 0,42 ± 0,57 0,44 0,78
13 Cannesson M (2009) [13] 25 25 CS ∆CI (%) Coll. ND 500 FC TPTD 0,15 67,0 80,0 ND ND 25 MV 0,53 ± 0,12 ND ND
14 Cannesson M (2011) [14] 413 413 NCS ∆CO (%) Coll. ND 500 FC TPTD, PPWA или EchoCG 0,15 65,0 74,3 ND ND 413 MV 0,57 ± 0,26 ND ND
15 Cecconi M (2012) [15] 31 31 NC. ICU ∆SV (%) Coll. ND 250 mini-FC PPWA 0,15 65,0 74,2 ND ND 31 MV 0,62 ± 0,56 ND ND
16 de Oliveira OH (2016) [16] 20 20 NC. ICU ∆VTI (%) Cryst. 10,0 500 FC EchoCG 0,15 50,0 40,0 ND ND 20 MV 0,65 ± 0,13 0,55 0,78
17 de Waal EE (2009) [17] 22 22 CS ∆SVI (%) Coll. ND 10 ml/kg FC TPTD 0,12 66,0 81,8 26,3 ND 22 MV 0,64 ± 0,55 ND ND
18 Dépret F (2018) [18] 28 28 Shock ∆CI (%) Cryst. ND 500 FC TPTD 0,15 66,5 ND ND ND 28 MV 0,51 ± 0,12 ND ND
19 Desgranges FP (2011) [19] 28 28 CS ∆CI (%) Coll. 8,0 500 FC TPTD 0,15 62,0 82,1 ND ND 28 MV 0,58 ± 0,65 0,58 0,33
20 Fu Q (2012) [20] 51 51 NCS ∆SVI (%) Coll. 7,5 8 ml/kg FC PPWA 0,1 48,7 51,0 ND ND 51 MV 0,61 ± 0,59 0,61 0,64
21 Fu Q (2014) [21] 30 30 OV ∆CI (%) Coll. ND 8 ml/kg FC PPWA 0,1 52,4 70,0 ND ND 30 MV 0,56 ± 0,58 0,93 0,44
22 Geerts BF (2011) [22] 24 24 C. ICU ∆CO (%) Coll. 9,0 500 FC TPTD 0,1 64,0 79,12 ND ND 24 MV 0,69 ± 0,48 0,71 0,57
23 Giraud R (2018) [23] 20 20 NC. ICU ∆CO (%) Cryst. ND 500 FC TPTD 0,15 62,2 45,0 ND ND 20 MV 0,54 ± 0,56 ND ND
24 Guarracino F (2014) [24] 50 50 Shock ∆CI (%) Cryst. 8,0 7 ml/kg FC PPWA 0,15 66,3 64,0 ND ND 50 MV 0,68 ± 0,53 0,33 1,00
25 Haas S (2012) [25] 22 18 C. ICU ∆CI (%) Coll. ND 4 ml/kg mini-FC TPTD 0,1 67,5 72,2 ND ND 22 MV 0,81 ± 0,43 ND ND
26 Hahn RG (2016) [26] 80 80 NCS ∆SVI (%) Coll. 6,5 9 ml/kg FC PPWA 0,1 56,0 65,0 ND ND 80 MV 0,74 ± 0,49 0,68 0,75
27 Hikasa Y (2023) [27] 61 24 NCS ∆SVI (%) Coll. 19,0 200 mini-FC PPWA 0,15 66,0 75,0 22,3 ND 61 MV 0,58 ± 0,64 0,25 0,95
28 Hofer CK (2005) [28] 35 35 CS ∆SVI (%) Coll. ND 10 ml/kg FC TPTD 0,25 62,0 ND 27,0 ND 35 MV 0,54 ± 0,57 ND ND
29 Hofer CK (2018) [29] 34 34 C. ICU ∆SV (%) Coll. ND 500 FC TPTD 0,15 65,8 82,4 27,9 ND 34 MV 0,55 ± 0,60 ND ND
30 Høiseth LØ (2011) [30] 34 25 NCS ∆SV (%) Coll. ND 250 mini-FC EchoCG 0,15 61,0 48,0 ND ND 34 MV 0,58 ± 0,50 ND ND
31 Huang CC (2008) [31] 22 22 NC. ICU ∆CI (%) Coll. ND 500 FC PPWA 0,15 54,0 72,7 ND 21,8 22 MV 0,43 ± 0,38 0,55 0,44
32 Ibarra-Estrada MÁ (2015) [32] 59 19 Shock ∆SVI (%) Cryst. ND 7 ml/kg FC EchoCG 0,15 49,2 62,7 ND ND 59 MV 0,52 ± 0,52 ND ND
33 Ikeda K (2016) [33] 75 35 C. ICU ∆CI (%) Mix. ND 574 (SD: 361) non-classified TPTD 0,15 ND ND ND ND 75 MV 0,49 ± 0,67 ND ND
34 Ishihara H (2013) [34] 43 43 NC. ICU ∆CI (%) Coll. 6,5 250 mini-FC TPTD 0,15 65,0 97,7 ND ND 43 MV 0,69 ± 0,54 0,60 0,74
35 Kim SY (2013) [35] 66 66 CS ∆SVI (%) Coll. ND 500 FC PPWA 0,12 ND 74,2 ND ND 66 MV 0,53 ± 0,57 ND ND
36 Kramer A (2004) [36] 21 21 C. ICU ∆CO (%) Аутокровь ND 500 FC TPTD 0,12 64,7 71,4 ND ND 21 MV 0,49 ± 0,69 ND ND
37 Kumar N (2021) [37] 50 50 Shock ∆CI (%) Cryst. 7,5 10 ml/kg FC PPWA 0,1 44,9 64,0 ND ND 50 MV 0,56 ± 0,55 0,63 0,50
38 Kurtz P (2014) [38] 57 10 NC. ICU ∆CI (%) Coll. ND 250 mini-FC PPWA 0,1 52,7 40,0 ND 25,0 57 MV 0,61 ± 0,62 ND ND
39 Lakhal K (2010) [39] 102 102 Shock ∆CO (%) Coll. ND 500 FC TPTD 0,1 59,0 70,6 ND ND 102 MV 0,61 ± 0,53 ND ND
40 Lakhal K (2011) [40] 65 65 Shock ∆CO (%) Coll. ND 500 FC TPTD 0,1 59,0 69,2 ND ND 65 MV 0,63 ± 0,50 ND ND
41 Lanspa MJ (2012) [41] 34 25 Shock ∆CI (%) Cryst. или Coll. 8,0 10 ml/kg FC EchoCG 0,15 62,1 32,4 ND 20,0 19 Mixed 0,73 ± 0,59 ND ND
42 Lee JH (2007) [42] 20 20 NCS ∆SVI (%) Coll. ND 7 ml/kg FC EchoCG 0,1 49,0 40,0 24,0 ND 20 MV 0,54 ± 0,13 ND ND
43 Lee JH (2011) [43] 35 35 CS ∆CI (%) Coll. ND 10 ml/kg FC TPTD 0,15 63,0 57,1 25,0 ND 35 MV 0,70 ± 0,47 ND ND
44 Li J (2012) [44] 157 48 NCS ∆SV (%) Cryst. ND 200 mini-FC PPWA 0,1 44,0 58,3 ND ND 157 MV 0,54 ± 0,06 ND ND
45 Lu N (2017) [45] 49 49 Shock ∆CI (%) Cryst. 6,5 200 mini-FC TPTD 0,1 55,4 67,3 24,9 26,8 49 MV 0,68 ± 0,59 0,65 0,70
46 Ma GG (2018) [46] 70 70 C. ICU ∆SV (%) Coll. 11 500 FC PPWA 0,15 61,0 62,9 22,0 9,0 70 MV 0,70 ± 0,48 0,60 0,77
47 Ma Q (2022) [47] 56 56 NC. ICU ∆CO (%) Cryst. 4 5 ml/kg FC EchoCG 0,15 57,4 55,4 ND ND 0 Spontaneous Breathing 0,64 ± 0,49 0,82 0,47
48 Mallat J (2022) [48] 270 270 Shock ∆CI (%) Cryst. ND 500 FC EchoCG или TPTD 0,15 67,0 63,0 27,5 ND 270 MV 0,59 ± 0,63 ND ND
49 Mohamed ZU (2017) [49] 30 30 Shock ∆CI (%) Coll. ND 500 FC TPTD 0,15 57,5 40,0 ND ND 30 MV 0,75 ± 0,46 ND ND
50 Monge García MI (2008) [50] 30 30 NC. ICU ∆SVI (%) Coll. ND 500 FC PPWA 0,15 60,0 63,0 ND 11,0 0 Spontaneous Breathing 0,51 ± 0,11 ND ND
51 Monge García MI (2009) [51] 38 38 Shock ∆SVI (%) Coll. ND 500 FC PPWA 0,15 56,2 50,0 ND ND 38 MV 0,64 ± 0,09 ND ND
52 Moretti R (2010) [52] 29 29 NC. ICU ∆CI (%) Coll. ND 7 ml/kg FC TPTD 0,15 51,8 55,2 ND ND 29 MV 0,67 ± 0,47 ND ND
53 Muller L (2008) [53] 35 35 NC. ICU ∆SVI (%) Coll. 9,0 250 or 500 non-classified TPTD 0,15 64,6 60,0 ND 20,0 35 MV 0,68 ± 0,48 0,61 0,82
54 Muller L (2009) [54] 33 33 Shock ∆SVI (%) Mix. 7,0 250 or 500 non-classified TPTD 0,15 71,4 63,6 ND 23,3 33 MV 0,77 ± 0,10 0,54 1,00
55 Muller L (2010) [55] 57 57 Shock ∆SVI (%) Mix. 9,0 250 or 500 non-classified TPTD 0,15 70,3 68,4 ND 22,9 57 MV 0,76 ± 0,47 0,68 0,81
56 Muller L (2011) [56] 39 39 Shock ∆VTI (%) Coll. ND 500 FC EchoCG 0,15 66,0 76,9 ND 19,0 39 MV 0,61 ± 0,59 ND ND
57 Myatra SN (2017) [57] 30 20 Shock ∆CI (%) Cryst. ND 7 ml/kg FC TPTD 0,15 53,0 50,0 ND 24,0 30 MV 0,48 ± 0,56 ND ND
58 Oliveira-Costa CD (2012) [58] 37 37 Shock ∆CI (%) Cryst. ND 1000 FC TPTD 0,15 54,0 54,0 ND 28,0 37 MV 0,57 ± 0,57 ND ND
59 Pei S (2014) [59] 32 32 NCS ∆SVI (%) Coll. 4,5 250 mini-FC PPWA 0,1 54,8 56,25 23,0 ND 32 MV 0,73 ± 0,45 0,46 1,00
60 Peng S (2013) [60] 54 32 Shock ∆SV (%) Cryst. ND 250 mini-FC PPWA 0,1 67,0 62,5 ND 21,0 54 MV 0,59 ± 0,57 0,60 0,63
61 Preisman S (2005) [61] 70 18 C. ICU ∆SVI (%) Coll. ND 250 mini-FC PPWA 0,15 66,2 88,9 ND ND 70 MV 0,61 ± 0,59 ND ND
62 Reuter DA (2002) [62] 20 20 C. ICU ∆SVI (%) Coll. ND 20 ml * ИМТ FC TPTD 0,15 ND ND ND ND 20 MV 0,42 ± 0,61 ND ND
63 Reuter DA (2003) [63] 12 12 C. ICU ∆SVI (%) Coll. 6,0 10 ml * ИМТ non-classified TPTD 0,05 61,0 ND ND ND 12 MV 0,71 ± 0,33 0,50 0,90
64 Roy S (2013) [64] 37 37 CS ∆SVI (%) Coll. ND 500 FC TPTD 0,15 60,2 81,0 ND ND 37 MV 0,61 ± 0,52 ND ND
65 Saugel B (2013) [65] 24 24 NC. ICU ∆CI (%) Cryst. 12,0 7 ml/kg FC TPTD 0,15 58,8 71,0 ND ND 5 Mixed 0,63 ± 0,45 0,86 0,41
66 Shim JK (2014) [66] 34 34 CS ∆SVI (%) Coll. ND 6 ml/kg FC TPTD 0,12 59,0 94,1 24,3 ND 34 MV 0,58 ± 0,50 ND ND
67 Shin YH (2011) [67] 33 33 NCS ∆CI (%) Coll. ND 10 ml/kg FC TPTD 0,15 53,5 87,9 24,4 ND 33 MV 0,58 ± 0,50 ND ND
68 Soliman RA (2015) [68] 25 25 Shock ∆CI (%) Coll. ND 500 FC EchoCG 0,15 52,8 60,0 ND ND 25 MV 0,59 ± 0,26 ND ND
69 Song Y (2014) [69] 40 40 CS ∆SVI (%) Coll. ND 6 ml/kg FC PPWA 0,15 64,6 67,5 25,2 ND 40 MV 0,68 ± 0,53 ND ND
70 Suehiro K (2012) [70] 80 80 NC. ICU ∆CI (%) Cryst. ND 500 FC PPWA 0,15 58,6 65,0 ND ND 80 MV 0,56 ± 0,48 ND ND
71 Taccheri T (2021) [71] 30 30 NC. ICU ∆CI (%) Cryst. ND 500 FC TPTD 0,1 66,5 76,7 ND ND 30 MV 0,56 ± 0,11 ND ND
72 Thiel SW (2009) [72] 102 89 NC. ICU ∆SV (%) Cryst. ND 500 FC EchoCG 0,15 59,4 56,9 31,0 18,5 67 Mixed 0,52 ± 0,08 ND ND
73 Trof RJ (2011) [73] 12 12 C. ICU ∆SVI (%) Coll. ND 200-1800 non-classified TPTD 0,1 66,0 75,0 ND 9,0 ND ND 0,89 ± 0,30 ND ND
74 Vistisen ST (2009) [74] 23 30 C. ICU ∆VI (%) Coll. 8,0 500 FC TPTD 0,15 71,0 ND ND ND 23,00 MV 0,68 ± 0,56 0,35 1,00
75 Vistisen ST (2016) [75] 30 23 C. ICU ∆SV (%) Mix. ND 500 FC PPWA 0,15 66,5 76,7 ND ND 27 Mixed 0,54 ± 0,58 1,00 0,26
76 Wang Y (2018) [76] 18 18 OV ∆CI (%) Coll. 11,0 7 ml/kg FC TPTD 0,15 54,1 55,56 ND ND 18 MV 0,60 ± 0,57 0,33 1,00
77 Wyffels PA (2007) [77] 32 32 C. ICU ∆CI (%) Coll. ND 500 FC TPTD 0,15 66,0 68,75 ND ND 32 MV 0,60 ± 0,87 ND ND
78 Xu B (2016) [78] 40 40 Shock ∆CI (%) Mix. 11,0 500 FC TPTD 0,1 60,0 75,0 ND 27,0 37 Mixed 0,56 ± 0,56 0,56 0,55
79 Xu LY (2019) [79] 75 75 CS ∆VTI (%) Cryst. 7,0 6 ml/kg FC EchoCG 0,15 63,5 82,67 24,5 ND 75 MV 0,49 ± 0,57 0,57 0,45
80 Xu Y (2022) [80] 76 76 NC. ICU ∆CI (%) Cryst. 9,0 250 mini-FC TPTD 0,15 55,0 50,0 25,0 26,0 76 MV 0,68 ± 0,48 0,48 0,80
81 Yazigi A (2012) [81] 60 60 C. ICU ∆SVI (%) Coll. ND 7 ml/kg FC TPTD 0,15 75,3 63,3 ND ND 60 MV 0,43 ± 0,58 ND ND
82 Zhang X (2016) [82] 40 40 NCS ∆SVI (%) Coll. 6,5 7 ml/kg FC PPWA 0,15 46,0 60,0 ND ND 40 MV 0,63 ± 0,59 0,89 0,36
83 Zhao F (2015) [83] 25 25 NCS ∆SVI (%) Coll. 7,0 250 mini-FC PPWA 0,1 54,8 60,0 23,0 ND 25 MV 0,52 ± 0,50 0,50 0,62
84 Zimmermann M (2010) [84] 20 20 NCS ∆SVI (%) Coll. 10,5 7 ml/kg FC PPWA 0,15 53,0 65,0 26,1 ND 20 MV 0,55 ± 0,60 0,66 0,40
Table D3. Additional Characteristics and Outcomes Note: APACHE II — Acute Physiology and Chronic Health Evaluation II; AUROC — area under ROC curve; BMI — body mass index; C. ICU — cardiac intensive care unit; CS — cardiac surgery; FC — fluid challenge; mini-FC — mini-fluid challenge; EchoCG — echocardiography; GS — gold standard; MV — Mechanical Ventilation; NCS — non-cardiac surgery; ND — no data; NC. ICU — non-cardiac ICU; ОV — one-lung ventilation; PPWA — pulse pressure waveform analysis.Se — sensitivity; Sp — specificity; SD — standard deviation; Shock — patients with shock; TPTD — transpulmonary thermodilution; ∆CI — change in cardiac index; ∆СO — change in cardiac output; ∆SV — change in stroke volume; ∆SVI — change in stroke volume index; ∆VTI — change in velocity-time integral; cryst. — crystalloids; coll. — colloids; mix. — mixed.

Leave-One-Out Method: Mean AUROC Values with 95% Confidence Intervals. The trend line is at 0.596

Fig. D1. Leave-One-Out Method: Mean AUROC Values with 95% Confidence Intervals. The trend line is at 0.596

 

Univariate Meta-Regression

Parameter Studies, N Univariate Analysis
Coefficient SE p-value
Mean age, years 80 0,002 0,002 0,129
Male, % 77 -0,00026 0,001 0,757
BMI, kg/m2 20 -0,013 0,006 0,053
Table D4. Univariate Meta-Regression SE — standard error; BMI — body mass index.

Meta-regression helps identify study characteristics that may influence the results of a meta-analysis. In the context of our study, meta-regression was used to determine which study parameters might affect the AUROC value for CVP in comparison to the fluid challenge test.

Assessment of Publication Bias and Small-Study Effects: Funnel Plots

Fig. D2. Funnel plot for all 84 studies included in the meta-analysis

Fig. D3. Funnel Plots by Patient Cohorts

Fig. D4. Funnel Plots by Type of Gold Standard

Fig. D5. Funnel Plots by Type of Breathing

Fig. D6. Funnel Plots by Year of Publication

Risk of bias assessment

Study Test parameter Test cutoff Risk of bias (QUADAS-2) OVERALL bias Applicability concerns (QUADAS-2)
P I R FT P I R
1 Albano BBP (2021) CVP (mmHg) 6 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
2 Angappan S (2015) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
3 Baker AK (2013) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
4 Barbier C (2004) CVP (mmHg) 12 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
5 Berg JM (2021) CVP ND ✗ (not detailed description of the study population) ✗ (ND for cut-off) ✓ ✓ High ✓ ✓ ✓
6 Berkenstadt H (2001) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✗ (low value of gold standard cut-off) ✓ High ✓ ✓ ✓
7 Biais M (2008) CVP (mmHg) 3 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
8 Botros JM (2023) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
9 Bubenek-Turconi ŞI (2019) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
10 Cannesson M (2007) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
11 Cannesson M (2008)-1 CVP (mmHg) 3,5 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
12 Cannesson M (2008)-2 CVP (mmHg) 12,5 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
13 Cannesson M (2009) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
14 Cannesson M (2011) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
15 Cecconi M (2012) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
16 de Oliveira OH (2016) CVP (mmHg) 10 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
17 de Waal EE (2009) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
18 Dépret F (2018) CVP (mmHg) ND ✗ (not detailed description of the study population) ✗ (ND for cut-off) ✓ ✓ High ✓ ✓ ✓
19 Desgranges FP (2011) CVP (mmHg) 8 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
20 Fu Q (2012) CVP (mmHg) 7,5 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
21 Fu Q (2014) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
22 Geerts BF (2011) CVP (mmHg) 9 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
23 Giraud R (2018) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
24 Guarracino F (2014) CVP (mmHg) 8 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
25 Haas S (2012) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
26 Hahn RG (2016) CVP (mmHg) 6,5 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
27 Hikasa Y (2023) CVP (cmH2O) 19 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
28 Hofer CK (2005) CVP ND ✗ (not detailed description of the study population) ✗ (ND for cut-off) ✓ ✓ High ✓ ✓ ✓
29 Hofer CK (2018) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
30 Høiseth LØ (2011) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
31 Huang CC (2008) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✗ (not all patients including in analysis) High ✓ ✓ ✓
32 Ibarra-Estrada MÁ (2015) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
33 Ikeda K (2016) CVP ND ✗ (not detailed description of the study population and study enroument depence on clinical decision) ✗ (ND for cut-off) ✗ (inaccurate values volume of FC) ✓ Very High ✓ ✓ ✓
34 Ishihara H (2013) CVP (mmHg) 6,5 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
35 Kim SY (2013) CVP (mmHg) ND ✗ (not detailed description of the study population) ✗ (ND for cut-off) ✓ ✓ High ✓ ✓ ✓
36 Kramer A (2004) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
37 Kumar N (2021) CVP (mmHg) 7,5 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
38 Kurtz P (2014) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
39 Lakhal K (2010) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
40 Lakhal K (2011) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
41 Lanspa MJ (2012) CVP (mmHg) 8 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
42 Lee JH (2007) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
43 Lee JH (2011) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
44 Li J (2012) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
45 Lu N (2017) CVP (mmHg) 6,5 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
46 Ma GG (2018) CVP (mmHg) 11 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
47 Ma Q (2022) CVP (mmHg) 4 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
48 Mallat J (2022) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
49 Mohamed ZU (2017) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
50 Monge García MI (2008) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
51 Monge García MI (2009) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
52 Moretti R (2010) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
53 Muller L (2008) CVP (mmHg) 9 ✓ ✓ ✗ (inaccurate values volume of FC) ✓ Moderate ✓ ✓ ✓
54 Muller L (2009) CVP (mmHg) 7 ✓ ✓ ✗ (inaccurate values volume of FC) ✓ Moderate ✓ ✓ ✓
55 Muller L (2010) CVP (mmHg) 9 ✓ ✓ ✗ (inaccurate values volume of FC) ✓ Moderate ✓ ✓ ✓
56 Muller L (2011) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
57 Myatra SN (2017) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
58 Oliveira-Costa CD (2012) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
59 Pei S (2014) CVP (mmHg) 4,5 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
60 Peng S (2013) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
61 Preisman S (2005) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
62 Reuter DA (2002) CVP (mmHg) ND ✗ (not detailed description of the study population) ✗ (ND for cut-off) ✓ ✓ High ✓ ✓ ✓
63 Reuter DA (2003) CVP (mmHg) 6 ✗ (not detailed description of the study population) ✓ ✗ (inaccurate values volume of FC and low value of gold standard cut-off) ✓ High ✓ ✓ ✓
64 Roy S (2013) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
65 Saugel B (2013) CVP (mmHg) 12 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
66 Shim JK (2014) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
67 Shin YH (2011) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
68 Soliman RA (2015) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
69 Song Y (2014) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
70 Suehiro K (2012) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
71 Taccheri T (2021) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
72 Thiel SW (2009) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
73 Trof RJ (2011) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✗ (inaccurate values volume of FC) ✓ High ✓ ✓ ✓
74 Vistisen ST (2009) CVP (mmHg) 8 ✗ (not detailed description of the study population) ✓ ✓ ✓ Moderate ✓ ✓ ✓
75 Vistisen ST (2016) CVP ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
76 Wang Y (2018) CVP (mmHg) 11 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
77 Wyffels PA (2007) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
78 Xu B (2016) CVP (mmHg) 11 ✓ ✓ ✗(saline or gel) ✓ Moderate ✗(CVP) was no less than 8 mmHg ✓ ✓
79 Xu LY (2019) CVP (mmHg) 7 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
80 Xu Y (2022) CVP (mmHg) 9 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
81 Yazigi A (2012) CVP (mmHg) ND ✓ ✗ (ND for cut-off) ✓ ✓ Moderate ✓ ✓ ✓
82 Zhang X (2016) CVP (mmHg) 6,5 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
83 Zhao F (2015) CVP (mmHg) 7 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
84 Zimmermann M (2010) CVP (mmHg) 10,5 ✓ ✓ ✓ ✓ Low ✓ ✓ ✓
Table D5. Risk of bias assessment Р — patient selection; I —index test; R —reference standard; FT —flow and timing; CVP — central venous pressure; ND — no data; ✓ — indicates low risk; ✗ — indicates High risk.

 

Certainty of evidence based on the GRADE methodology

Statement Number of Patients and Studies bias Inconsistency Indirectness Imprecision Publication bias Upgrade Factors Certainty of Evidence
Diagnostic Accuracy
Statement: Central venous pressure demonstrates low diagnostic accuracy for assessing fluid responsiveness.
AUROC 0.596 (0.575; 0.618); I2 = 81.09% 		3729,
84 studies 		No concerns (0) Statistical 
heterogeneity 
(-1)		 No concerns (0) No concerns (0) No concerns (0) No (0) ⚫ ⚫ ⚫ ⚪
Moderate
Table D6. Certainty of evidence based on the GRADE methodology Abbreviations: AUROC, area under ROC-curve.
Key Notations: 0, no downgrading of evidence; -1, serious limitation; -2, very serious limitation; +1, upgrade of evidence.
The baseline level of evidence was high.

References

[1] Albano B., Habana L.M. Prediction of fluid responsiveness in the immediate post-operative period of cardiac surgery. J Anesth Crit Care Open Access. 2021; 13: 35–45. DOI: 10.15406/jaccoa.2021.13.00467

[2] Angappan S., Parida S., Vasudevan A., et al. The comparison of stroke volume variation with central venous pressure in predicting fluid responsiveness in septic patients with acute circulatory failure. Indian J Crit care Med peer-reviewed, Off Publ Indian Soc Crit Care Med. 2015 Jul; 19(7): 394–400. DOI: 10.4103/0972-5229.160278

[3] Baker A.K., Partridge R.J.O., Litton E., et al. Assessment of the plethysmographic variability index as a predictor of fluid responsiveness in critically ill patients: a pilot study. Anaesth Intensive Care. 2013 Nov; 41(6): 736–41. DOI: 10.1177/0310057X1304100608

[4] Barbier C., Loubières Y., Schmit C., et al. Respiratory changes in inferior vena cava diameter are helpful in predicting fluid responsiveness in ventilated septic patients. Intensive Care Med. 2004 Sep; 30(9): 1740–6. DOI: 10.1007/s00134-004-2259-8

[5] Berg J.M., Nielsen D.V., Abromaitiene V., et al. Changes in arterial blood pressure characteristics following an extrasystolic beat or a fast 50 ml fluid challenge do not predict fluid responsiveness during cardiac surgery. J Clin Monit Comput. 2022 Jun; 36(3): 889–900. DOI: 10.1007/s10877-021-00722-z

[6] Berkenstadt H., Margalit N., Hadani M., et al. Stroke volume variation as a predictor of fluid responsiveness in patients undergoing brain surgery. Anesth Analg. 2001 Apr; 92(4): 984–9. DOI: 10.1097/00000539-200104000-00034

[7] Biais M., Nouette-Gaulain K., Cottenceau V., et al. Uncalibrated pulse contour-derived stroke volume variation predicts fluid responsiveness in mechanically ventilated patients undergoing liver transplantation. Br J Anaesth. 2008 Dec; 101(6): 761–8. DOI: 10.1093/bja/aen277

[8] Botros J.M., Salem Y.S.M., Khalil M., et al. Effects of tidal volume challenge on the reliability of plethysmography variability index in hepatobiliary and pancreatic surgeries: a prospective interventional study. J Clin Monit Comput. 2023 Oct; 37(5): 1275–85. DOI: 10.1007/s10877-023-00977-8

[9] Bubenek-Turconi S.I., Hendy A., Baila S., et al. The value of a superior vena cava collapsibility index measured with a miniaturized transoesophageal monoplane continuous echocardiography probe to predict fluid responsiveness compared to stroke volume variations in open major vascular surgery: a prospe. J Clin Monit Comput. 2020 Jun; 34(3): 491–9. DOI: 10.1007/s10877-019-00346-4

[10] Cannesson M., Attof Y., Rosamel P., et al. Respiratory variations in pulse oximetry plethysmographic waveform amplitude to predict fluid responsiveness in the operating room. Anesthesiology. 2007 Jun; 106(6): 1105–11. DOI: 10.1097/01.anes.0000267593.72744.20

[11] Cannesson M., Slieker J., Desebbe O., et al. The ability of a novel algorithm for automatic estimation of the respiratory variations in arterial pulse pressure to monitor fluid responsiveness in the operating room. Anesth Analg. 2008 Apr; 106(4): 1195–200, table of contents. DOI: 10.1213/01.ane.0000297291.01615.5c

[12] Cannesson M., Desebbe O., Rosamel P., et al. Pleth variability index to monitor the respiratory variations in the pulse oximeter plethysmographic waveform amplitude and predict fluid responsiveness in the operating theatre. Br J Anaesth. 2008 Aug; 101(2): 200–6. DOI: 10.1093/bja/aen133

[13] Cannesson M., Musard H., Desebbe O., et al. The ability of stroke volume variations obtained with Vigileo/FloTrac system to monitor fluid responsiveness in mechanically ventilated patients. Anesth Analg. 2009 Feb; 108(2): 513–7. DOI: 10.1213/ane.0b013e318192a36b

[14] Cannesson M., Le Manach Y., Hofer C.K., et al. Assessing the diagnostic accuracy of pulse pressure variations for the prediction of fluid responsiveness: a “gray zone” approach. Anesthesiology. 2011 Aug; 115(2): 231–41. DOI: 10.1097/ALN.0b013e318225b80a

[15] Cecconi M., Monti G., Hamilton M.A., et al. Efficacy of functional hemodynamic parameters in predicting fluid responsiveness with pulse power analysis in surgical patients. Minerva Anestesiol. 2012 May; 78(5): 527–33.

[16] de Oliveira O.H., Freitas F.G.R de., Ladeira RT., et al. Comparison between respiratory changes in the inferior vena cava diameter and pulse pressure variation to predict fluid responsiveness in postoperative patients. J Crit Care. 2016 Aug; 34: 46–9. DOI: 10.1016/j.jcrc.2016.03.017

[17] de Waal E.E.C., Rex S., Kruitwagen C.L.J.J., et al. Dynamic preload indicators fail to predict fluid responsiveness in open-chest conditions. Crit Care Med. 2009 Feb; 37(2): 510–5. DOI: 10.1097/CCM.0b013e3181958bf7

[18] Depret F., Jozwiak M., Teboul J.L., et al. Esophageal Doppler Can Predict Fluid Responsiveness Through End-Expiratory and End-Inspiratory Occlusion Tests. Crit Care Med. 2019 Feb; 47(2): e96–102. DOI: 10.1097/CCM.0000000000003522

[19] Desgranges F.P., Desebbe O., Ghazouani A., et al. Influence of the site of measurement on the ability of plethysmographic variability index to predict fluid responsiveness. Br J Anaesth. 2011 Sep; 107(3): 329–35. DOI: 10.1093/bja/aer165

[20] Fu Q., Mi W.D., Zhang H. Stroke volume variation and pleth variability index to predict fluid responsiveness during resection of primary retroperitoneal tumors in Hans Chinese. Biosci Trends. 2012 Feb; 6(1): 38–43. DOI: 10.5582/bst.2012.v6.1.38

[21] Fu Q., Zhao F., Mi W., et al. Stroke volume variation fail to predict fluid responsiveness in patients undergoing pulmonary lobectomy with one-lung ventilation using thoracotomy. Biosci Trends. 2014 Feb; 8(1): 59–63. DOI: 10.5582/bst.8.59

[22] Geerts B.F., Maas J., de Wilde R.B.P., et al. Arm occlusion pressure is a useful predictor of an increase in cardiac output after fluid loading following cardiac surgery. Eur J Anaesthesiol. 2011 Nov; 28(11): 802–6. DOI: 10.1097/EJA.0b013e32834a67d2

[23] Giraud R., Abraham P.S., Brindel P., et al. Respiratory changes in subclavian vein diameters predicts fluid responsiveness in intensive care patients: a pilot study. J Clin Monit Comput. 2018 Dec; 32(6): 1049–55. DOI: 10.1007/s10877-018-0103-x

[24] Guarracino F., Ferro B., Forfori F., et al. Jugular vein distensibility predicts fluid responsiveness in septic patients. Crit Care. 2014 Dec; 18(6): 647. DOI: 10.1186/s13054-014-0647-1

[25] Haas S., Trepte C., Hinteregger M., et al. Prediction of volume responsiveness using pleth variability index in patients undergoing cardiac surgery after cardiopulmonary bypass. J Anesth. 2012 Oct; 26(5): 696–701. DOI: 10.1007/s00540-012-1410-x

[26] Hahn R.G., He R., Li Y. Central venous pressure as an adjunct to flow-guided volume optimisation after induction of general anaesthesia. Anaesthesiol Intensive Ther. 2016; 48(2): 110–5. DOI: 10.5603/AIT.a2015.0066

[27] Hikasa Y., Suzuki S., Tanabe S., et al. Stroke volume variation and dynamic arterial elastance predict fluid responsiveness even in thoracoscopic esophagectomy: a prospective observational study. J Anesth. 2023 Dec; 37(6): 930–7. DOI: 10.1007/s00540-023-03256-7

[28] Hofer C.K., Muller S.M., Furrer L., et al. Stroke volume and pulse pressure variation for prediction of fluid responsiveness in patients undergoing off-pump coronary artery bypass grafting. Chest. 2005 Aug; 128(2): 848–54. DOI: 10.1378/chest.128.2.848

[29] Hofer C.K., Geisen M., Hartnack S., et al. Reliability of Passive Leg Raising, Stroke Volume Variation and Pulse Pressure Variation to Predict Fluid Responsiveness During Weaning From Mechanical Ventilation After Cardiac Surgery: A Prospective, Observational Study. Turkish J Anaesthesiol Reanim. 2018 Apr; 46(2): 108–15. DOI: 10.5152/TJAR.2018.29577

[30] Hoiseth L.O., Hoff I.E., Skare O., et al. Photoplethysmographic and pulse pressure variations during abdominal surgery. Acta Anaesthesiol Scand. 2011 Nov; 55(10): 1221–30. DOI: 10.1111/j.1399-6576.2011.02527.x

[31] Huang C.C., Fu J.Y., Hu H.C., et al. Prediction of fluid responsiveness in acute respiratory distress syndrome patients ventilated with low tidal volume and high positive end-expiratory pressure. Crit Care Med. 2008 Oct; 36(10): 2810–6. DOI: 10.1097/CCM.0b013e318186b74e

[32] Ibarra-Estrada M.A., Lopez-Pulgarin J.A., Mijangos-Mendez J.C., et al. Respiratory variation in carotid peak systolic velocity predicts volume responsiveness in mechanically ventilated patients with septic shock: a prospective cohort study. Crit Ultrasound J. 2015 Dec; 7(1): 29. DOI: 10.1186/s13089-015-0029-1

[33] Ikeda K., Smith G., Renehan J., et al. Multiparameter Predictor of Fluid Responsiveness in Cardiac Surgical Patients Receiving Tidal Volumes Less Than 10 mL/kg. Semin Cardiothorac Vasc Anesth. 2016 Sep; 20(3): 188–96. DOI: 10.1177/1089253216654765

[34] Ishihara H., Hashiba E., Okawa H., et al. Neither dynamic, static, nor volumetric variables can accurately predict fluid responsiveness early after abdominothoracic esophagectomy. Perioper Med (London, England). 2013 Feb; 2(1): 3. DOI: 10.1186/2047-0525-2-3

[35] Kim S.Y., Song Y., Shim J.K., et al. Effect of pulse pressure on the predictability of stroke volume variation for fluid responsiveness in patients with coronary disease. J Crit Care. 2013 Jun; 28(3): 318.e1–7. DOI: 10.1016/j.jcrc.2012.09.011

[36] Kramer A., Zygun D., Hawes H., et al. Pulse pressure variation predicts fluid responsiveness following coronary artery bypass surgery. Chest. 2004 Nov; 126(5): 1563–8. DOI: 10.1378/chest.126.5.1563

[37] Kumar N., Malviya D., Nath S.S., et al. Comparison of the Efficacy of Different Arterial Waveform-derived Variables (Pulse Pressure Variation, Stroke Volume Variation, Systolic Pressure Variation) for Fluid Responsiveness in Hemodynamically Unstable Mechanically Ventilated Critically Ill Patie. Indian J Crit care Med peer-reviewed, Off Publ Indian Soc Crit Care Med. 2021 Jan; 25(1): 48–53. DOI: 10.5005/jp-journals-10071-23440

[38] Kurtz P., Helbok R., Ko S.B., et al. Fluid responsiveness and brain tissue oxygen augmentation after subarachnoid hemorrhage. Neurocrit Care. 2014 Apr; 20(2): 247–54. DOI: 10.1007/s12028-013-9910-6

[39] Lakhal K., Ehrmann S., Runge I., et al. Central venous pressure measurements improve the accuracy of leg raising-induced change in pulse pressure to predict fluid responsiveness. Intensive Care Med. 2010 Jun; 36(6): 940–8. DOI: 10.1007/s00134-010-1755-2

[40] Lakhal K., Ehrmann S., Benzekri-Lefevre D., et al. Respiratory pulse pressure variation fails to predict fluid responsiveness in acute respiratory distress syndrome. Crit Care. 2011; 15(2): R85. DOI: 10.1186/cc10083

[41] Lanspa M.J., Brown S.M., Hirshberg E.L., et al. Central venous pressure and shock index predict lack of hemodynamic response to volume expansion in septic shock: a prospective, observational study. J Crit Care. 2012 Dec; 27(6): 609–15. DOI: 10.1016/j.jcrc.2012.07.021

[42] Lee J.H., Kim J.T., Yoon S.Z., et al. Evaluation of corrected flow time in oesophageal Doppler as a predictor of fluid responsiveness. Br J Anaesth. 2007 Sep; 99(3): 343–8. DOI: 10.1093/bja/aem179

[43] Lee J.H., Jeon Y., Bahk J.H., et al. Pulse-pressure variation predicts fluid responsiveness during heart displacement for off-pump coronary artery bypass surgery. J Cardiothorac Vasc Anesth. 2011 Dec; 25(6): 1056–62. DOI: 10.1053/j.jvca.2011.07.013

[44] Li J., Ji F.H., Yang J.P. Evaluation of stroke volume variation obtained by the FloTrac/Vigileo system to guide preoperative fluid therapy in patients undergoing brain surgery. J Int Med Res. 2012; 40(3): 1175–81. DOI: 10.1177/147323001204000338

[45] Lu N., Xi X., Jiang L., et al. Exploring the best predictors of fluid responsiveness in patients with septic shock. Am J Emerg Med. 2017 Sep; 35(9): 1258–61. DOI: 10.1016/j.ajem.2017.03.052

[46] Ma G.G., Hao G.W., Yang X.M., et al. Internal jugular vein variability predicts fluid responsiveness in cardiac surgical patients with mechanical ventilation. Ann Intensive Care. 2018 Jan; 8(1): 6. DOI: 10.1186/s13613-017-0347-5

[47] Ma Q., Shi X., Ji J., et al. The diagnostic accuracy of inferior vena cava respiratory variation in predicting volume responsiveness in patients under different breathing status following abdominal surgery. BMC Anesthesiol. 2022 Mar; 22(1): 63. DOI: 10.1186/s12871-022-01598-5

[48] Mallat J., Fischer M.O., Granier M., et al. Passive leg raising-induced changes in pulse pressure variation to assess fluid responsiveness in mechanically ventilated patients: a multicentre prospective observational study. Br J Anaesth. 2022 Sep; 129(3): 308–16. DOI: 10.1016/j.bja.2022.04.031

[49] Mohamed Z.U. Dynamic Parameters do not Predict Fluid Responsiveness in Ventilated Patients with Severe Sepsis or Septic Shock. In 2017.

[50] Monge Garcia M.I., Gil Cano A., Diaz Monrove J.C. Arterial pressure changes during the Valsalva maneuver to predict fluid responsiveness in spontaneously breathing patients. Intensive Care Med. 2009 Jan; 35(1): 77–84. DOI: 10.1007/s00134-008-1295-1

[51] Monge Garcia M.I., Gil Cano A., Diaz Monrove J.C. Brachial artery peak velocity variation to predict fluid responsiveness in mechanically ventilated patients. Crit Care. 2009; 13(5): R142. DOI: 10.1186/cc8027

[52] Moretti R., Pizzi B. Inferior vena cava distensibility as a predictor of fluid responsiveness in patients with subarachnoid hemorrhage. Neurocrit Care. 2010 Aug; 13(1): 3–9. DOI: 10.1007/s12028-010-9356-z

[53] Muller L., Louart G., Bengler C., 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 Aug; 107(2): 607–13. DOI: 10.1213/ane.0b013e31817e6618

[54] Muller L., Louart G., Teboul J.L., et al. Could B-type Natriuretic Peptide (BNP) plasma concentration be useful to predict fluid responsiveness [corrected] in critically ill patients with acute circulatory failure? Ann Fr Anesth Reanim. 2009 Jun; 28(6): 531–6. DOI: 10.1016/j.annfar.2009.04.003

[55] Muller L., Louart G., Bousquet P.J., et al. The influence of the airway driving pressure on pulsed pressure variation as a predictor of fluid responsiveness. Intensive Care Med. 2010 Mar; 36(3): 496–503. DOI: 10.1007/s00134-009-1686-y

[56] Muller L., Toumi M., Bousquet P.J., et al. An increase in aortic blood flow after an infusion of 100 ml colloid over 1 minute can predict fluid responsiveness: the mini-fluid challenge study. Anesthesiology. 2011 Sep; 115(3): 541–7. DOI: 10.1097/ALN.0b013e318229a500

[57] Myatra S.N., Prabu N.R., Divatia J.V., et al. The Changes in Pulse Pressure Variation or Stroke Volume Variation After a “Tidal Volume Challenge” Reliably Predict Fluid Responsiveness During Low Tidal Volume Ventilation. Crit Care Med. 2017 Mar; 45(3): 415–21. DOI: 10.1097/CCM.0000000000002183

[58] Oliveira-Costa CDA de., Friedman G., Vieira SRR., et al. Pulse pressure variation and prediction of fluid responsiveness in patients ventilated with low tidal volumes. Clinics (Sao Paulo). 2012 Jul; 67(7): 773–8. DOI: 10.6061/clinics/2012(07)12

[59] Pei S., Yuan W., Mai H., et al. Efficacy of dynamic indices in predicting fluid responsiveness in patients with obstructive jaundice. Physiol Meas. 2014 Mar; 35(3): 369–82. DOI: 10.1088/0967-3334/35/3/369

[60] Peng S., Zhang L., Zhong M.M., et al. The value of stroke volume variation in prediction of responsiveness to fluid resuscitation in patients with septic shock. Chinese J Emerg Med. 2013; 22: 1260–4. DOI: 10.3760/cma.j.issn.1671-0282.2013.11.013

[61] Preisman S., Kogan S., Berkenstadt H., et al. Predicting fluid responsiveness in patients undergoing cardiac surgery: functional haemodynamic parameters including the Respiratory Systolic Variation Test and static preload indicators. Br J Anaesth. 2005 Dec; 95(6): 746–55. DOI: 10.1093/bja/aei262

[62] Reuter D.A., Felbinger T.W., Kilger E., et al. Optimizing fluid therapy in mechanically ventilated patients after cardiac surgery by on-line monitoring of left ventricular stroke volume variations. Comparison with aortic systolic pressure variations. Br J Anaesth. 2002 Jan; 88(1): 124–6. DOI: 10.1093/bja/88.1.124

[63] Reuter D.A., Kirchner A., Felbinger T.W., et al. Usefulness of left ventricular stroke volume variation to assess fluid responsiveness in patients with reduced cardiac function. Crit Care Med. 2003 May; 31(5): 1399–404. DOI: 10.1097/01.CCM.0000059442.37548.E1

[64] Roy S., Couture P., Qizilbash B., et al. Hemodynamic pressure waveform analysis in predicting fluid responsiveness. J Cardiothorac Vasc Anesth. 2013 Aug; 27(4): 676–80. DOI: 10.1053/j.jvca.2012.11.002

[65] Saugel B., Kirsche S.V., Hapfelmeier A., et al. Prediction of fluid responsiveness in patients admitted to the medical intensive care unit. J Crit Care. 2013 Aug; 28(4): 537.e1–9. DOI: 10.1016/j.jcrc.2012.10.008

[66] Shim J.K., Song J.W., Song Y., et al. Pulse pressure variation is not a valid predictor of fluid responsiveness in patients with elevated left ventricular filling pressure. J Crit Care. 2014 Dec; 29(6): 987–91. DOI: 10.1016/j.jcrc.2014.07.005

[67] Shin Y.H., Ko J.S., Gwak M.S., et al. Utility of uncalibrated femoral stroke volume variation as a predictor of fluid responsiveness during the anhepatic phase of liver transplantation. Liver Transplant Off Publ Am Assoc Study Liver Dis Int Liver Transplant Soc. 2011 Jan; 17(1): 53–9. DOI: 10.1002/lt.22186

[68] Soliman R.A., Samir S., el Naggar A., et al. Stroke volume variation compared with pulse pressure variation and cardiac index changes for prediction of fluid responsiveness in mechanically ventilated patients. Egypt J Crit Care Med [Internet]. 2015; 3(1): 9–16. DOI: https://doi.org/10.1016/j.ejccm.2015.02.002

[69] Song Y., Kwak Y.L., Song J.W., et al. Respirophasic carotid artery peak velocity variation as a predictor of fluid responsiveness in mechanically ventilated patients with coronary artery disease. Br J Anaesth. 2014 Jul; 113(1): 61–6. DOI: 10.1093/bja/aeu057

[70] Suehiro K., Rinka H., Ishikawa J., et al. Stroke volume variation as a predictor of fluid responsiveness in patients undergoing airway pressure release ventilation. Anaesth Intensive Care. 2012 Sep; 40(5): 767–72. DOI: 10.1177/0310057X1204000503

[71] Taccheri T., Gavelli F., Teboul J.L.L., et al. Do changes in pulse pressure variation and inferior vena cava distensibility during passive leg raising and tidal volume challenge detect preload responsiveness in case of low tidal volume ventilation? Crit Care. 2021 Mar; 25(1): 110. DOI: 10.1186/s13054-021-03515-7

[72] Thiel S.W., Kollef M.H., Isakow W. Non-invasive stroke volume measurement and passive leg raising predict volume responsiveness in medical ICU patients: an observational cohort study. Crit Care. 2009; 13(4): R111. DOI: 10.1186/cc7955

[73] Trof R.J., Danad I., Reilingh M.W.L., et al. Cardiac filling volumes versus pressures for predicting fluid responsiveness after cardiovascular surgery: the role of systolic cardiac function. Crit Care. 2011; 15(1): R73. DOI: 10.1186/cc10062

[74] Vistisen S.T., Struijk J.J., Larsson A. Automated pre-ejection period variation indexed to tidal volume predicts fluid responsiveness after cardiac surgery. Acta Anaesthesiol Scand. 2009 Apr; 53(4): 534–42. DOI: 10.1111/j.1399-6576.2008.01893.x

[75] Vistisen S.T. Using extra systoles to predict fluid responsiveness in cardiothoracic critical care patients. J Clin Monit Comput. 2017 Aug; 31(4): 693–9. DOI: 10.1007/s10877-016-9907-8

[76] Wang Y., Jiang Y., Wu H., et al. Assessment of fluid responsiveness by inferior vena cava diameter variation in post-pneumonectomy patients. Echocardiography. 2018 Dec; 35(12): 1922–5. DOI: 10.1111/echo.14172

[77] Wyffels P.A.H., Durnez P.J., Helderweirt J., et al. Ventilation-induced plethysmographic variations predict fluid responsiveness in ventilated postoperative cardiac surgery patients. Anesth Analg. 2007 Aug; 105(2): 448–52. DOI: 10.1213/01.ane.0000267520.16003.17

[78] Xu B., Yang X., Wang C., et al. Changes of central venous oxygen saturation define fluid responsiveness in patients with septic shock: A prospective observational study. J Crit Care. 2017 Apr; 38: 13–9. DOI: 10.1016/j.jcrc.2016.09.030

[79] Xu L.Y., Tu G.W., Cang J., et al. End-expiratory occlusion test predicts fluid responsiveness in cardiac surgical patients in the operating theatre. Ann Transl Med. 2019 Jul; 7(14): 315. DOI: 10.21037/atm.2019.06.58

[80] Xu Y., Guo J., Wu Q., et al. Efficacy of using tidal volume challenge to improve the reliability of pulse pressure variation reduced in low tidal volume ventilated critically ill patients with decreased respiratory system compliance. BMC Anesthesiol. 2022 May; 22(1): 137. DOI: 10.1186/s12871-022-01676-8

[81] Yazigi A., Khoury E., Hlais S., et al. Pulse pressure variation predicts fluid responsiveness in elderly patients after coronary artery bypass graft surgery. J Cardiothorac Vasc Anesth. 2012 Jun; 26(3): 387–90. DOI: 10.1053/j.jvca.2011.09.014

[82] Zhang X., Feng J., Zhu P., et al. Ultrasonographic measurements of the inferior vena cava variation as a predictor of fluid responsiveness in patients undergoing anesthesia for surgery. J Surg Res. 2016 Jul; 204(1): 118–22. DOI: 10.1016/j.jss.2016.03.036

[83] Zhao F., Wang P., Pei S., et al. Automated stroke volume and pulse pressure variations predict fluid responsiveness in mechanically ventilated patients with obstructive jaundice. Int J Clin Exp Med. 2015; 8(11): 20751–9.

[84] Zimmermann M., Feibicke T., Keyl C., et al. Accuracy of stroke volume variation compared with pleth variability index to predict fluid responsiveness in mechanically ventilated patients undergoing major surgery. Eur J Anaesthesiol. 2010 Jun; 27(6): 555–61. DOI: 10.1097/EJA.0b013e328335fbd1