Perioperative Hemodynamic Goal Directed Therapy. Where are we now?

Academiejaar 2013 2014 Perioperative Hemodynamic Goal Directed Therapy. Where are we now? An updated systematic review and meta-analysis of a heterogeneous literature Dr. Koen Lapage Promotor: Dr. Wyffels Piet Co-promotor: Prof. dr. Wouters Patrick Masterproef voorgedragen in de master in de specialistische geneeskunde Anesthesie-Reanimatie 1

Preface In this preface, I would like to thank several people who made this publication possible: I'd like to start with thanking Dr. P. Wyffels. His guidance during this particular work and his statistical cunning mind have helped me tremendously in this systematic review. Apart from his academic insight, he's a great colleague to work with. Thanks a lot, Piet! - Second of all, I want to thank Prof. dr. Wouters for his rereading and editorial work. - At last, and most important of all, I want to thank my wife, Griet, and my little girls, Marthe and Oona who had to miss my company during the writing of this systematic review. Without your support, this wouldn't have been possible. You're the fuel that keeps my engine going. A very big loving thanks! 5

Table of Content Preface... 5 1. Abstract... 9 a. Background... 9 b. Methods... 9 c. Main results... 9 d. Authors' conclusions... 10 2. Introduction... 11 3. Objectives... 15 4. Methods... 16 a. Criteria for considering studies for this review... 16 i. Types of studies... 16 ii. Types of participants... 16 iii. Types of interventions... 16 iv. Types of outcome measures... 16 b. Search methods for identification of studies... 17 i. Electronic searches... 17 ii. Searching other resources... 17 c. Data collection and analysis... 18 i. Selection of studies... 18 ii. Data extraction and management... 19 iii. Assessment of risk of bias in included studies... 19 iv. Measures of treatment effect... 20 v. Dealing with missing data... 20 vi. Assessment of heterogeneity... 20 vii. Assessment of reporting biases... 20 viii. Data synthesis... 20 ix. Subgroup analysis and investigation of heterogeneity... 21 x. Sensitivity analysis... 21 5. Results... 22 a. Description of studies... 22 b. Results of the search... 22 i. Included studies... 22 ii. Excluded studies... 22 6

c. Risk of bias in included studies... 23 i. Allocation (selection bias)... 24 ii. Blinding (performance bias and detection bias)... 25 iii. Incomplete outcome data (attrition bias)... 25 iv. Selective reporting (reporting bias)... 25 v. Other potential sources of bias... 25 d. Effects of interventions... 27 i. Mortality... 27 ii. Morbidity... 28 iii. Resource utilization... 30 iv. Subgroup analysis... 31 v. Sensitivity analysis... 35 6. Discussion... 36 a. Summary of main results... 36 b. Overall completeness and applicability of evidence... 37 c. Potential biases in the review process... 38 d. Agreements and disagreements with other studies or reviews... 38 7. Authors' conclusions... 39 a. Implications for practice... 39 b. Implications for research... 39 8. References... 40 9. Nederlandstalige samenvatting... 52 10. Characteristics of studies... 55 a. Characteristics of included studies... 55 b. Characteristics of excluded studies... 73 c. Characteristics of ongoing studies... 75 11. Analyses... 92 a. Analysis 1: Individual Forrest plots of Fixed and Random effect models of the original Cochrane systematic review... 92 b. Analysis 2: Individual Forrest plots of the Grocott et al systematic review and the Update (both analyses using a random effect model)... 100 c. Analysis 3: Subgroup analysis using the pooled data... 108 d. Analysis 4: Subgroup Analysis Sensitivity Analysis excluding studies with active controls... 116 7

12. Appendices... 124 a. Appendix 1: Search string applied for the CENTRAL search... 124 b. Appendix 2: Search string applied for the MEDLINE literature search... 124 c. Appendix 3: Search string applied for the EMBASE search... 125 8

1. Abstract a. Background Increased morbidity and mortality result from the inability to tune global blood flow. Goaldirected hemodynamic optimization aims to augment flow in the perioperative setting. A triad of haemodynamic variables has been used to guide fluid therapy. b. Methods We searched the Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE and MEDLINE (from April 2012 to December 2013). We included randomized controlled trials (RCT) with or without blinding, without language restrictions. We included adult participants undergoing elective or semi-elective surgery in the operating theatre. The intervention meets following criteria: perioperative fluid administration, with or without inotropic or vasoactive drugs, to increase blood flow (relative to control) against explicit measured goals. c. Main results When patients are treated with a perioperative goal-directed fluid therapy protocol, the intervention results in a mean reduction of both the longest reported mortality (RR of 0.72, 95% CI 0.53 to 0.96) and in-hospital/28-day mortality (RR of 0.73, 95% CI 0.55 to 0.97). In addition, retrieved data show that the intervention yields a significant benefit on several morbidity entities. The intervention shows to avoid renal impairment in 3/100 patients, a pneumonia in 2/100 patients, a wound infection in 4/100 patients, postoperative respiratory failure/ards in 7/100 patients and in 21/100 patients postoperative complications. The intervention does not yield a reduced risk of developing arrhythmia, myocardial infarction, congestive heart failure/pulmonary edema and venous thrombosis. No reduction in infection rate except wound infections was described. Postoperative length of hospital stay is reduced by one day without a reduced ICU stay. Subgroup analysis shows no significant reduction of mortality(p=0.629) and in-hospital/28- day mortality(p=0.594) in any subgroup. Dynamic filling parameter(dfp)-studies have a reduced risk of developing renal impairment and wound infections. Less complications in DFP-studies and a shorter hospital stay in the fluid challenge(fc)-studies was noted. There was no reduced duration of the ICU stay. 9

d. Authors' conclusions Manipulating global blood flow results in a magnitude of risk reductions in mortality and shows to avoid relevant complications in high risk surgical patients with a shorter hospital stay duration. A strong trend towards the beneficial use of dynamic filling parameters in the intervention is described. 10

2. Introduction Surgery deregulates fluid homeostasis[1] and induces a hyper-metabolic state with increased tissue oxygen consumption (VO2)[2]. Increased morbidity and mortality result from the inability to tune oxygen delivery (DO2) to the increased consumption requirements[3]. The concept of oxygen debt and tissue hypoperfusion being the cornerstone in the pathophysiology of shock arose early in the previous century, based on VO2 measurements[2,4]. Further investigations revealed temporal patterns of oxygen consumption during shock. An early reduction in VO2 preceded an initial hypotensive event and was followed by compensatory increases in cardiac output and VO2[5-9]. In 1992, Shoemaker documented greater tissue oxygen deficits in patients who subsequently developed multiple organ failure than in patients without organ failure. Furthermore, his research demonstrated that a reduction in the calculated oxygen debt resulted in reduced organ failure and decreased mortality when VO2 was maintained at supranormal values[10]. In order to prevent organ failure by increasing global blood flow, Shoemaker and his colleagues proposed an oxygen delivery index > 600 ml O2/min.m² which was found in survivors without complications after high risk surgery [8,11]. Numerous researchers studied the clinical impact and efficacy of supranormal oxygen delivery by increasing global blood flow. Several non-systematic reviews have tried to draw general conclusions [12-16]. These conclusions only remain indicative due to non-systematic approach lacking a rigorous scientific approach towards the search for or abstraction and analysis of the included data. The past 15 years, several systematic reviews were conducted in order to draw a general conclusion on this topic. Some of these meta-analyses narrowed their scope on one specific aspect of morbidity [17,18]; others didn't restrict their field of interest to the perioperative setting [19-22]. Recently, Grocott M. et al published a systematic review and meta-analysis on goal-directed therapy in the perioperative setting with data published until March 2012, conducted in a thorough scientific manner as depicted by the Cochrane Collaboration Review Steering group. They concluded that optimizing global blood flow results in a reduction of postoperative complications and length of hospital stay, rather than reducing perioperative mortality[23]. DO2 is determined by several factors: (a) cardiac output which is determined by stroke volume (SV) and heart frequency; (b) arterial oxygen content with haemoglobin, arterial 11

oxygen saturation and, to a lesser extent, arterial partial pressure of oxygen as key factors. Possible contributing factors deranging tissue oxygenation from normal values can be: (a) Anaesthesia related cardiovascular depression; (b) delay or failure to keep up with fluid and blood losses; (c) anaemia; (d) pre-existing comorbidities such as cardiac, pulmonary or renal insufficiencies; (e) activation of an inflammatory response to surgery resulting in haemodynamic active substances as cytokines. Perioperative fluid management is a practice which remains subject to debate amongst anaesthesiologists. Intraoperative fluid management based on heart rate, mean arterial pressure, central venous pressure, urine output and replacement of ongoing fluid losses define today's standard approach to the perioperative fluid paradigm[24]. Defining the circulatory volume status of patients is complex and the preoperative fluid deficit differs amongst individual patients in different surgical settings[25]. Adverse outcomes can be the result from either inadequate fluid administration or fluid overloading. Failure to compensate ongoing fluid losses will result in a reduction of circulatory volume, shifting blood towards vital organs (brain, heart) away from non-vital organs (skin, gut, kidneys) and hypoperfusion of non-vital organs. Investigators documented an association between adverse perioperative outcome and inadequate tissue perfusion measured by gastric tonometry [26,27]. On the contrary, fluid excess will also lead to adverse outcomes. Overloading the vascular compartment leads to increased hydrostatic pressures, hereby deranging the equilibrium between filtration and absorption of fluid on the level of the capillary vessels. This leads to peripheral and/or lung edema and results in a local and/or systemic decrease in tissue oxygenation. During the last decade, several investigators have shown the increased incidence of postoperative complications and risk of death after surgery due to fluid excess[28-34]. At the end of the late nineteenth century, Frank and Starling described a relationship between SV and preload. Preload is defined as the end volumetric pressure that stretches the cardiac myocytes prior to contraction[35]. Essentially, preload is the volume of blood present in the ventricle prior to isovolumetric contraction of the ventricle which is measurable by ultrasonography as the end-diastolic volume (EDV). Hypovolaemia will result in a reduction of preload and a reduced SV. Administering fluid in these patients increases preload and SV. Hypervolaemia increases the EDV without SV gain. On the contrary, it will overstretch the cardiac myocytes, losing contractile force and reducing the stroke volume due to a loss of 12

inotropy. Both conditions have been investigated with documented poorer overall outcome as mentioned above. Figure 1 shows the aforementioned relationship between EDV and SV. The curve evolves from a steep part (where a small increase in EDV results in a reasonable increase in SV) to a flat part (an increase in EDV barely increases SV). Based on these findings, patients can be split in either one of 2 groups: (a) a patient responds to a fluid bolus with a significant increase in EDV and SV (on the steep part of the EDV-SV relationship), meaning patients are responsive to the fluid bolus; (b) a patient does not respond to the fluid bolus (on the flat part of the EDV-SV relationship), being unresponsive to fluid therapy. Fluid therapy can be titrated on a patient's need, based on his position on the EDV-SV slope. Figure 1 - Frank-Starling relationship showing the relationship between enddiastolic volume (EDV; ventricular preload) and stroke volume (SV)(Y-axis). Performing on the steep part of the curve, a slightly increased EDV will result in an important increase in SV (A), called fluid responsiveness. Sitting on the flat part of the curve (B), an increase in EDV will not significantly increase SV; in this situation a patient is called fluid unresponsive. Adapted from Michard 2000. Goal-directed haemodynamic optimization (GDHO) aims to augment flow in the perioperative setting. Flow augmentation is initially reached by administering fluid, although this is not always the case as the EDV-SV relationship teaches. Michard et al. demonstrated that merely 50% of patients in acute circulatory failure responded to volume expansion [36]. 13

The need for predictive factors of volume expansion efficacy is hereby stressed. The tool to discriminate responders to volume expansion from non-responders wherein vasopressor and/or inotropic support should preferentially be used, remains however mystified. Several parameters have been postulated to be predictive factors in order to steer a patients' fluid management. The central venous pressure and pulmonary artery occlusion pressure, as bedside indicators of respectively RV and LV preload, have been persistently used to expand a patients' circulatory volume. However, the poor value of CVP[37-40] and PAOP[37,38,40-42] in predicting volume expansion efficacy has been demonstrated. The mean baseline value of CVP and PAOP between volume responders and non-responders was not statistically different in the previous reported trials[37-41,43]. Toussignant did report a significant difference but due to overlap of individual baseline values, no discriminating threshold value was retrieved[42]. One should bare in mind that it is necessary to relate the preload dependency with contractility and afterload. A certain preload value can be associated with preload dependency in a normal heart whereas the same value can be associated with preload independency in a failing heart (Figure 2). Despite these findings, CVP is still frequently used and/or recommended in guidelines as a parameter to steer fluid management[44,45]. A systematic review by Marik showed in 2008 that there is no association between the CVP and blood volume and that the CVP is not able Figure 2 - This figure shows both the Frank-Starling relationship of a normal performing left heart (A) and a failing heart (B). This finding stresses that a particular preload value can not be determined as a threshold value for fluid dependency. In situation A, a fluid bolus will significantly increase the heart's stroke volume. In situation B, a fluid bolus will not result in a significant increase of the stroke volume and should be avoided. Adapted from Michard 2000. 14

to predict fluid responsiveness. At any CVP the likelihood that the CVP could predict fluid responsiveness was only 56%, no better than flipping a coin. This finding also suggests that there is no clear threshold to determine whether a patient is hypovolaemic or hypervolaemic[46]. As more evidence is provided during the last decade that static measurements fail to predict fluid responsiveness, a duality of monitoring was designed the last 2 decades: (a) fluid bolus based Doppler measurements (e.g. SV and corrected flow time (FTc) or echo assessment of SV and inferior cava compressibility (IVC variability)); (b) dynamic filling parameters (e.g. plethysmographic variability index (PVI), invasive arterial pressure-based parameters such as stroke volume (SV), stroke volume variation (SVV), pulse pressure variation (PPV) or systolic pressure variation (Delta-down). The physiological base of these parameters lies in the haemodynamic variability induced by mechanical ventilation on heart-lung interaction and volume status[47]. Doppler-based algorithms require a fluid bolus in order to assess whether a > 10% increase in SV is achieved. If such an increase is not achieved, practitioners should refrain from extra fluid boluses and consider pharmacotherapy to increase flow. Before using a certain variable into clinical practice, practitioners need to be aware of the requirements and limitations. Using either SVV or PPV into a titrated fluid practice requires a patient to be ventilated by a controlled positive pressure ventilation with an adequate tidal volume in the absence of cardiac arrhythmias. These parameters have not been adequately validated in other conditions. In appropriate cases, SVV > 10% or PPV > 12% is usually used as threshold for fluid administration[48-50]. A fluid-unresponsive patient however will present himself with a SVV < 10% or PPV < 12%, meaning adding iv fluid will not increase preload. A fluid-unresponsive patient has more benefit from fluid restriction with added pharmacotherapy (vasopressors, inotropes) to gain flow augmentation. This knowledge permits to simplify clinical decision-making, pushing perioperative care of high risk patients to a further level. 3. Objectives Whether an intervention provides more benefit than it harms patients is of key importance, especially in high risk surgery patients with a limited physiologic capacity. The aim of this 15

systematic review and meta-analysis is to update the publication of Grocott M. et al with published data until December, 31th 2013. We conducted also a subgroup analysis with the following research question: Does the use of different parameters for fluid management (e.g. static parameters, dynamic parameters or fluid challenge variables) have a different impact on postoperative outcome? 4. Methods a. Criteria for considering studies for this review i. Types of studies We included randomized controlled trials (RCT), with or without blinding, that were available as full published studies. We applied no language restrictions. ii. Types of participants We included participants, aged 16 years or older, undergoing an elective or semi-elective surgical intervention in the operating theatre. iii. Types of interventions Perioperative administration (with no defined restrictions towards temporal characteristics of the fluid regimen) of fluid, with or without the administration of inotropic or vasoactive drugs, to increase blood flow (relative to control) against explicit measured goals: cardiac output (CO), cardiac index (CI), oxygen delivery (DO2) or oxygen delivery index (DO2I), oxygen consumption (VO2) or oxygen consumption index (VO2I), mixed venous saturation (SvO2), O2 extraction ratio (O2ER) and lactate. iv. Types of outcome measures Primary outcomes - Mortality (at longest reported follow-up). - Mortality at 28 days. 16

Secondary outcomes - Morbidity: 1. Rate of overall complications; 2. Rate of renal impairment, arrhythmia, infection, acute respiratory distress syndrome (ARDS), myocardial infarction, pulmonary edema, venous thrombosis; 3. Resource utilization: length of intensive care unit (ICU) stay, length of hospital stay. b. Search methods for identification of studies i. Electronic searches We searched the Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane library (2013, issue 2) restricting the search in time from April 2012 to December 2013 (Appendix 1); EMBASE via OpenAthens (from April 2012 to December 2013) (Appendix 2); MEDLINE via OpenAthens (from April 2012 to December 2013) (Appendix 3). From our EMBASE search, we combined our top-specific keywords with the Cochrane highly sensitive search strategy for identifying RCTs (Higgins 2011, Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.1). We modified the filter settings for MEDLINE. For identifying potentials RCTs, we used specific keywords. ii. Searching other resources We extended the search to ongoing RCTs. The list of ongoing RCTs is available, see Ongoing studies. 17

c. Data collection and analysis i. Selection of studies Two independent authors (KL and PW from April 2012 to December 2013) identified titles and abstracts of potentially eligible studies. Any disagreement was resolved by discussion. We abstracted the study characteristics in data sheets, see Characteristics of included studies. Study characteristics contain study design, study population, interventions and outcomes. Study flow diagram is shown in Figure 3 (with respect to the PRISMA statement, Transparent reporting of systematic reviews and meta-analysis). Figure 3 - Study flow diagram. 18

ii. Data extraction and management Two authors (KL and PW) independently extracted data. Consensus was achieved by resolving disparity in data collection by discussion. We did not make contact with authors in the absence of appropriate published data. iii. Assessment of risk of bias in included studies We performed a risk of bias assessment according to the Cochrane risk of bias tool[55]. This tool allows review authors to weigh several domains of the methodological quality of the included trials. The several domains are listed as following: 1. Random sequence generation (selection bias): description of the method used to generate the allocation sequence of patients. The aim of this domain is to provide sufficient details whether the used sequence provides comparable groups. 2. Allocation concealment (selection bias): description of the used concealment method in order to assess whether patient allocation could be foreseen in advance of, or during enrolment. 3. Blinding of participants and personnel (performance bias): description of methods used, if any, to prevent participants and involved care personnel to be unaware of the planned intervention. Details, on whether the intended blinding was effective, have to be reported as well. 4. Blinding of outcome assessment (detection bias): description of the used measures, if any, to blind outcome assessors to which intervention a participant received. Details, on whether the intended blinding was effective, have to be reported as well. 5. Incomplete outcome data (attrition bias): description of the completeness of the outcome data for each main outcome, including attrition and exclusion from the analysis. Reports on attrition and exclusions, the number in each intervention group (in respect to the overall number of participants), reasons for attrition, exclusions and re-inclusions in the analysis performed by the review authors. 6. Selective reporting (reporting bias): evaluation of how selective reporting outcome was weighed by the review authors and what was found. 7. Other bias: any bias withheld by the review authors, not fitting in either of previous summarised domains. 19

iv. Measures of treatment effect The meta-analysis was performed using R 2013[51]; (URL: http://www.r-project.org). The Metafor package was used for R; Metafor[52] (URL: http://cran.rproject.org/package=metafor). Relative risk and mean differences were used as measures of treatment effect. v. Dealing with missing data Due to the lacking of sufficient time, we did not contact the authors if data were not reported in the publication or the electronic supplements. vi. Assessment of heterogeneity We assessed heterogeneity in the different analysis using the significance test of Q, T² and I². All values are reported, but due to power issues the planned subgroup analysis was always performed. vii. Assessment of reporting biases Contour-enhanced meta-analysis funnel plots were constructed according to Peters et al.[53], if at least 10 studies were included in the analysis in line with recent recommendations[54]. viii. Data synthesis The meta-analysis was performed using R 2013[51]; (URL: http://www.r-project.org). The Metafor package was used for R; Metafor[52] (URL: http://cran.rproject.org/package=metafor). We performed the following sequence of analysis: 1. The original published analysis with a fixed effects model was compared with a random effects model. 2. The treatment effect calculated using a random effects model on the studies included by Grocott et al.[23] were compared to the treatment effect using that same model on the included studies from the literature update. We used according to Grocott et al.[23] relative risk (with a 95% confidence interval (CI)) for dichotomous outcomes and mean difference (with a standard deviation (SD) of 95% CI) for continuous variables. 20

ix. Subgroup analysis and investigation of heterogeneity We conducted a subgroup analysis based on the filling parameters used in the different goal directed treatment algorithms: "Static Filling Parameters": predefined CVP and PCWP values to guide fluid administration based in the study-specific algorithm. "Fluid Challenge": a fast acting cardiac output measurement device (e.g. Doppler, Pulse contour analysis) is used in the study-specific algorithm to measure changes in cardiac output after fluid loading. A threshold value is predefined and used to steer the study-specific fluid management. "Dynamic Filling Parameters": The study-specific algorithm uses ventilation-induced changes in cardiac output like stroke volume variation (or a surrogate like PPV) to assess the effectiveness of the fluid loading. We tested the subgroups' results with a between group test to test their relative significance. x. Sensitivity analysis The random effects model was used for both the primary outcome (longest reported mortality) and the secondary outcomes, done in this meta-analysis with subgroup analysis. The intervention in the protocol group was relative to the study design. Some studies were excluded due to the control group having explicit blood flow goals. We performed a sensitivity analysis excluding these studies. 21

5. Results a. Description of studies The retrieved studies can be found at Characteristics of included studies, Characteristics of excluded studies and Characteristics of ongoing studies. Each included study is a randomized controlled trial of surgical participants. Compared to the control group, the intervention group had a specific algorithm to optimize global blood flow towards predefined goals. b. Results of the search The initial electronic search, concerning the literature update, retrieved 861 potential RCTs (see Figure 3). After duplicate removal, the search yielded 571 studies. One additional study was added to this result. This study was provided by a relevant haemodynamic monitoring company. No additional studies were identified following screening of the reference lists with respect to the update-linked time interval. After screening the titles and abstracts of the withheld studies, 44 potentially eligible RCTs were withheld in the selection process. Of these 44 RCTs, 33 studies were excluded from the selection based on the inclusion criteria for the meta-analysis. The reasons for exclusions are listed in Characteristics of excluded studies. Inclusion criteria were met in 11 RCTs, containing 1034 participants. A list of the included studies can be found in Characteristics of included studies. i. Included studies We included 11 RCTs in the literature update (see Characteristics of included studies). These studies took place in Europe (8), New Zealand (1), China (1) and the U.S.A. (1). All but one study recruited patients, scheduled for elective surgery. One study recruited patients planned for semi-elective surgery. All of these studies were published between April 1th 2012 and December 31th 2013. ii. Excluded studies Based on the inclusion criteria, we excluded 33 RCTs[98-130] (Characteristics of excluded studies). We excluded one study containing critically ill patients. We excluded 23 studies in 22

which the study protocol was not comparing flow goals. Other studies were excluded because of a restrictive fluid regimen (3), different outcome parameters not eligible for this metaanalysis (1), critically ill participants (1) and not being a RCT (5). c. Risk of bias in included studies The risk of bias was evaluated with the Cochrane tool[55]. This was performed by two authors (KL and PW) independently; any disparity was resolved by discussion. The methodological quality is presented in a summary table and a graph (Figure 4, Figure 5). Figure 4 - Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies. 23

Figure 5 - Risk of bias summary: review authors' judgements about each risk of bias item for each included study. i. Allocation (selection bias) All included studies randomly allocated participants. The randomization procedure was described in all 11 included studies (100%)[56-66]. A sequentially, sealed envelope technique was used in 7 studies (73%)[59-61,63-66]. One study used a telephone based method of allocation concealment[56]. The method of allocation concealment was unclear in 3 24

studies[57,58,62]. The method of random allocation was weighed as adequate in 10 studies (91%)[56-62,64-66]. The method of allocation concealment was considered adequate in 7 studies (64%)[56,59-61,63,65,66]. ii. Blinding (performance bias and detection bias) We assessed the blinding of personnel or participants as adequate in 6 studies (55%), reflecting the nature of the intervention [56-58,60,64,65]. Blinding of all outcomes was assessed as adequate in 10 studies (91%)[56-62,64-66]. iii. Incomplete outcome data (attrition bias) Attrition bias was detected in one study[56] where data collection on a few secondary outcome parameters was still ongoing. It remains in this particular study unclear if the ongoing data collection can introduce attrition bias. iv. Selective reporting (reporting bias) As a result of ongoing data collection, one study[56] was proven inadequate with a possibility to introduce reporting bias. The incomplete reporting of two predefined secondary outcome parameters in another study[65] may have introduced reporting bias. v. Other potential sources of bias Exclusion of participants after randomization was present in two studies[63,64] which may have introduced selection bias. One study[57] used unblinded data after randomization, making selection bias susceptible. In order to test the effect of publication bias, we generated a contour-enhanced funnel plot for the primary outcome (Figure 6). Accordingly, we produced a contour-enhanced funnel plot for the complication rate (Figure 7). 25

Figure 6 - Contour-enhanced funnel plot: mortality. 26

Figure 7 Contour-enhanced funnel plot: rate of complications. d. Effects of interventions See: Summary of findings for the main comparison. i. Mortality 1. Long-term Mortality In the meta-analysis of Grocott et al.[23] a fixed effects model was used as a statistical model. We re-analyzed the reported data to a random effects model (Analysis 1.1) yielding a RR of 0.70 (95% CI 0.52 to 0.96, p=0.09, I²=30.35%). Two studies in the literature update reported mortality data[56,59]. The overall mortality in these 2 papers, using the longest reported follow-up, was 4/141 (2.83%) in the intervention group and 5/149 (3.36%) in the control group, RR of 0.72 (95% CI 0.53 to 0.96, p=0.149, I²=25.95%)(Analysis 2.1). 27

2. Hospital or 28 day mortality In the meta-analysis of Grocott et al.[23] a fixed effects model was used as a statistical model. We re-analysed the reported data to a random effects model (Analysis 1.2), yielding a RR of 0.72 (95% CI 0.53 to 0.97, p=0.242, I²=18.86%). Two studies in the literature update reported mortality data[56,59]. The in-hospital or 28 day mortality of these 2 studies was 4/141 (2.83%) in the intervention group and 5/149 (3.36%) in the control group. Based on these update data, a RR of 0.73 (95% CI 0.55 to 0.97, p=0.349, I²=14.98%) was calculated (Analysis 2.2). ii. Morbidity According to the previously published meta-analysis[23], we analyzed eight categories of morbidity using the definitions of the study investigators. In most cases no specific criteria were reported for morbidities. 1. Renal impairment According to Grocott et al., we accepted the reported rate of renal impairment by the study investigators[23]. In a random effects model using the Grocott et al. data, the intervention reduced the risk of renal impairment, RR of 0.66 (95% CI 0.44 to 1.00, p=0.222, I²=33.83%)(Analysis 1.3). Four studies in the literature update reported renal impairment[56,58,60,63]. These data confirm that the intervention reduced the risk of renal impairment, RR of 0.66 (95% CI 0.47 to 0.94, p=0.396, I²=24.7%)(Analysis 2.3). 2. Arrhythmia Based on the meta-analysis of Grocott et al.[23], we re-analyzed the reported data to a random effects model (Analysis 1.4), yielding a RR of 0.84 (95% CI 0.64 to 1.11, p=0.57, I²=5.67%). Four studies in the literature update reported the development of an arrhythmia[57-60]. Using these data, we were not able to confirm that there is a significant difference between the intervention and the control group in developing an arrhythmia, RR 0.86 (95% CI 0.70 to 1.06, p=0.797, I²=0%)(Analysis 2.4). 28

3. Infection Based on the meta-analysis of Grocott et al.[23], we re-analyzed the reported data to a random effects model(analysis 1.5), yielding a mean RR of 0.88 (95% CI 0.70 to 1.12, p=0.454, I²=0%). Only two studies[60,63] reported the number of participants with infections. Random effects model analysis of this data yielded as result that the intervention reduced the number of participants with infections, a mean RR of 0.74 (95% CI 0.53 to 1.04, p=0.287, I²=24.89%)(Analysis 2.5). Given the provided data in the Grocott et al. publication[23], we re-analyzed this data set in a random effects model for each type of infection (Analysis 1.6; Analysis 1.7; Analysis 1.8; Analysis 1.9). Six studies reported the types of infection (such as pneumonia, sepsis, abdominal/wound infections) separately[56-59,61,65]. The authors of the Cochrane publication[23] stressed that it was not possible to add the total number of infections as the exact denominator was unknown. Whilst extracting the data from the literature update, we came to the same conclusion. Thereby, we kept the analysis for each infection separately. There was no difference between the intervention and control group in the rates of: pneumonia, a mean RR of 0.78 (95% CI 0.61 to 0.99, p=0.968, I²=0%) (Analysis 2.6); sepsis, a mean RR of 0.87 (95% CI 0.36 to 2.10, p=0.92, I²=0%) (Analysis 2.7) and abdominal infections, a mean RR of 0.56 (95% CI 0.22 to 1.44, p=0.666, I²=0%) (Analysis 2.8). The intervention did significantly reduce the risk of developing a wound infection, a mean RR of 0.62 (95% CI 0.45 to 0.86, p=0.533, I²=14.38%)(Analysis 2.9). 4. Respiratory failure/acute Respiratory Distress Syndrome Our random effects model analysis on the data of Grocott et al.[23] is shown in Analysis 1.10. None of the included studies provided data concerning the rate of respiratory failure or ARDS (Analysis 2.10). Thereby, we come to the same conclusion: a significant difference in the incidence of respiratory failure and ARDS was noted in the intervention group, a mean RR of 0.48 (95% CI 0.26 to 0.90, p=0.666, I²=0%). 5. Myocardial infarction Our random effects model analysis on the data of Grocott et al.[23] is shown in Analysis 1.11. One study in the literature update provided data on the incidence of myocardial infarction in the study population[60]. Based on this data set, we concluded that the intervention did not 29

reduce the rate of myocardial infarction, a mean RR of 0.99 (95% CI 0.69 to 1.43, p=0.743, I²=2.47%)(Analysis 2.11). 6. Congestive heart failure - pulmonary edema Our random effects model analysis on the data of Grocott et al.[23] is shown in Analysis 1.12. None of the included studies provided data concerning the rate of congestive heart failure and pulmonary edema (Analysis 2.12). Thereby, we come to the same conclusion: the intervention did not yield a significant difference in the incidence of congestive heart failure and pulmonary edema, a mean RR of 0.95 (95% CI 0.70 to 1.30, p=0.697, I²=9.58%). 7. Venous thrombosis Our random effects model analysis on the data of Grocott et al.[23] is shown in Analysis 1.13. One study reported the incidence of venous thrombosis[61]. These data confirm that the intervention did reduce the risk of developing venous thrombosis, RR of 0.37 (95% CI 0.15 to 0.91, p=0.793, I²=0%)(Analysis 2.13). 8. Complications Our random effects model analysis on the data of Grocott et al.[23] is shown in Analysis 1.14. Due to the heterogeneous aspect of the included studies for this analysis, there is a significant difference with the findings of Grocott et al.[23], RR of 0.68 (95% CI 0.52 to 0.89, p=0, I²=77.72)(Analysis 1.14). Ten of 11 studies (91%)[56-61,63-66] in the literature update reported the incidence of individual complications in the study population. These data confirmed earlier findings by Grocott et al.[23] that the intervention did reduce the number of study participants with complications, RR of 0.75 (95% CI 0.63 to 0.89, p=0, I²=68.54%)(Analysis 2.14). iii. Resource utilization 1. Length of hospital stay Postoperative length of hospital stay was reported in 7 studies (63.6%) in the literature update[56-60,62-65]. We also used the statistical equations by Hozo et al.[67] to convert median (range/iqr) to mean (SD). SD was estimated in accordance to the study sample size. Based on these data, the intervention did significantly decrease the postoperative length of hospital stay with 1.2 days (95% CI -1.8 to -0.64)(Analysis 2.15). 30

2. Length of critical care stay Postoperative length of hospital stay was reported in 3 studies (27.3%) in the literature update[58,60,64]. Again, the statistical equations by Hozo et al.[67] to convert median (range/iqr) to mean (SD). SD was estimated in accordance to the study sample size. The intervention did not significantly reduce the postoperative length of stay on the intensive care unit (ICU), a mean difference of -0.35 (95% CI -0.71 to 0.00)(Analysis 2.16). iv. Subgroup analysis The second part in our research question was whether the use of a certain haemodynamic parameter (e.g. static filling parameters (SFP), dynamic filling parameters (DFP) or fluid challenge variables (FC)) would have an impact on the postoperative outcome of a surgical patient. We analyzed both the data from the Grocott et al. meta-analysis as the data provided in the literature update in a random effects model. 3. Long-term Mortality We analyzed the effect of the different haemodynamic variables to steer the fluid therapy on postoperative longest reported mortality. Fourteen studies made use of SFP[8,68-80] which yielded a RR of 0.56 (95% CI 0.34 to 0.91) in longest reported mortality in the intervention group. Seventeen studies made use of FC[56-59,61,65,81-91]. Ten studies used DFP[62-64,66,92-97]. The analysis of the FC-based studies resulted in a mean RR of 0.91 (95% CI 0.54 to 1.53). The analysis of the DFP-based protocols yielded a mean RR of 0.75 (95% CI 0.42 to 1.33). Further analysis tested the relative significance between the RR of the tested groups. The difference in RR between the subgroups was not significant (p=0.629)(analysis 3.1). 4. Hospital or 28 day mortality Fourteen studies made use of SFP[8,68-80] which yielded a RR of 0.57 (95% CI 0.36 to 0.93) in in-hospital or 28 day mortality in the intervention group. Seventeen studies made use of FC[56-59,61,65,81-91]. Ten studies used DFP[62-64,66,92-97]. FC resulted in a mean RR of 0.89 (95% CI 0.51 to 1.56). The analysis of the DFP yielded a mean RR of 0.78 (95% CI 0.43 to 1.42). Further analysis tested the relative significance between the calculated RR of the subgroups. The difference in RR between the subgroups was not significant (p=0.594)(analysis 3.2). 31

5. Renal impairment Eleven studies made use of SFP[8,68-72,74-76,78,79] which yielded a RR of 0.69 (95% CI 0.52 to 0.90) in the intervention group for the rate of renal impairment. Ten studies[56,58,81,83-87,91] made use of FC. Six studies reported results based on a DFP protocol[60,63,93,95-97]. The use of FC resulted in a calculated mean RR of 1.01 (95% CI 0.63 to 1.62). The analysis of the DFP yielded a reduction in the risk of developing renal impairment for the DFP-based intervention, a mean RR of 0.41 (95% CI 0.22 to 0.77). Further analysis tested the relative significance between the calculated RR of the subgroups. The difference in RR between the subgroups was not significant (p-value=0.074)(analysis 3.3). 6. Arrhythmia Five studies made use of SFP[70,74,76,79,80] which yielded a RR of 0.89 (95% CI 0.69 to 1.17) in the intervention group for the rate of arrhythmia. Seven studies[57-59,61,83,86,89] made use of FC. Six studies reported results based on a DFP protocol[60,66,92,93,95,97]. Analysis of these last two subgroups yielded a calculated risk reduction when using FC, a mean RR of 0.88 (95% CI 0.52 to 1.49) and DFP, a mean RR of 0.72 (95% CI 0.48 to 1.07). Further analysis tested the relative significance between the calculated RR of the subgroups. The difference in RR between the subgroups was not significant (p=0.652)(analysis 3.4). 7. Infection Three studies made use of SFP[68,74,78] which yielded a RR of 0.71 (95% CI 0.33 to 1.53) in the intervention group for the number of infections. Four studies[81,82,87,90] made use of FC. Four studies reported results based on a DFP protocol[60,63,94,96]. Analysis of these last two subgroups concluded that the mean RR of using FC was 0.63 (95% CI 0.20 to 1.94) and that the mean RR, when using DFP, was 0.71 (95% CI 0.46 to 1.12)(Analysis 3.5). Further analysis tested the relative significance between the calculated mean RR of the subgroups. The difference in mean RR between the subgroups was not significant (p=0.986)(analysis 3.5). Given the previous statistic analysis, we subdivided the several types of infections analogously. For chest infections/pneumonia, 5 studies reported results for SFP[71,74,76,78,79], 8 studies with results for FC[57-59,61,81-84] and 5 studies with results for DFP[66,92,94,95,97]. None of the 3 parameters show a significant reduction of the risk of developing chest 32

infections/pneumonia. We calculated respectively for SFP a mean RR of 0.85 (95% CI 0.64 to 1.13), for FC a mean RR of 0.60 (95% CI 0.30 to 1.21) and for DFP a mean RR of 0.58 (95% CI of 0.33 to 1.04). The inter-subgroup difference in RR is not significant (p=0.388)(analysis 3.6). For sepsis, no sepsis related data was reported in studies with a DFP-based study protocol. For SFP, data from five studies were eligible for analysis[8,68,71,74,79]. For FC, 3 studies were included in the analysis[56,58,59]. For sepsis, we calculated a mean RR, when using SFP, of 0.68 (95% CI 0.26 to 1.77) and a calculated mean RR of 1.19 (95% CI 0.26 to 5.58) when FC was used. The inter-subgroup difference in RR is not significant (p=0.546)(analysis 3.7). For abdominal infections, three studies reported SFP related data[8,71,79], two studies reported FC-related data[58,59] and three studies reported DFP-related data[92,95,97]. When using SFP, a mean RR of 0.50 (95% CI 0.12 to 2.14) was calculated; using FC resulted in a mean RR of 1.61 (95% CI 0.47 to 5.58); the mean RR for using DFP was found to be 0.54 (95% CI 0.19 to 1.52). The inter-subgroup difference in RR is not significant (p=0.343)(analysis 3.8). For wound infections, five studies made use of SFP[8,71,74,76,79], nine studies reported data based on FC[56-59,61,65,83,84,90] and four studies made use of DFP[64,66,92,95]. We calculated the mean RR when using DFP resulting in a mean RR of 0.27 (95% CI 0.13 to 0.55); Studies based on SFP-based protocols had a calculated mean RR of 0.77 (95% CI 0.57 to 1.04) and FC-based studies showed to have a mean RR of 0.67 (95% CI 0.42 to 1.08). The difference between the subgroups was found to be significant (p=0.029)(analysis 3.9). 8. Respiratory failure/acute Respiratory Distress Syndrome Five studies reported SFP-related data[8,71,72,77,79]; two studies made use of FC[81,84]; two studies with DFP-based protocols[92,95] were included in the subgroup analysis. For SFP, a mean RR of 0.47 (95% CI 0.23 to 0.98) was calculated; for FC, a mean RR of 0.33 (95% CI of 0.05 to 2.06) and for DFP, a mean RR of 0.79 (95% CI 0.22 to 2.83). No significant difference was found when subgroups were compared to one another (p=0.709)(analysis 3.10). 33

9. Myocardial infarction Nine studies with SFP-based data[8,68-71,76,78-80], three studies with FC-based data[59,61,83] and five studies with DFP-based data[60,92,93,95,97] were included in the subgroup analysis. When using SFP, we calculated a mean RR of 1.11 (95% CI 0.77 to 1.61); for the use of FC, a mean RR of 1.24 (95% CI 0.22 to 7.07); when using DFP, a mean RR of 0.42 (95% CI 0.18 to 0.96) was calculated. Comparing subgroups showed no significant difference (p=0.105)(analysis 3.11). 10. Congestive heart failure - pulmonary edema Nine studies made use of SFP[8,68-71,74,76,78,79] from which we calculated a mean RR of 0.96 (95% CI 0.70 to 1.32) in the risk of developing congestive heart failure/pulmonary edema in the intervention group. Five studies made use of FC[58,61,82,83,87]. Two studies used DFP[92,95]. Analysis of these last two subgroups calculated for FC a mean RR of 0.89 (95% CI 0.51 to 1.56). The analysis of the DFP yielded a mean RR of 0.78 (95% CI 0.43 to 1.42). No significant difference was found when subgroups were compared to one another (p=0.652)(analysis 3.12). 11. Venous thrombosis Five studies reported SFP-related data[8,71,74,76,79]; five studies made use of FC[57,59,61,83,89]; three studies with DFP-based protocols[92,95,97] were included in the subgroup analysis. For the use of SFP a mean RR of 0.78 (95% CI 0.18 to 3.45) was calculated; for the use of FC, we found a mean RR of 1.33 (95% CI of 0.36 to 4.88) and for the use of DFP, a mean RR of 0.69 (95% CI 0.11 to 4.26) was retrieved. No significant difference was found when subgroups were compared to one another (p=0.694)(analysis 3.13). 12. Complications Seven studies reported SFP-related data[8,69,72-74,77,79]; twelve studies made use of FC[56-59,61,65,81,82,85,87,88,91]; eight studies with DFP-based protocols[60,64-66,92,95-97] were included in the subgroup analysis. For the use of SFP, we were able to calculate a mean RR of 0.68 (95% CI 0.41 to 1.13). The analysis of FC-based protocols resulted in a mean RR of 0.81 (95% CI of 0.67 to 0.97) and for DFP-base algorithms, a RR of 0.78 (95% 34

CI 0.11 to 4.26) was found. No significant difference was found when subgroups were compared to one another (p=0.994)(analysis 3.14). 13. Postoperative length of hospital and intensive care unit stay For postoperative length of hospital stay, eleven studies made use of SFP[8,68-72,74-76,78,79]; fourteen studies reported data based on FC[56-58,65,81-88,90,91] and four studies made use of DFP[60,62,63,92-97]. Analyzing these data for SFP, we found -0.58 days (95% CI -1.52 to 0.35) reduction of length of hospital stay; for FC-based studies, the reduction of hospital stay was calculated to -2.19 days (95% CI -3.27 to -1.10). Using DFP-based algorithms, the postoperative length of hospital stay was reduced with -0.95 days (-2.47 to 0.57). The difference between the subgroups was not significant (p=0.092)(analysis 3.15). For postoperative intensive care unit stay, nine studies reported SFP-related data[8,68,69,71,73-75,78,79]; one study made use of FC[58]; six studies with DFP-based protocols[60,64,92,93,95,96] were included in the subgroup analysis. The reduction in length of ICU stay for SFP-based protocols was calculated to -0.31 hours (95% CI -0.90 to 0.29) and for FC-based studies a reduction of -0.12 (95% -0.39 to 0.15) was found in the one study. For DFP-based studies, the reduction of ICU stay was found to be -0.59 hours (95% CI -1.17 to - 0.01). The difference between the subgroups was not significant (p=0.73)(analysis 3.16). v. Sensitivity analysis A random effects model was used for both primary outcome and all secondary outcome analyses as well as the specified subgroup analysis. We performed sensitivity analyses of the analysis method used to generate the relative risks for mortality, morbidity and resource utilization in the subgroup analysis. Four studies[73-75,77] of the original Cochrane analysis[23] were excluded due to access to blood flow measures and administration of fluid and/or inotrope therapy in the control group. From the literature update, four studies[57,58,61,62] were excluded from a sensitivity analysis for the same reason. With exclusion of these studies, the intervention reduced longest reported mortality with a RR of 0.73 (95% CI 0.54 to 0.99) (Analysis 4.1) and in-hospital or 28-day mortality with a RR of 0.74 (95% CI 0.57 to 0.95) (Analysis 4.2). The sensitivity analysis performed on the updated data in addition to the data set of Grocott et al.[23] did not show any alterations for all outcomes (Analysis 4.3; Analysis 4.4; Analysis 4.5; Analysis 4.6; Analysis 4.7; Analysis 4.8; Analysis 4.9; Analysis 4.10; Analysis 4.11, Analysis 4.12; Analysis 4.13; Analysis 4.14; 35