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Supplementary Text R2 1

Additional details of methods and results

Content

Search strategy…………………………………………………………………………..3

Eligibility criteria………………………………………………………………………..3

Data extraction…………………………………………………………………………..4

Assessment of methodological quality………………………………………………….5

Search results……………………………………………………………………………5

Study characteristics…………………………………………………………………….6

Risk of bias……………………………………………………………………………...6

References to studies included in the multiple-treatments meta-analysis………………7

WinBUGS codes for random effects model and fixed effects model for binary outcome

data…………………………………………...................................................................19

WinBUGS codes for random effects meta-regression model for binary outcome

data……….......................................................................................................................22

Rankogram and SUCRA……………………………………………………………….24

Inconsistency…………………………………………………………………………...26

Cross validation………………………………………………………………………...26

Between-study heterogeneity…………………………………………………………..27

Publication bias…………………………………………………………………………28

References……………………………………………………………………………...29


Search strategy

This multiple treatment meta-analysis followed the Preferred Reporting Items for

Systematic Review and Meta analyses (PRISMA) statements (Supplementary PRISMA

Checklist).1 Using MEDLINE, the Web of Science, and the Cochrane Library, an

English literature search was carried out for randomized controlled trials (RCTs)

published from January 1990 to February 2013 that evaluated the clinical efficacy of

SPN, SEN, IMPN, and IMEN in human adult patients undergoing elective

gastrointestinal surgery. Also, bibliographic reviews and abstracts presented through

2013 were manually searched. The following text words and medical subject headings

(MeSH) terms were used for searching: “Enteral Nutrition” AND/OR “Parenteral

Nutrition” AND/OR “Immunomodulations” AND/OR “immunonutrition” AND/OR

“immuno-enhancing” AND/OR “Omega-3 Fatty Acids” AND/OR “Omega-6 Fatty

Acids” AND/OR “Arginine” AND/OR “Glutamine” AND/OR “Fish Oils” AND/OR

“RNA” AND/OR “Nutritional Support” combined with “Randomized Controlled Trial”

AND/OR “Gastrointestinal” AND/OR “Surgical Procedures” AND/OR “Perioperative

Period” AND/OR “Postoperative Period” AND/OR “Preoperative Period”. The

experimental designs taken into consideration were as follows: RCTs.

Eligibility criteria


SEN and IMEN were the perioperative delivery of any nutrient in solid or liquid form

(including usual food intake) that passed through any part of the digestive tract,

regardless of whether the patients received conventional oral diets with intravenous

fluids (standard care) or tube feeds. SPN was defined as administration of nutritional

liquids containing a minimum of glucose and amino acids that were perioperatively

administered through the central or peripheral venous system. IMPN was also defined

as administration of SPN with fish oil emulsions. If more than one version of the same

study was retrieved, the most recent study was used. Exclusion criteria are as follows:

(1) trials that investigated the efficacy of an oral nutritional supplement (sip feed); (2)

trials that evaluated the impact of nutrition only on nutritional or physiologic outcomes

(e.g., nitrogen balance or amino acid profile); (3) trials that treated patients receiving

home parenteral nutrition; (4) trials that included cardiopulmonary, head injury,

pediatric, gynecologic, urological, traumatic, emergency, transplantation surgery,

chemotherapy, radiotherapy, or critically ill patients.

Data extraction

Data were extracted on study design, setting, patient population, pathology of diseases,

site of surgery, the regimens, methods of nutritional support, and the outcome variables

listed above. Outcomes assessed were the incidence of any infection, overall


complication, mortality, wound infection, pneumonia, anastomotic leak, intra-abdominal

abscess, sepsis, and urinary tract infection for binary outcome data. Data were extracted

as the total number of patients affected by complications rather than the total number of

incidences of complications.

Assessment of methodological quality

Study quality was assessed using the Cochrane risk of bias tool, an established tool

based on the following domains: sequence generation for the randomization of subjects,

allocation concealment of treatment, blinding of participants, personnel, and outcome

assessors, incomplete outcome date, selective outcome reporting, and other sources of

bias—study design, early stopping, baseline imbalance, and some other problems. For

each study, the risk of bias was reported as “low risk”, “unclear risk”, or “high risk” in

the domains. Bias assessment was performed using Review Manager Version 5.2.3

(Cochrane Collaboration, UK).2

Search results

Seventy-four studies totaling 7,572 participants met all of the inclusion criteria; SPN

was compared with SEN in 29 studies; SPN was compared with IMPN in 18 studies;

SPN was compared with IMEN in 12 studies; SEN was compared with IMPN in 2


studies; SEN was compared with IMEN in 29 studies, and IMPN was compared with

IMEN in 2 studies (Supplementary Table 1 and Figure 2).

Study characteristics

Sixty studies stated the underlying pathology of the study participants, of which 48

studies (80%) were comprised of malignant status and the remaining 10 studies (20%)

included both malignant and benign status (Supplementary Table 1). No study included

only benign diseases. Twenty-two studies (30%) reported the number of patients with

malnutrition (Supplementary Table 1). Patients were fed through either a catheter tube

or orally with 21 kinds of EN (Supplementary Table 2); 5 kinds of IMEN and 15 kinds

of SEN (Supplementary Tables 3 and 4). Seven kinds of parenteral lipid emulsions were

administered; 3 kinds of IMPN, and 4 kinds of standard lipid emulsion (Supplementary

Tables 2 and 5). In fifty-seven studies, the nutrition was administered postoperatively;

preoperatively in 13 studies, and perioperatively in 12 studies (Supplementary Table 2).

Risk of bias

The risk of bias was adequate for 37 studies (50%) in the randomized sequence, clear

for 35 studies (47%) in the allocation concealment, adequate for 24 studies (32%) in the

double blinding, complete for 13 studies (18%) in the blinding of the outcome

assessment, low for 42 studies (57%) in the incomplete outcome data, low for 29 studies


(39%) in the selective reporting, and free of other bias for 22 studies (30%)

(Supplementary Figures 1A and 1B).

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41. Helminen H, Raitanen M, Kellosalo J. Immunonutrition in elective gastrointestinal

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44. Daly JM, Lieberman MD, Goldfine J, et al. Enteral nutrition with supplemental

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45. Daly JM, Weintraub FN, Shou J, et al. Enteral nutrition during multimodality

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46. Wachtler P, Hilger RA, König W, et al. Influence of a pre-operative enteral

supplement on functional activities of peripheral leukocytes from patients with


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47. Kenler AS, Swails WS, Driscoll DF, et al. Early enteral feeding in postsurgical

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48. Senkal M, Mumme A, Eickhoff U, et al. Early postoperative enteral

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49. McCarter MD, Gentilini OD, Gomez ME, et al. Preoperative oral supplement with

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50. Braga M, Gianotti L, Radaelli G, et al. Perioperative immunonutrition in patients

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52. Erdem NZ, Kulaçoĝlu İH, Temel NA, et al. Perioperative Oral Supplement

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53. Braga M, Gianotti L, Nespoli L, et al. Nutritional approach in malnourished

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54. Jiang XH, Li N, Zhu WM, et al. Effects of postoperative immune-enhancing enteral

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55. Farreras N, Artigas V, Cardona D, et al. Effect of early postoperative enteral

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WinBUGS code for random effects and fixed effects model for binary outcome

data

A hierarchical model with random effects was used to account for between-study

variance, in which data was formatted in a binomial likelihood with a logit link

function.3 The probability of an event in arm k reported in study i is denoted by pik. The

number of events r ik in arm k of study i has the following binomial likelihood,

rik B( pik , nik) where nik is the sample size. For the binomial likelihood, we model the

probabilities pik on the logit scale, logit ( pik )=μi+δik whereμi is a random parameter for

the baseline. Then, the random effects are distributed normallyδ ik N (μik , σk2), where μik


is a study-specific logarithm of the odds ratio and σ k2 is the between study variance. The

μik is expressed by dk (treatment effects) and d1 (reference treatment effect); that is,

μik=dk−d1. Prior distribution needs to be set for μi, sd (standard deviation), and dk:

μi N (0 , 0.0001), sd U (0 ,5), and dk N (0 , 0.0001).4 The estimated odds ratio (OR) of

a treatment c versus a treatment k is derived as: ¿ck=exp (dc−dk ), where d1=0 for the

treatment that has been denoted as the reference treatment. For a fixed effects model,

the between study variance (σ 2) is set to zero. WinBUGS codes were available at:

http://www.nicedsu.org.uk.

# Random effects model for multi-arm trials for binary outcome data

model{

for (i in 1:ns){

# adjustment for multi-arm trials is zero for control arm

w[i,1] <- 0

# treatment effect is zero for control arm

delta[i,1] <- 0

# vague priors for all trial baselines

mu[i] ~ dnorm(0,0.0001)

for (k in 1:na[i]) {

# binomial likelihood

r[i,k] ~ dbin(p[i,k],n[i,k])

# model for linear predictor

logit(p[i,k]) <- mu[i] + delta[i,k]

# expected value of the numerators

rhat[i,k] <- p[i,k] * n[i,k]

# deviance contribution

dev[i,k] <- 2 * (r[i,k] * (log(r[i,k])-log(rhat[i,k])) + (n[i,k]-r[i,k]) * (log(n[i,k]-r[i,k]) - log(n[i,k]-

rhat[i,k])))}

http://www.nicedsu.org.uk/


# summed residual deviance contribution for this trial

resdev[i] <- sum(dev[i,1:na[i]])


# trial-specific LOR distributions

delta[i,k] ~ dnorm(md[i,k],taud[i,k])

# mean of LOR distributions (with multi-arm trial correction)

md[i,k] <- d[t[i,k]] - d[t[i,1]] + sw[i,k]

# precision of LOR distributions (with multi-arm trial correction)

taud[i,k] <- tau *2*(k-1)/k

# adjustment for multi-arm RCTs

w[i,k] <- (delta[i,k] - d[t[i,k]] + d[t[i,1]])

# cumulative adjustment for multi-arm trials

sw[i,k] <- sum(w[i,1:k-1])/(k-1)}}

# total residual deviance

totresdev <- sum(resdev[])

# treatment effect is zero for reference treatment

d[1]<-0

# vague priors for treatment effects

for (k in 2:nt){

d[k] ~ dnorm(0,0.0001)}

# vague prior for between-trial SD

sd ~ dunif(0,5)

# between-trial precision = (1/between-trial variance)

tau <- pow(sd,-2)

# between-trial variance

var <- pow(sd,2)

# ranking on relative scale

for (k in 1:nt){

# assume events are "good"

rk[k]<-nt+1-rank(d[],k)

# assume events are "bad"

rk[k]<-rank(d[],k)

# calculate probability that treat k is best

best[k]<-equals(rk[k],1)}

# ranking of treatments

for (k in 1:nt) {


# when the outcome is positive - omit 'nt+1-'

order[k]<- rank(d[],k)

# when the outcome is negative

most.effective[k]<-equals(order[k],1)

for (j in 1:nt) {

effectiveness[k,j]<- equals(order[k],j)}}

for (k in 1:nt) {

for (j in 1:nt) {

cumeffectiveness[k,j]<- sum(effectiveness[k,1:j])}}

# SUCRAS

for (k in 1:nt) {

SUCRA[k]<- sum(cumeffectiveness[k,1:(nt-1)]) /(nt-1)}

# pairwise ORs and LORs for all possible pair-wise comparisons, if nt>2

for (c in 1:(nt-1)) {

for (k in (c+1):nt) {

or[c,k] <- exp(d[k] - d[c])

lor[c,k] <- (d[k]-d[c])}}}

# Fixed effects model for multi-arm trials for binary outcome data

model{

for(i in 1:ns){


mu[i] ~ dnorm(0,.0001)

for (k in 1:na[i]){




logit(p[i,k]) <- mu[i] + d[t[i,k]] - d[t[i,1]]



#Deviance contribution

dev[i,k] <- 2 * (r[i,k] * (log(r[i,k])-log(rhat[i,k])) + (n[i,k]-r[i,k]) * (log(n[i,k]-r[i,k]) - log(n[i,k]-

rhat[i,k])))}


resdev[i] <- sum(dev[i,1:na[i]])}

# Total residual deviance




d[1]<-0

WinBUGS code for random effects meta-regression model for binary outcome data

To assess the influence of covariates on the true efficacy of immunonutrition, a meta-

regression model was considered. The meta-regression analysis was performed

concerning the following 3 covariates: published year of articles, timing of the

administration of nutrition (preoperatively, postoperatively, or perioperatively), and

country of origin (Europe, North America, or Asia). The number of patients with

malnutrition was considered as a relevant covariate; but it could not be used due to the

reasons that only 34 studies out of the total of 74 (34%) reported the number of patients

with malnutrition. The main body of the WinBUGS code for the random effects meta-

regression model is similar to the random effects model; however, a subgroup and a

continuous covariate are incorporated into the model. In the case of a continuous

covariate, the analysis used centered covariate values (x[i]-mx). This is achieved by

subtracting the mean covariate value from each covariate.5 WinBUGS codes were

available at: http://www.nicedsu.org.uk.

# Random effects meta-regression model for multi-arm trials

model{

for(i in 1:ns){

# adjustment for multi-arm trials is zero for control arm

w[i,1] <- 0

# treatment effect is zero for control arm

http://www.nicedsu.org.uk/


delta[i,1] <- 0


mu[i] ~ dnorm(0,0.0001)





logit(p[i,k]) <- mu[i] + delta[i,k] + (beta[t[i,k]]-beta[t[i,1]]) * (x[i]-mx)



# deviance contribution

dev[i,k] <- 2 * (r[i,k] * (log(r[i,k])-log(rhat[i,k])) + (n[i,k]-r[i,k]) * (log(n[i,k]-r[i,k]) -log(n[i,k]-

rhat[i,k])))}


resdev[i] <- sum(dev[i,1:na[i]])


# trial-specific LOR distributions

delta[i,k] ~ dnorm(md[i,k],taud[i,k])

# mean of LOR distributions (with multi-arm trial correction) covariate effect relative to treat in arm 1

md[i,k] <- d[t[i,k]] - d[t[i,1]] + sw[i,k]

# precision of LOR distributions (with multi-arm trial correction)

taud[i,k] <- tau *2*(k-1)/k

# adjustment for multi-arm RCTs

w[i,k] <- (delta[i,k] - d[t[i,k]] + d[t[i,1]])

# cumulative adjustment for multi-arm trials

sw[i,k] <- sum(w[i,1:k-1])/(k-1)}}

# total residual deviance



d[1]<-0

# covariate effect is zero for reference treatment

beta[1] <- 0

for (k in 2:nt){

# vague priors for treatment effects

d[k] ~ dnorm(0,0.0001)

# common covariate effect


beta[k] <- B}

# vague prior for covariate effect

B ~ dnorm(0,0.0001)

# vague prior for between-trial standard deviation

sd ~ dunif(0,5)

# between-trial precision = (1/between-trial variance)

tau <- pow(sd,-2)

# pairwise ORs and LORs for all possible pair-wise comparisons

for (c in 1:(nt-1)){

for (k in (c+1):nt){

or[c,k] <- exp(d[k] - d[c])

lor[c,k] <- (d[k]-d[c])}}}

Rankogram and SUCRA plot

Bayesian posterior probabilities rank the treatments for each outcome according to the

estimated effect size; that, the proportion of each Markov chain Monte Carlo cycle in

which a given treatment is ranked first out of the total number of cycles gives the

probability that the treatment is ranked in the best. Rank probabilities (rankograms) are

plotted against possible rank for all treatments (Supplementary Figure 2). However,

rankograms are unlikely to provide a ranking measure when many treatments are

competing and the probabilities to achieve each one of the possible ranks are the same

among the treatments.6,7 Alternatively, a cumulative probability that the treatment is

among the top b=1 ,2 ,⋯ , a (anywhere between first and bth rank) may be useful. The

cumulative probability can be plotted against the possible rank. The larger the surface

under the cumulative probability ranking curve (SUCRA), the more probable are the


lowest rank—the more efficacious or acceptable treatment. If a treatment is always

ranks first, then SUCRA is 1, and if it always ranks last it is 0; that is SUCRA would be

1 when a treatment is certain to be the best and 0 when a treatment is certain to be the

worst (Supplementary Figure 3). 6 The order of treatment k in every Markov chain

Monte Carlo cycle is calculated as order k=∑b=1

a

I (db ≤ dk ) where I ( db≤ dk )=1 if db ≤ dk

and 0 otherwise. The probability of treatment k to be at the b order is estimated from

effectivenessk , b; and, the cumulative probability is derived from cumeffectivenessk ,b. The

SUCRA for treatment k is SUCR A k=∑b=1

a−1 cumeffectivenessk ,b

nt−1. 6

Inconsistency

A consistency between the direct and indirect evidence is an important assumption of

network meta-analysis.8,9 Consistency was assessed using the ifplot command

implemented in software package Stata Version 12.1 (Stata Corporation, College

Station, Texas)10. This command identified consistency within all first-order (triangles)

and second-order (quadrilaterals) closed loops in a network formed by multiple

treatments. Within each loop, the command defines the direct estimates and indirect

estimates for a randomly chosen contrast; then, the inconsistency factor (IF) is defined


as the difference between the direct and indirect estimates. The command presents the

absolute IF value and its CI (truncated to 0). Under the null hypothesis that there is no

inconsistency (H0: IF = 0; between-study variance, σ2), the approximate test can be

obtained asz= IFσ

N (0 ,1). A loop is defined as statistically inconsistent when |z| >

1.96. When the 95% CI seems to include 0, the direct estimate of the summary effect

does not differentiate from the indirect estimate. Inconsistency was not significantly

recognized in a total of 33 loops (Supplementary Table 7).

Cross validation

We used a leave-one-out cross validation (LOO-CV) technique5 for detection of an

outlier; the LOO-CV removes a single data set from the original data set, and then

compare the observed treatment effect from the original data set to the posterior

predictive distribution of effects expected from the remaining data set. The predictive

probability of each outcome in a future study pnew is given by

logit ( pnew )=logit ( pbase )+δ new, where pbase is the probability of reference treatment and

δ new is the predictive treatment effect in a future study. The predictive number of events

rnew in the treatment arm of a future study of the same size (n) as the omitted study can

be drawn from a binomial distribution, rnew Bin ( pnew , n). The rnew is compared with the


observed number of the omitted study (r ) to obtain a Bayesian p-value Pr (rnew>r ): the

probability obtains a value as extreme as that observed in the omitted study. Extra lines

of code were added to the random effects model. The LOO-CV showed that 4 studies

were outliers: the studies of Badia-Tahull31 (P = 0.02), Beier-Holgersen6 (P = 0.002),

and Daly44 (P = 0.02) for any infection; the studies of Beier-Holgersen6 (P = 0.02) and

Daly44 (P = 0.02) for overall complication; the studies of Beier-Holgersen6 (P = 0.006)

and Senkal51 (P = 0.003) for wound infection; the study of Daly44 (P = 0.008) for

pneumonia (Supplementary Table 8).

Between-study heterogeneity (σ 2 )

Heterogeneity is characterized as between-study variation within treatment contrasts—

the result of an uneven distribution of treatment effect modifiers. The presence of

between-study heterogeneity was assessed as estimates of the median between-study

variance (σ 2 ) for each comparison. The σ 2values of ≤ 0.04, 0.14, and 0.40 ≥ indicate

evidence of low, moderate, and high heterogeneity, respectively.11 The σ 2 estimates

were low for all outcomes, except for any infection, overall complication, mortality, and

sepsis with moderate σ 2. Supplementary Table 10 shows the value of median σ 2 with

95% Crl for all outcomes.

Publication bias


Publication biaswas assessed visually using a netfunnel command—a comparison-

adjusted funnel plot—implemented in software package Stata version 12.1(Stata

Corporation, College Station, Texas). In a pair-wise meta-analysis, a funnel plot is a

scatter plot of standard error vs. effect estimates for each study. Different treatment

comparisons have their own summary estimates in network meta-analysis; therefore,

there is not a common line of symmetry for all the studies. In the comparison-adjusted

funnel plot, the horizontal axis is each study's i observed logOR ( y iXY ) centered to the

comparison’s mean logOR obtained from the pairwise meta-analysis (μXY ); and, the

vertical axis is the inverted standard error of the effect sizes as used in a standard funnel

plot. If all studies appear symmetrically around the zero line, the comparison-adjusted

funnel plot suggests no evidence of small-study effects in the network.12This result is

shown in Supplementary Figure 4, which shows that there is no evidence of publication

bias for any outcome because we cannot be sure if it is present or not using these

methods.

References

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reviews and Meta-analysis: The PRISMA statement. PLOS Med. 2009; 6:

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2. Higgins JPT, Altman DG. Assessing risk of bias in included studies. In: Higgins

JPT, Green S, eds. Cochrane handbook for systematic reviews of interventions.

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meta-analysis: a comparative study. Stat Med. 1995;14:2685–2699.

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randomized controlled trials. Available at: http://www.nicedsu.org.uk/. Accessed

February 14, 2013.

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http://www.nicedsu.org.uk/. Accessed February 14, 2013.

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antimanic drugs in acute mania: a multiple-treatments meta-analysis. Lancet.

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8. Lu G, Ades AE. Assessing evidence inconsistency in mixed treatment comparisons.

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9. Salanti G. Indirect and mixed-treatment comparison, network, or multiple

treatments meta-analysis: many names, many benefits, many concerns for the next

generation evidence synthesis tool. Res Syn Meth. 2012;3:80–97.

10. Veroniki AA, Vasiliadis HS, Higgins JPT, et al. Evaluation of inconsistency in

networks of interventions. Int J Epidemiol. 2012;42:332–345.

11. Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian approaches to clinical trials and

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small-study effects in a network of interventions. Res Syn Meth. 2012;3:161–176.

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