Probiotics for Preventing Necrotizing Enterocolitis in Preterm Infants: A Network Meta-Analysis

Background: Recent evidence supports a role of probiotics in preventing necrotizing enterocolitis (NEC) in preterm infants. Methods: A systematic review and network meta-analysis of randomized controlled trials (RCTs) on the role of probiotics in preventing NEC in preterm infants, focusing on the differential effect of type of feeding, was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A random-effects model was used; a subgroup analysis on exclusively human milk (HM)-fed infants vs. infants receiving formula (alone or with HM) was performed. Results: Fifty-one trials were included (10,664 infants, 29 probiotic interventions); 31 studies (19 different probiotic regimens) were suitable for subgroup analysis according to feeding. In the overall analysis, Lactobacillus acidophilus LB revealed the most promising effect for reducing NEC risk (odds ratio (OR), 0.03; 95% credible intervals (CrIs), 0.00–0.21). The subgroup analysis showed that Bifidobacterium lactis Bb-12/B94 was associated with a reduced risk of NEC stage ≥2 in both feeding type populations, with a discrepancy in the relative effect size in favour of exclusively HM-fed infants (OR 0.04; 95% CrIs <0.01–0.49 vs. OR 0.32; 95% CrIs 0.10–0.36). Conclusions: B. lactis Bb-12/B94 could reduce NEC risk with a different size effect according to feeding type. Of note, most probiotic strains are evaluated in few trials and relatively small populations, and outcome data according to feeding type are not available for all RCTs. Further trials are needed to confirm the present findings.


1) Supplemental Section A 2) Supplemental Section
15) Supplemental Table 2 16) Supplemental Table 3 17) Supplemental Table 4 INTRODUCTION Rationale 3 Describe the rationale for the review in the context of what is already known, including mention of why a network meta-analysis has been conducted.

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Objectives 4 Provide an explicit statement of questions being addressed, with reference to participants, interventions, comparisons, outcomes, and study design (PICOS).

Protocol and registration 5
Indicate whether a review protocol exists and if and where it can be accessed (e.g., Web address); and, if available, provide registration information, including registration number.

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Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale. Clearly describe eligible treatments included in the treatment network, and note whether any have been clustered or merged into the same node (with justification).

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Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.

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Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.

Appendix B
Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

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Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for pp. 2-3 obtaining and confirming data from investigators.

Data items 11
List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

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Geometry of the network S1 Describe methods used to explore the geometry of the treatment network under study and potential biases related to it. This should include how the evidence base has been graphically summarized for presentation, and what characteristics were compiled and used to describe the evidence base to readers.

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Risk of bias within individual studies 12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

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Describe the statistical methods used to evaluate the agreement of direct and indirect evidence in the treatment network(s) studied. Describe efforts taken to address its presence when found.

Risk of bias across studies 15
Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).

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Additional analyses 16 Describe methods of additional analyses if done, indicating which were pre-specified. This may include, but not be limited to, the following: • Sensitivity or subgroup analyses; • Meta-regression analyses; • Alternative formulations of the treatment network; and • Use of alternative prior distributions for Bayesian analyses (if applicable).

Study selection 17
Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

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Presentation of network structure

S3
Provide a network graph of the included studies to enable visualization of the geometry of the treatment network.

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Provide a brief overview of characteristics of the treatment network. This may include commentary on the abundance of trials and randomized patients for the different interventions and pairwise comparisons in the network, gaps of evidence in the treatment network, and potential biases reflected by the network structure.

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Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations. Table 1 Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment.

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Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: 1) simple summary data for each intervention group, and 2) effect estimates and confidence intervals.
Modified approaches may be needed to deal with information from larger networks. Table 1 Synthesis of results

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Present results of each meta-analysis done, including confidence/credible intervals. In larger networks, authors may focus on comparisons versus a particular comparator (e.g. placebo or standard care), with full findings presented in an appendix. League tables and forest plots may be considered to summarize pairwise comparisons. If additional summary measures were explored (such as treatment rankings), these should also be presented.

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Describe results from investigations of inconsistency. This may include such information as measures of model fit to compare consistency and inconsistency models, P values from statistical tests, or summary of inconsistency estimates from different parts of the treatment network.

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Risk of bias across studies

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Present results of any assessment of risk of bias across studies for the evidence base being studied.

Supplemental Figure1
Results of additional analyses

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Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression analyses, alternative network geometries studied, alternative choice of prior distributions for Bayesian analyses, and so forth).

Summary of evidence 24
Summarize the main findings, including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policymakers).

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Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review level (e.g., incomplete retrieval of identified research, reporting bias). Comment on the validity of the assumptions, such as transitivity and consistency. Comment on any concerns regarding network geometry (e.g., avoidance of certain comparisons).

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Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research.

Funding 27
Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review. This should also include information regarding whether funding has been received from manufacturers of treatments in the network and/or whether some of the authors are content experts with professional conflicts of interest that could affect use of treatments in the network.

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Supplemental Section B Search strategy  Supplemental Figure 4. Matrix of all treatment comparison estimates, presented as posterior medians of odds ratios from the network meta-analysis with 95% credible intervals. The upper triangle is not displayed to avoid redundancy.

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* 97.5% of the posterior distribution is below one (lower risk than the reference). † 97.5% of the posterior distribution is above one (higher risk than the reference). Supplemental Figure 10. Bar chart of SUCRA scores (surface under the cumulative ranking) resulting from secondary network meta-analysis conducted on trials that assessed multi-genera (MG) treatments and provided outcome according to infant feeding. Supplemental Figure 11. Classic pair-wise forest plot showing the association between the use of single probiotics treatment included in the MG group and NEC all stages in 8 studies reporting data for exclusively human milk-fed preterm infants. The study by Gomez-Rodriguez et colleagues is a head-to-head comparison between the multi genus probiotic above and the single strain L. acidophilus LB Supplemental  Table 4. Results from frequentist network meta-analysis based on electrical network theory. P-scores measure the certainty that one treatment is better than another treatment, averaged over all competing treatments, and are equivalent to the posterior means of SUCRA scores from Bayesian network meta-analysis.