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Cochrane Database of Systematic Reviews Protocol - Intervention

Probiotics for people with cystic fibrosis

Authors' declarations of interest

Version published: 12 February 2018 Version history

https://doi.org/10.1002/14651858.CD012949

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view the current version 22 January 2020
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Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To assess the efficacy and safety of probiotics for improving health outcomes in children and adults with CF.

Background

Description of the condition

Cystic fibrosis (CF) is a life‐limiting genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene affecting approximately 70,000 children and adults worldwide (CF Foundation 2017). It is a multisystem disease which results in thick secretions and predominantly affects the lungs, gastrointestinal tract, pancreas and liver. The growth and nutritional status of people with CF is of paramount importance as they are major determinants of lung function and therefore survival (Corey 1988; Jadin 2011). Currently only around half of people with CF achieve an adequate nutritional status (McCormick 2010; Turck 2016). In the era of newborn screening, and despite improvements in nutrition and pulmonary care, many children with CF fail to achieve catch‐up weight gain to a level comparable with their birth weight z‐score within two years of CF diagnosis (Jadin 2011; Shoff 2006).

Intestinal dysbiosis (i.e. microbial imbalance) is well‐documented in people with CF (Dhaliwal 2015; Duytschaever 2011; Madan 2012; Nielsen 2016; Rogers 2016; Schippa 2013) and emerging evidence suggests that it occurs within the first year of life and then progresses further from normal with increasing age compared to healthy controls (Nielsen 2016). By three years of age, the intestinal microbiota fully resembles that of an adult in terms of composition and diversity, and once established the adult microbiome is stable and relatively difficult to disturb (Faith 2013; Yatsunenko 2012). Currently there is a paucity of data about the developmental trajectory of intestinal microbiota in early CF years (Hoen 2015; Madan 2012). The gastrointestinal microbiota plays an important role in health and disease as it contributes to metabolic function and immunity (Quigley 2013). The alteration in gut microbiota in CF is hypothesised to be due to the dehydrated, acidic luminal environment and inspissated (thick) slow‐to‐clear mucus within the gut (Lee 2012). High‐caloric, high‐fat diets (Tomas 2016) and frequent antibiotic therapy (Duytschaever 2011) also contribute to this dysbiosis. Intestinal inflammation has been identified in people with CF and this may be due to the altered composition of the gut bacterial ecosystem (Dhaliwal 2015; Lee 2012; Smyth 2000; Werlin 2010). Faecal calprotectin, measured using commercially available ELISA kits, is commonly reported as a marker of intestinal inflammation in CF, with levels greater than 50 µg/g considered inflamed (Fallahi 2013; von Roon 2007). It has recently been observed that intestinal inflammation is inversely correlated with growth in children with CF (Dhaliwal 2015).

Compared to healthy controls, the intestinal milieu in CF (beyond bacterial communities) is altered with different metabolomic* (Kaakoush 2016) and proteomic profiles* (Debyser 2016). Furthermore, intestinal dysbiosis may also be associated with impaired innate (inherent) immunity in CF children (Ooi 2015a). Two recent longitudinal studies on the developing respiratory and intestinal microbiomes of infants and young children with CF suggested that:

  1. colonisation of the gut by potentially pathogenic bacteria (Escherichia and Enterococcus species (spp)) appeared to precede colonisation of the lungs; and

  2. increased gut microbiota diversity was associated with prolonged periods of health, with delays in time to initial Pseudomonas aeruginosa colonisation and first CF exacerbation (Hoen 2015; Madan 2012).

* Metabolomics is the study of small‐molecule metabolites which provides insights into cellular processes and metabolic pathways, whereas proteomics is the study of host and microbial proteins which provides insights into functional pathways.

Description of the intervention

Probiotics were first described in 1907 by Elie Metchnikoff (Metchnikoff 1908), and are now often defined as "live microorganisms which when consumed in adequate amounts confer a health benefit on the host" (FAO/WHO 2002). Probiotics have been commercially available since the 1930s and have been used both prophylactically and therapeutically to improve gut microbial profiles, as well as to improve mucosal, epithelial, intestinal and systemic immune activity.

Oral probiotic supplements are usually in the form of either a tablet, capsule, powder, yogurt, milk or food product. Several probiotic strains including Lactobacillus and Bifidobacterium spp are 'Generally Recognized as Safe' by the United States Food and Drug Administration (www.fda.gov). In many countries, probiotics are not regulated as drugs, but rather classified as food supplements which have different regulations. Probiotics commonly administered in clinical trials of CF include Lactabacillus spp, Bifidobacterium spp, Saccharomyces spp and Streptococcus spp. Knowledge regarding the optimal strains, dose and duration of therapy is still lacking for many conditions including CF.

How the intervention might work

The presence of intestinal dysbiosis and inflammation is well‐established in CF (Dhaliwal 2015; Kaakoush 2016; Nielsen 2016; Ooi 2015a; Pang 2015) and probiotics are hypothesised to correct these alterations by restoring the gut microbial profile towards 'normal'. A large and progressive number of metabolic, immune‐mediated and inflammatory diseases, such as obesity, atopic and allergic disorders, type I diabetes and inflammatory bowel disease have been linked with intestinal dysbiosis and probiotics have shown beneficial effects on the gastrointestinal environment in some of these diseases (Arrieta 2014). Probiotics have also been beneficial in managing Clostridium difficile infections (Goldenberg 2013) and antibiotic‐associated diarrhoea (Goldenberg 2015) as well as preventing acute upper respiratory tract infections (Hao 2015). These conditions commonly affect people with CF.

Although the exact mechanisms are unclear, probiotics promote benefits in CF by correcting dysbiosis, inhibiting potentially pathogenic bacteria, promoting beneficial metabolic pathways, improving intestinal motility and barrier function whilst also conferring positive local and systemic immune effects (Bruzzese 2014; del Campo 2014; Li 2014; Quigley 2013). Consequently, a reduction in intestinal inflammation has been observed in people with CF (Bruzzese 2014; del Campo 2014). Intestinal health and homeostasis also likely confers direct, indirect and systemic benefits on:

Why it is important to do this review

Intestinal inflammation, unlike airway inflammation, in CF has largely been understudied as a therapeutic target. Probiotics are commercially available over‐the‐counter and a survey from a CF clinic in the USA found that 60% of paediatric patients were self‐medicated with probiotics (Sullivan 2015). Furthermore, recent European consensus guidelines stated that further studies were required before a recommendation on probiotics could be made (Turck 2016). Given the prevalence and life‐long burden of CF disease, the potential benefit of probiotics on intestinal, respiratory and general health should be further explored and reviewed.

To date three systematic reviews on probiotics in children or adults (or both) with CF have been performed (Anathan 2016; Anderson 2017; Nikniaz 2017). This review will differ by only including randomised controlled trials (RCTs) and will evaluate both children and adults. A meta‐analysis on probiotics in children and adults with CF is yet to be performed.

Objectives

To assess the efficacy and safety of probiotics for improving health outcomes in children and adults with CF.

Methods

Criteria for considering studies for this review

Types of studies

We will include RCTs and quasi‐RCTs which assess efficacies of probiotics in children and adults with CF. Cross‐over RCTs will be included; however, only results from the first phase of each trial will be analysed if no 'washout' period is included in the trial design. A washout phase is designed to limit potential residual treatment effects and as there is no consensus on duration, we have not defined a minimum duration for the washout. Trials will not be excluded for failure to conceal treatment allocation or blind the outcome assessor.

Types of participants

All participants must fulfil consensus diagnostic criteria for CF (e.g. Farrell 2008). No restrictions for included participants will be placed on age, gender, genotype, pancreatic exocrine sufficiency status, disease severity, co‐morbidities, antibiotic use or CFTR modulator therapy.

Types of interventions

Any oral probiotic formulation (any strain(s), dose or formulation, with or without a prebiotic) compared to any other probiotic formulation, placebo or no treatment control.

Types of outcome measures

Primary outcomes

  1. Pulmonary exacerbation defined using consensus criteria e.g. Fuch's criteria (Fuchs 1994)

    1. number of pulmonary exacerbations

    2. duration of antibiotic therapy (any route) for pulmonary exacerbations (days)

  2. Inflammatory biomarkers (mean change from baseline and post‐treatment absolute mean)

    1. intestinal

      1. calprotectin (µg/g)

      2. M2 pyruvate kinase (U/mL)

      3. rectal nitric oxide (µmol/L)

    2. serum

      1. C‐reactive protein (mg/L)

      2. cytokines (pg/mL))

    3. sputum

      1. C‐reactive protein (mg/L)

      2. cytokines (pg/mL)

  3. Adverse events

    1. serious adverse reactions defined as any untoward medical occurrence which results in death, is life‐threatening, requires hospitalisation or prolongation of hospitalisation, or results in persistent or significant disability or incapacity (e.g. sepsis (bacteraemia))

    2. adverse reaction defined as any untoward medical occurrence with reasonable causal relationships (e.g. nausea and abdominal bloating)

    3. mortality (all causes)

Secondary outcomes

  1. Growth and nutrition (mean change from baseline and post‐treatment absolute mean)

    1. height (cm and z‐score)

    2. weight (kg and z‐score)

    3. body mass index (BMI) (kg/m² and z‐score)

  2. Lung function (mean change from baseline and post‐treatment absolute mean)

    1. forced expiratory volume in one second per cent predicted (FEV1%)

    2. forced expiratory volume (FEV) (L)

    3. lung clearance index (LCI)

  3. Hospitalisations (all causes)

    1. number

    2. duration (days)

  4. Health‐related quality of life (QoL) measured using a validated questionnaire (e.g. CFQ‐R (Quittner 2009))

  5. Gastrointestinal symptoms measured using a validated symptom score (e.g. JenAbdomen‐CF Score (Tabori 2017))

  6. Intestinal microbial profile assessed using next‐generation sequencing of stool samples

    1. alpha diversity (richness or Shannon index)

    2. beta diversity (Bray‐Curtis dissimilarity)

Search methods for identification of studies

There will be no restrictions regarding language or publication status.

Electronic searches

The Cochrane Cystic Fibrosis and Genetic Disorders Group's Information Specialist will conduct a systematic search of the Group's Cystic Fibrosis Trials Register for relevant trials using the following terms: probiotics.

The Cystic Fibrosis Trials Register is compiled from electronic searches of the Cochrane Central Register of Controlled Trials (CENTRAL) (updated each new issue of the Cochrane Library), weekly searches of MEDLINE, a search of Embase to 1995 and the prospective handsearching of two journals ‐ Pediatric Pulmonology and the Journal of Cystic Fibrosis. Unpublished work is identified by searching the abstract books of three major cystic fibrosis conferences: the International Cystic Fibrosis Conference; the European Cystic Fibrosis Conference and the North American Cystic Fibrosis Conference. For full details of all searching activities for the register, please see the relevant section of the Cochrane Cystic Fibrosis and Genetic Disorders Group's website.

In addition to the above, we will conduct a search of the following databases and trials registers:

For details of our search strategies, please see Appendix 1.

Searching other resources

We will add any resulting papers to the main search results for screening by two review authors. We will check the bibliographies of included trials and any relevant systematic reviews identified for further references to relevant trials. We will contact trial authors, experts and organisations in the field to obtain additional information on relevant trials and ongoing trials.

Data collection and analysis

We will employ the standard methods of the Cochrane Cystic Fibrosis and Genetic Disorders Group and of the Cochrane Handbook of Systematic Reviews (Higgins 2011a).

Selection of studies

Once the authors identify a complete list of references, one author will check for and remove duplicates and enter the list into the Covidence online software product (www.covidence.org/). Two review authors will independently assess abstracts and, if necessary, the full text of each trial to determine which trials satisfy the inclusion criteria. We will resolve discrepancies by discussion and consensus with a third review author. We will present the results in a flow diagram.

Data extraction and management

Two review authors (MC and MG) will independently extract data using a standard data extraction form in Covidence online software product (www.covidence.org/), piloting this on three articles. We will arrange for the translation of any trials reported in non‐English language before assessment. We will collect data on:

  • participant characteristics;

  • trial characteristics and design;

  • interventions and comparator;

  • outcome data ‐ reported separately for each outcome.

We will resolve discrepancies by discussion and consensus with a third review author. When data are incomplete, we will contact the primary investigator to request further information and clarification. When multiple publications from the one trial are identified, we will group reports together.

We will enter the extracted data into RevMan for analysis (Review Manager 2014). We plan to group outcome data into three‐monthly intervals for the first 12 months and then annually thereafter. If we identify multiple probiotic strains, we will perform a combined analysis (i.e. all probiotics versus placebo) followed by subgroup analyses at the genus level.

Assessment of risk of bias in included studies

To assess the risk of bias we will use the risk of bias tool described in the Cochrane Handbook of Systematic Reviews for Interventions (Higgins 2011b). Two review authors will independently assess the risk of bias for each included trial across the six domains:

(i) sequence generation;
(ii) allocation concealment;
(iii) blinding (self‐reported and objective);
(iv) incomplete outcome data;
(v) selective reporting; and
(vi) other potential sources of bias.

We will resolve discrepancies by discussion and consensus with a third review author.

If a trial describes the randomisation and allocation processes, including concealment from the researchers and at least two review authors deem these to be adequate, then we will consider the trial to have a low risk of bias. When these processes are inadequate or unclear, we will deem the trial as having a high risk of bias or unclear risk of bias, respectively. To assess blinding, we will look at who was blinded and the method used to determine the risk of bias. We will examine missing data, the distribution of missing data between groups and how the investigators managed withdrawals and loss to follow‐up. We will consider an intention‐to‐treat (ITT) analysis to be highly favourable in minimising the risk of bias. We will assess outcome reporting by reviewing the outcomes to be measured, either in the trial paper or a published protocol. If trial investigators measured relevant outcomes but did not report these, we will deem the trial of be at high risk of bias. We will search for any other potential sources of bias.

We will enter the data into individual trial and summary 'Risk of bias' tables. We will not exclude trials on the basis of risk of bias, but will perform a sensitivity analysis to explore the synthesis of evidence with variable quality.

Measures of treatment effect

For dichotomous outcomes, we will record the number of participants with the event and the number of participants analysed in each group. We will calculate a pooled estimate of the treatment effect for each outcome across trials using a risk ratio (RR) with 95% confidence intervals (CI), where appropriate. We will present absolute treatment effects with number needed to treat for an additional beneficial (NNTB) or harmful outcome (NNTH) for all outcome measures regardless of statistical significance.

For continuous outcomes, we will record the mean change and standard deviation (SD) from baseline for each group or mean post‐treatment or intervention values and SD or standard error (SE) for each group. We will calculate a pooled estimate of treatment effect for each outcome using the mean difference (MD) with 95% CI or standardised mean difference (SMD) with 95% CI depending on the variability of outcome measures.

Unit of analysis issues

The unit of analysis in a trial with a parallel group design will be the individual participant.

We will review any trial with a cross‐over design. If a 'washout' period is included in the trial design and investigators perform an appropriate paired analysis, then we will include the effect estimate of the intervention for each outcome in a meta‐analysis using the generic inverse‐variance method (Higgins 2011c). If a 'washout' period is not included or investigators do not analyse the data appropriately (i.e. paired analyses), we will include data from the first phase of the cross‐over and analyse these as if the trial had a parallel group design.

Dealing with missing data

If the reported trial data are insufficient or unclear for our purposes, we will contact the trial author(s) or sponsor(s) (or both) through written correspondence. We will request data and additional information to complete our assessment. We will assess whether investigators have performed an ITT analysis and report the number of participants missing from each trial arm where possible.

Assessment of heterogeneity

We will assess heterogeneity between trials using the Chi² and I² statistics and by visual inspection of the overlap in CIs on the forest plots (Higgins 2003). Regarding the Chi² test, a P value of less than 0.1 is of interest. Regarding the I² statistic, we will define our interpretation as in chapter 9 of the Cochrane Handbook for Systematic Reviews of Interventions (Deeks 2011):

  • 0% to 40%: might not be important;

  • 30% to 60%: may represent moderate heterogeneity;

  • 50% to 90%: may represent substantial heterogeneity;

  • 75% to 100%: considerable heterogeneity.

In the presence of heterogeneity, we will investigate possible sources of heterogeneity through subgroup and sensitivity analyses.

Assessment of reporting biases

We will minimise reporting bias from the non‐publication of trials or selective outcome reporting by using a broad search strategy, searching trial registries and contacting regulatory agencies. If at least 10 trials are identified, we will create a funnel plot to assess for publication bias (Sterne 2011). To assess for selective reporting, we will compare trial protocols (if available) with the reported outcomes in the trial publication. If a trial protocol is unavailable, we will compare the methods section and outcomes reported in the results section. We will record information on the sponsors and funding sources for trials and conflicts of interest of authors to assess for external bias.

Data synthesis

When possible we plan to combine trials in a meta‐analysis, we will generate forest plots using the Review Manager software (Review Manager 2014). If multiple probiotic strains are used in trials, then irrespective of the statistical heterogeneity, a random‐effects model will be used as it does not assume that all intervention effects are the same.

Subgroup analysis and investigation of heterogeneity

We will perform subgroup analyses to assess treatment effect for:

  • age (adults (18 years of age and over) versus children (up to 18 years of age)).

If we identify heterogeneity with a Chi² P value less than 0.1 or an I² statistic greater than 40%, we will investigate this by undertaking the following subgroup analyses:

  • probiotic strain(s) (grouped by genus): Lactobacillus, Bifidobacterium, Streptococcus, Saccharomyces, and combinations;

  • probiotic dose (colony forming units (CFU) per day): less than10⁸,10⁸ to 10⁹, over 10⁹ to 10ₑ⁰, over 10ₑ⁰ to 10ₑₑ, or over 10ₑₑ;

  • duration of washout included in cross‐over RCTs (weeks): up to two weeks, over two weeks and up to four weeks, over four weeks and up to six weeks, over six weeks and up to 12 weeks or over 12 weeks.

Sensitivity analysis

If we perform a meta‐analysis, we will conduct sensitivity analyses to assess for effect of the risk of bias by including or not including trials with an overall high risk of bias. We will also assess the inclusion or exclusion of cross‐over trials.

Summary of findings table

We will report a 'Summary of findings' table for each of the following outcomes:

  • number of pulmonary exacerbations;

  • change in intestinal calprotectin (µg/g);

  • adverse events;

  • change in weight (kg);

  • change in lung function (FEV1%);

  • number of hospitalisations;

  • health‐related QoL score(s).

We chose the outcomes based on relevance to clinicians and consumers. We will determine the overall quality of the evidence for each outcome using the GRADE approach (Schünemann 2011a). For each outcome we will report the population, setting, intervention, comparison, illustrative comparative risks, magnitude of effect (RR or MD or SMD), number of participants and trials, GRADE score and comments.