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Antibiotic prophylaxis versus placebo or no intervention for people with cirrhosis and variceal bleeding

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Abstract

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

To assess the benefits and harms of antibiotic prophylaxis in people with cirrhosis and variceal bleeding.

Background

Description of the condition

Liver cirrhosis is the advanced stage of liver fibrosis, and histologically, it is defined as the presence of regenerative nodules surrounded by extensive fibrosis (scarring of the liver) (Schuppan 2009; Moon 2016). Even though the definition of liver cirrhosis is histological and the gold standard for diagnosing liver cirrhosis is liver biopsy, most of the time diagnosis is made only from clinical history, clinical features, and laboratory abnormalities. The absence of standardised criteria for diagnosing liver cirrhosis when liver biopsy is not performed, often leads to an unclear boundary between liver disease, liver fibrosis, and liver cirrhosis (Ratib 2017).

In 2001, cirrhosis was the 14th cause of death in the world and the 10th in low‐income countries (Sarin 2012). In most low‐income countries and in some high‐income countries, such as the UK, the risk of death related to cirrhosis has increased not only because of increased alcohol consumption and obesity, but also because of previous underreported mortality (Ratib 2017). A systematic analysis with data from 187 countries reported an increase in deaths due to liver cirrhosis from 676,000 in 1980 to over 1 million in 2010 (2% of all deaths) (Mokdadd 2014). In the USA, the estimated annual direct healthcare costs amounted up to $2.5 billion ($2500 million), while the annual indirect costs related to loss of work productivity and reduction in health‐related quality of life were $10.6 billion ($10,600 million) (Neff 2011).

Liver cirrhosis is commonly recognised as resulting from either 1) a result of toxic, infectious, immunopathological, or vascular insult that chronically damages the liver, or 2) a result of the accumulation of substances due to an inborn error of metabolism (i.e. iron in haemochromatosis, copper in Wilson's disease, or some inborn errors of metabolism of carbohydrates, amino acids, organic acids, and others) (Wiegand 2013). The most common specific causes of liver cirrhosis are chronic hepatitis B infection, alcohol, chronic hepatitis C infection, and non‐alcoholic fatty liver disease (Hsiang 2015).

One third of people with liver cirrhosis will remain asymptomatic (Scaglione 2015). Of those presenting with symptoms, non‐specific and general manifestations, such as anorexia, fatigue, weight loss, and muscle wasting are the most common. The most reported clinical features are jaundice (yellow discolouration of skin and eyes), ascites (accumulation of fluid in the abdominal cavity), spider angioma (arterioles with tiny radiating vessels visible in the skin), irregular and hard liver on palpation, enlarged spleen, caput medusae (prominent veins radiating from umbilicus), Cruveilhier Baumgarten syndrome (epigastric vascular murmur), reddening of the palms that spares the central portion, white bands on nails, Dupuytren’s contracture (fibrosis and contraction of the palms), gynaecomastia (proliferation of glandular male breast tissue), and loss of male hair pattern (Schuppan 2009).

Model for End Stage Liver Disease (MELD) score and the Child‐Pugh score are among the most‐used scoring systems for prognosis of liver cirrhosis. MELD score uses bilirubin, creatinine, international normalised ratio, and sodium levels to predict short‐term mortality in people with liver cirrhosis and is used for people on the waiting list for liver transplantation (Wiesner 2003; Kim 2008). Child‐Pugh score uses presence of ascites, presence of encephalopathy, and serum bilirubin, albumin and international normalised ratio levels, and was originally designed to predict mortality during surgery, but it is now used to assess severity and predict mortality for longer periods than MELD score (one and two years) (Child 1964). Fibrosis can also be classified histologically, using Laennec staging system with five grades: F0 (no fibrosis) to F4 (advanced fibrosis), depending on the amount of fibrotic tissue, and with a sub‐classification that divides stage 4 in A to C, depending on the width of fibrous septa and the amount of nodules detected on the tissue sample (Bedossa 1996; Kim 2011).

Liver cirrhosis increases resistance through the hepatic sinusoids, causing an increase in portal blood flow which leads to endothelial dysfunction with imbalance of vasodilator and vasoconstrictor factors, and a distortion of the hepatic vascular bed (Iwakiri 2012). Vasodilator factors and changes in hepatic circulation lead to arterial peripheric vasodilatation, mainly in the splanchnic area that contributes to a hyperdynamic state with decreased effective blood volume (Hilzenrat 2012). The presence of clinically significant portal hypertension (portal pressure gradient ≥ 10 mmHg) promotes the formation of collateral portosystemic circulation, portal hypertensive gastropathy, gastric varices, and oesophageal varices (Bosch 2008).

Variceal development and progression depends on the severity of portal hypertension and severity of liver disease (Garcia‐Tsao 2007). Kovolak 2007 reported the presence of varices in 42% of patients with Child‐Pugh class A and in 71.9% of patients with Child‐Pugh B or C cirrhosis.

Variceal haemorrhage is the consequence of variceal rupture, which is explained by an excessive wall tension. Wall tension in oesophageal and gastro‐oesophageal varices is determined mainly by three factors: 1) increased portal pressure, 2) increased size of the vessel, and 3) decreased wall thickness (Laplace’s law). Therefore, the associated risk factors for developing variceal bleeding are portal pressure, the size of the vessel, and the endoscopic characteristics of the vessel that translate into thinner walls (red marks) (Hilzenrat 2012).

Upper gastrointestinal bleeding comprises haemorrhage from the oesophagus to the ligament of Treitz, recently redefined as a bleeding source that is potentially visualised by upper endoscopy (Kim 2014). Upper gastrointestinal bleeding remains one of the most severe decompensating events in people with cirrhosis, and the second most frequent severe decompensating event after ascites (EASL 2018). Variceal haemorrhage causes 70% of the upper gastrointestinal bleeding events in people with ascites and cirrhosis (Mallet 2017). Risks of death within six weeks due to variceal haemorrhage ranges from 15% to 25%, and is particularly high when associated with acute kidney injury or infections (D'Amico 2014; EASL 2018).

People with upper gastrointestinal bleeding are at high risk of developing bacterial infections (Garcia‐Tsao 2017). Up to 50% of active upper gastrointestinal bleeding events are related to the presence of bacterial infections, and infections are considered an independent predictor of failure to control bleeding and mortality (Goulis 1998).

The association between infection and variceal bleeding in people with cirrhosis is reciprocal and its implications have been extensively studied (Bernard 1995; Goulis 1998; Augustin 2009). It is not always possible to define if a bleeding event leads to the development of infection or vice versa (Lee 2014). If variceal bleeding leads to infection, then it could be due to instrumental procedures, endoscopic procedures, bacterial translocation, or placement of catheters (Blaise 1994). When infection is present, release of toxins occur leading to an activation of mediators that damage vessels and enhance the haemostatic impairment present in most people with cirrhosis, which might lead to failure to control a bleeding haemorrhage and re‐bleeding events (Lee 2014).

The most common bacterial infections developed in people with cirrhosis are caused by Gram‐negative bacteria, producing spontaneous bacterial peritonitis (25%), urinary tract infections (20%), pneumonia (15%), and bacteraemia (12%) (Fernandez 2002).

Description of the intervention

The prophylactic use of oral or intravenous antibiotics for people with cirrhosis and gastrointestinal bleeding has been recommended in consensus guidelines (Tipathi 2015; Garcia‐Tsao 2017; EASL 2018). The recommended drugs are mainly intravenous cephalosporins (ceftriaxone 1 g every 24 hours for seven days) or oral quinolones (norfloxacin 400 mg every 12 hours for seven days) (Garcia‐Tsao 2009). Glycopeptides, aminoglycosides, macrolides, and rifaximine have also been administered (Rimola 1985; Altraif 2011; Huang 2018).

Cephalosporins (e.g. cefalexin, cefalotin, ceftriaxone, cefotaxime, cefazolin, cefixime, cefepime, ceftolozane) are part of the beta‐lactam family, and the mechanism of action is to disrupt synthesis of the bacterial wall (Török 2009). There are five generations of cephalosporins: the first generation (e.g. cefalexin, cefazolin, cefalotin ) covers mostly gram‐positive bacteria, the second generation (e.g. cefaclor, cefuroxime, and cefotetan) has less activity for gram‐positive but greater activity for gram‐negative than the first generation, and the third generation (e.g. cefotaxime, ceftriaxone, cefixime) works mostly against gram‐negative bacteria (Marshall 1999). The fourth and fifth generation of cephalosporins (e.g. cefepime, ceftaroline, ceftolozane) are considered broad spectrum antibiotics, acting against gram‐negative and gram‐positive bacteria. In addition, some of them have antipseudomonal activity (Török 2009). Cephalosporins can be administered orally or intravenously. A large retrospective study with data from 352,661 people who had received cephalosporins reported rash as the most common adverse event, followed by anaphylactoid reaction (Shi 2013).

Quinolones (e.g. norfloxacin, ciprofloxacin, levofloxacin, ofloxacin) prevent bacterial replication by preventing DNA winding and duplication (Aldred 2014). Quinolones, available for both oral or intravenous administration, work against both gram‐negative and gram‐positive bacteria. Most common adverse effects are mild gastrointestinal symptoms, but other adverse effects are described, mainly involving the musculoskeletal system (specifically risk of tendon rupture) and arrhythmia conditions such as QT prolongation (Norrby 1991). Nevertheless, serious adverse events have been uncommonly reported, and quinolones are considerably cheaper than cephalosporins (Xu 2011).

Glycopeptides (e.g. vancomycin, teicoplanin, ramoplanin) have a similar mechanism of action as the beta‐lactam antibiotics, inhibiting bacterial wall synthesis of gram‐positive bacteria, but their use is limited to infections caused by beta‐lactam resistant bacteria because of the great risk of toxicity affecting kidneys and inner ears, and the high risk of hypersensitivity reactions (Török 2009). Glycopeptides were designed only for intravenous administration, but in recent years, oral administration was approved for the treatment of clostridium difficile‐associated diarrhoea (FDA 2018).

Aminoglycosides (e.g. streptomycin, gentamicin, neomycin) inhibit protein synthesis of gram‐negative bacteria (Mingeot‐Leclercq 1999). The associated adverse effects are inner ear toxicity and renal toxicity. Aminoglycosides can be administrated intravenously or orally (Mingeot‐Leclercq 1999).

Macrolides (e.g. erythromycin, clarithromycin, azithromycin) inhibit growth of bacteria by affecting protein synthesis (Hamilton‐Miller 1973). The main adverse effects are arrhythmias, reversible deafness, and allergic reactions (Periti 1993; Jespersen 2006; Winkel 2011; Winkel 2015; Mosholder 2018). Erythromycin is known to have a prokinetic effect, and, therefore, it is used in states of gastrointestinal hypomotility (Peeters 1993). Macrolides can be administrated intravenously or orally (Periti 1993).

Rifaximine is a macrocyclic drug derived from rifamycin (Yu 2013). Rifaximine interferes with bacterial replication binding to RNA polymerase and interfering in bacterial DNA transcription (Yu 2013). Because of its poor oral absorption, it is used for gastrointestinal infections (Yu 2013). The lack of systemic absorption diminishes systemic adverse effects. Recently, some studies report that rifaximin inhibits the activation of NF‐kB and reduces the expression of pro‐inflammatory cytokines, downregulating inflammatory response (Ponziani 2017).

How the intervention might work

The different antibiotics, due to their mechanisms of action, are expected to kill or assist in killing the bacteria in people with weakened immune system due to liver cirrhosis. This could have beneficial effects on the course of the infection and, more importantly, on the patients, and it could even assist in stopping the upper gastrointestinal bleeding.

Why it is important to do this review

If people with cirrhosis and variceal bleeding do not receive antibiotic prophylaxis, then 30% to 48% of them will develop a bacterial infection (Bernard 1995; Moon 2016; Mallet 2017). Prophylactic use of antibiotics during an episode of upper gastrointestinal bleeding in people with cirrhosis is considered the standard of care, and it is encouraged in European, American, and UK guidelines (Tipathi 2015; Garcia‐Tsao 2017; EASL 2018). However, these recommendations are based on evidence from a meta‐analysis by Bernard and colleagues, including only five randomised clinical trials, with few patients and few events (Bernard 1999).

The first Cochrane Review on antibiotic prophylaxis in people with cirrhosis was published in 2002, and it included eight trials comparing antibiotics versus placebo or no antibiotic in 864 randomised participants (Soares‐Weiser 2002). Clinically significant beneficial effects were found on mortality (risk ratio (RR) 0.73, 95% confidence interval (CI) 0.55 to 0.95), and in the incidence of bacterial infections (RR 0.40, 95% CI 0.32 to 0.51). The review was updated in 2010, and included 12 trials with 1241 participants (Chavez‐Tapia 2010). The updated review confirmed a decrease in all‐cause‐mortality (RR 0.79, 95% CI 0.63 to 0.98) and bacterial infections (RR 0.36, 95% CI 0.27 to 0.49), and also found a decrease in the proportion of re‐bleeding events (RR 0.53, 95% CI 0.38 to 0.74). No serious adverse events were reported. However, all the included trials were at high risk of bias and uncertainty was observed regarding missing data. Also, the cumulative Z curve did not reach the required information size and the boundaries for benefit, futility, or harm were not crossed in Trial Sequential Analysis (Chavez‐Tapia 2010).

A number of randomised clinical trials have been published since the last search for trials in June 2010 (Chavez‐Tapia 2010), and the additional data need to be implemented in this review update, for which we will also follow the most recent Cochrane methodology. As the review by Chavez‐Tapia 2010 encompasses two comparisons, the current review will only assess antibiotic prophylaxis versus no intervention or placebo in people with cirrhosis and variceal bleeding. This is why we will publish it as a separate, new review. We will also prepare another separate review with the remaining comparisons, in the Chavez‐Tapia 2010 review, on different types of antibiotics for prophylaxis in people with cirrhosis and variceal bleeding (Sanchez‐Jimenez 2018).

Objectives

To assess the benefits and harms of antibiotic prophylaxis in people with cirrhosis and variceal bleeding.

Methods

Criteria for considering studies for this review

Types of studies

Randomised clinical trials, irrespective of publication type, publication status, publication date, and language, assessing the benefits and harms of antibiotic prophylaxis in people with cirrhosis and variceal bleeding.

During the selection of trials, if we identify observational studies (e.g. quasi‐randomised studies, cohort studies, or patient reports) that report adverse events caused by or associated with antibiotic prophylaxis, we will include these studies for a review of the reported adverse events. We will not specifically search for observational studies for inclusion in this review, which will be a limitation. We are aware that by not searching for all observational studies on harms, we run the risks of putting more weight on potential benefits than on potential harms, and of overlooking rare and late adverse events (Storebø 2018).

Types of participants

Adults with cirrhosis and variceal bleeding (as diagnosed by trialists) regardless of sex, age, aetiology, severity of the underlying liver disease (defined by Child‐Pugh score), or presence of identified precipitating factors. We will exclude people with acute liver failure and people with non‐cirrhotic portal hypertension.

Types of interventions

Experimental intervention

  • Any antibiotic used.

Control intervention

  • Placebo or no intervention.

The antibiotics could have been administered at any dose, mode of administration, and duration.

We will allow co‐interventions if they were meant to be administered equally to all comparison groups.

Types of outcome measures

Primary outcomes

  • All‐cause mortality.

  • Proportion of trial participants with one or more serious adverse events. Serious adverse events will be defined as any untoward medical occurrence that led to death; was life‐threatening; required hospitalisation or prolongation of hospitalisation; or resulted in persistent or significant disability; or was a congenital anomaly/birth defect; or any important medical event that might have jeopardised the person (ICH‐GCP 1997). However, we will use the definitions of study authors for serious adverse events, if defined.

  • Health‐related quality of life measured by any validated scale, be it disease‐specific (CDLQ, LDQOL) or not ( EQ‐5D‐5L) (Younossi 1999; Gralnek 2000; EuroQol 2018).

Secondary outcomes

  • A composite of infection‐related mortality (as defined by trialists) or bleeding‐related mortality (defined as death associated to failure to control bleeding or re‐bleeding (see Baveno VI definition) (De Franchis 2015).

  • Early re‐bleeding, defined as new bleeding episode within the acute period (five days) including clinically significant re‐bleeding: haematemesis (vomiting of blood) or melaena (black tarry stools), > 100 mL of fresh blood or 3 g drop in haemoglobin (9 % drop in haematocrit) (see Baveno VI definition) (De Franchis 2015).

  • Proportion of participants with one or more major infections (as defined by trialists).

  • Non‐serious adverse events (all adverse events that did not fulfil the criteria listed under serious adverse events). We will also use the definitions of study authors for non‐serious adverse events, if defined. We will measure the proportion of people with one or more non‐serious adverse events.

Exploratory outcomes

  • Individual serious adverse events or complications (e.g. hepatorenal syndrome, hepatic coma).

  • Individual non‐serious adverse events.

We will assess all outcomes at the maximum duration of follow‐up (Cochrane Hepato‐Biliary Group Module).

Search methods for identification of studies

Electronic searches

We will search The Cochrane Hepato‐Biliary Group Controlled Trials Register (Cochrane Hepato‐Biliary Group Module), Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library, MEDLINE Ovid, Embase Ovid, LILACS (Bireme), Science Citation Index Expanded (Web of Science), and Conference Proceedings Citation Index ‐ Science (Web of Science) (Royle 2003). We will apply no language or document type restrictions. Appendix 1 shows the preliminary search strategies with the expected time spans of the searches.

Searching other resources

We will identify additional references by manually searching the references of articles from the computerised databases. We will also search on‐line trial registries such as ClinicalTrials.gov (clinicaltrials.gov), the European Medicines Agency (EMA) (www.ema.europa.eu/ema), the World Health Organization (WHO) International Clinical Trials Registry Platform (www.who.int/ictrp), and the Food and Drug Administration (FDA) (www.fda.gov) for ongoing or unpublished trials. We will contact experts in the field and pharmaceutical companies to enquire about additional trials.

Data collection and analysis

We will use the software package RevMan 5 (Review Manager 2014), and Trial Sequential Analysis (Thorlund 2011; TSA 2011; Wetterslev 2017). We will use a pre‐piloted data extraction form created in Excel and present a table describing the types of serious adverse events reported in each trial. We will also tabulate the types of adverse events (serious and non‐serious) that are reported in the non‐randomised studies retrieved with the searches for the randomised trials.

Selection of studies

Two review authors (BS and NC), working independently, will read the electronic search output, perform additional manual searches, and list potentially eligible trials. Two review authors (BS and NC) will read the potentially eligible trials and participate in the final selection of trials for inclusion. For trials described in more than one publication, we will select the paper with the longest duration of follow‐up as our primary reference. We will list details of all the included studies in the Characteristics of included studies table, and all the excluded trials with the reasons for their exclusion in the Characteristics of excluded studies table.

Data extraction and management

Two review authors (BS, NC) will independently extract data. We will request for missing information of published trials by writing to the correspondent author.

We will gather the following data from each study.

Characteristics of trials

  • Date, location, and setting of trial

  • Publication status

  • Case definitions used (clinical, serological, bacteriological)

  • Sponsor of trial (known or unknown; industry or not industry)

  • Inclusion and exclusion criteria

Characteristics of participants

  • Number of participants recruited

  • Age

  • Sex

  • Severity of liver disease and cirrhosis according to the aetiology of liver disease, regardless of the criteria used

Characteristics of interventions

  • Type of antibiotic, dose, mode of administration, schedule, length of follow‐up (in days)

  • Number of days that antibiotic prophylaxis was provided.

  • Length of follow‐up

Outcome data

  • Number of participants randomised

  • Number of participants included for the analysis

  • Number of participants with events for binary outcomes, mean and standard deviation for continuous outcomes, number of events for count outcomes

  • Definition of outcomes or scale used if appropriate

  • Risk of bias.

Assessment of risk of bias in included studies

Two review authors (BS, NC) will independently assess the risk of bias in the included studies. We will assess risk of bias according to the Cochrane 'Risk of bias' tool (Higgins 2011), the Cochrane Hepato‐Biliary Group Module (Cochrane Hepato‐Biliary Group Module), and methodological studies (Schulz 1995; Moher 1998; Kjaergard 2001; Wood 2008; Savović 2012a; Savović 2012b; Lundh 2017; Savović 2018), using the following sources of bias, defined as follows.

Allocation sequence generation

  • Low risk of bias: sequence generation was achieved using computer random number generation or a random number table. Drawing lots, tossing a coin, shuffling cards, and throwing dice were adequate if performed by an independent person not otherwise involved in the trial.

  • Uncertain risk of bias: the method of sequence generation was not specified.

  • High risk of bias: the sequence generation method was not random.

Allocation concealment

  • Low risk of bias: the participant allocations could not have been foreseen in advance of, or during enrolment. Allocation was controlled by a central and independent randomisation unit; or the allocation sequence was unknown to the investigators (for example, if the allocation sequence was hidden in sequentially‐numbered, opaque, and sealed envelopes).

  • Uncertain risk of bias: the method used to conceal the allocation was not described so that intervention allocations may have been foreseen in advance of, or during enrolment.

  • High risk of bias: the allocation sequence was likely to be known to the investigators who assigned the participants.

Blinding of participants and personnel

  • Low risk of bias: any of the following: blinding of participants and key study personnel ensured, and it was unlikely that the blinding could have been broken; or (rarely) no blinding or incomplete blinding, but the review authors judged that the outcome was not likely to be influenced by lack of blinding, such as all‐cause mortality or serious adverse events.

  • Unclear risk of bias: any of the following: insufficient information to permit judgement of 'low risk' or 'high risk'; or the trial did not address this outcome.

  • High risk of bias: any of the following: no blinding or incomplete blinding, and the outcome was likely to be influenced by lack of blinding; or blinding of key study participants and personnel attempted, but likely that the blinding could have been broken, and the outcome was likely to be influenced by lack of blinding.

Blinded outcome assessment

  • Low risk of bias: any of the following: blinding of outcome assessment ensured, and unlikely that the blinding could have been broken; or (rarely) no blinding of outcome assessment, but the review authors judged that the outcome measurement was not likely to be influenced by lack of blinding, such as all‐cause mortality or serious adverse events.

  • Unclear risk of bias: any of the following: insufficient information to permit judgement of 'low risk' or 'high risk'; or the trial did not address this outcome.

  • High risk of bias: any of the following: no blinding of outcome assessment, and the outcome measurement was likely to be influenced by lack of blinding; or blinding of outcome assessment, but likely that the blinding could have been broken, and the outcome measurement was likely to be influenced by lack of blinding.

Incomplete outcome data

  • Low risk of bias: missing data were unlikely to make treatment effects depart from plausible values. The study used sufficient methods, such as multiple imputation, to handle missing data.

  • Unclear risk of bias: there was insufficient information to assess whether missing data in combination with the method used to handle missing data were likely to induce bias in the results.

  • High risk of bias: the results were likely to be biased due to missing data.

Selective outcome reporting

  • Low risk of bias: the trial reported at least the following outcomes: all‐cause mortality and serious adverse events. If the original trial protocol was available, the outcomes should have been those set out in that protocol. If we obtained the trial protocol from a trial registry (e.g. www.ClinicalTrials.gov), the outcomes sought should have been those stated in the original protocol if the trial protocol was registered before or at the time that the trial was begun. If the trial protocol was registered after the trial was begun, we would not consider those outcomes to be reliable.

  • Unclear risk of bias: not all predefined or clinically relevant and reasonably expected outcomes were reported fully, or it was unclear whether data on these outcomes were recorded or not.

  • High risk of bias: one or more predefined or clinically relevant and reasonably expected outcomes were not reported, despite the fact that data on these outcomes should have been available and even recorded.

For‐profit bias

  • Low risk of bias: the trial appeared free of industry sponsorship or other type of for‐profit support that could manipulate the trial design, conductance, or results (industry‐sponsored trials overestimate the efficacy by about 25%) (Lundh 2017).

  • Unclear risk of bias: the trial may or may not have been free of for‐profit bias as the trial did not provide any information on clinical trial support or sponsorship.

  • High risk of bias: the trial was sponsored by industry or received another type of for‐profit support (Lundh 2017).

Other bias

  • Low risk of bias: the trial appeared free of other factors that could put it at risk of bias.

  • Unclear risk of bias: the trial may or may not have been free of other factors that could put it at risk of bias.

  • High risk of bias: there were other factors in the trial that could put it at risk of bias.

Overall risk of bias

We will assess overall risk of bias in the trials as follows.

  • Low risk of bias: if all the above risk of bias sources are assessed at low risk of bias.

  • High risk of bias: if one or more of the above risk of bias sources are assessed at 'unclear risk of bias' or 'high risk of bias'.

We will assess 'Blinding of outcome assessment,' 'Incomplete outcome data,' and 'Selective outcome reporting' source of bias for each outcome. Thus, we will be able to assess the bias risk for each outcome in addition to each trial.

We will attempt to base our primary conclusions on the results of our primary outcomes at low risk of bias.

Measures of treatment effect

For dichotomous variables, we will calculate the risk ratios (RRs) with 95% confidence intervals (CIs), and if the cumulative Z‐curve does not cross any of the trial sequential monitoring boundaries, we will also calculate the Trial Sequential Analysis‐adjusted CI (see below).

For continuous outcomes, we plan to impute the standard deviation (SD) from P values according to guidance given in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). If the data are likely to be normally distributed, we plan to use the median for meta‐analysis when the mean is not available. If it is not possible to calculate the SD from the P value or the CIs, we plan to impute the SD using the largest SD in other trials for that outcome. We will calculate the mean difference (MD) (if all outcomes are similar) or the standardised mean difference (SMD) if the outcomes are heterogenous) with 95% CIs, and if the cumulative Z‐curve does not cross any of the trial sequential monitoring boundaries, we will also calculate the Trial Sequential Analysis‐adjusted CI (see below).

Unit of analysis issues

Participants as randomised to the intervention groups. In the trials with two parallel groups design, we will compare the experimental antibiotic intervention group versus the placebo or no intervention control group. In the trials with a parallel group design with more than two intervention groups, we will compare in separate each of the experimental antibiotic group with each half of the placebo or no intervention control group.

In case of trials with a cross‐over design, we will include the data from the first trial period in order avoid residual effects from the treatment (Higgins 2011). In order to avoid repeated observations on trial participants, we will use participant trial data at the longest follow‐up (Higgins 2011).

Dealing with missing data

We will perform an intention‐to‐treat analysis whenever possible. Otherwise, we will use the data that are available to us (e.g. a trial may have reported only 'per‐protocol' analysis results), or if additional data are received from trialists. As 'per‐protocol' analyses may be biased, we plan to conduct best‐worst case scenario analysis (good outcome in intervention group and bad outcome in control group) and worst‐best case scenario analysis (bad outcome in intervention group and good outcome in control group) for our dichotomous primary outcomes only as sensitivity analyses, whenever possible (Hollis 1999). These analyses are defined as follows.

  • Best‐worse case scenario: none of the dropouts/participants lost from the experimental group, but all of the dropouts/participants lost from the control group experienced the outcome, including all randomised participants in the denominator.

  • Worst‐best case scenario: all dropouts/participants lost from the experimental group, but none from the control group experienced the outcome, including all randomised participants in the denominator.

For our continuous primary outcomes, we plan to conduct beneficial outcome (group mean plus two SD of the group mean) and harmful outcome (group mean minus two SD of the group mean) scenario analyses.

Assessment of heterogeneity

We will assess the presence of clinical heterogeneity by comparing effect estimates of different aetiologies for cirrhosis, severity of the underlying liver disease, presence of other decompensation, treatment applied to control bleeding event, and based on co‐interventions. We will assess methodological heterogeneity by comparing intervention effect in trials with low risk of bias to that of trials with unclear or high risk of bias (i.e. trials that lack one or more adequate domain) (Schulz 1995; Kjaergard 2001).

We will primarily inspect forest plots visually in order to assess if there are signs of statistical heterogeneity (Jakobsen 2014). We will also assess statistical heterogeneity using the Chi2 test with significance set at a P value of less than 0.10 and measure the quantities of heterogeneity using I2 (Higgins 2011).

Assessment of reporting biases

Funnel plot of the primary outcome will be used to provide a visual assessment of whether treatment estimates are associated with study size. We will use two tests to assess funnel plot asymmetry: adjusted rank correlation test and regression asymmetry test (Begg 1994; Egger 1997).

Data synthesis

Meta‐analysis

We will conduct the systematic review according to recommendations stated in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011) and according to the eight‐step procedure for validation of meta‐analytic results in systematic reviews as suggested by Jakobsen and colleagues (Jakobsen 2014). We will meta‐analyse data using the statistical software Review Manager 5.3 (Review Manager 2014) as well as Trial Sequential Analysis (follows below).

Trial Sequential Analysis

Cumulative meta‐analyses are at risk of producing random errors due to sparse data and multiple testing of accumulating data (Brok 2008; Wetterslev 2008; Brok 2009; Thorlund 2009, Wetterslev 2009; Thorlund 2010; Wetterslev 2017); therefore, Trial Sequential Analysis (TSA 2011) can be applied to control this risk (Thorlund 2011). The required information size (that is, the number of participants needed in a meta‐analysis to detect or reject a certain intervention effect) can be calculated in order to control random errors (Wetterslev 2008; Wetterslev 2009; Wetterslev 2017). The required information size takes into account the event proportion in the control group, the assumption of a plausible RR reduction, and the heterogeneity of the meta‐analysis (Wetterslev 2008; Wetterslev 2009; Turner 2013; Wetterslev 2017). Trial Sequential Analysis enables testing for significance to be conducted each time a new trial is included in the meta‐analysis. On the basis of the required information size, trial sequential monitoring boundaries can be constructed. This enables one to determine the statistical inference concerning cumulative meta‐analysis that has not yet reached the required information size (Wetterslev 2008).

If the trial sequential monitoring boundary is crossed before reaching the calculated information size, we may conclude that sufficient evidence is collected to validly assess benefit or harm, and that inclusion of additional trial data may be redundant. In contrast, if the boundaries for benefit or harm are not crossed, we may conclude that further trials are necessary before a certain intervention effect can be evaluated. Trial Sequential Analysis also allows for assessment of the sufficiency of evidence for a postulated intervention effect. A lack of effect is evident if the cumulative Z‐score crosses the trial sequential monitoring boundaries for futility.

We will make relatively conservative estimations of the anticipated intervention effect to control the risks of random error (Jakobsen 2014). Large anticipated intervention effects lead to small required information sizes, and the thresholds for significance will be less strict after the information size has been reached (Jakobsen 2014).

We will analyse all primary and secondary outcomes using Trial Sequential Analysis. These analyses will allow us to calculate the Trial Sequential Analysis‐adjusted CIs based on the following assumptions.

Primary outcomes

We will estimate the diversity‐adjusted required information size (Wetterslev 2009), based on the proportion of patients with an outcome in the control group. We will use an alpha of 0.025 because of our three primary outcomes, a beta of 10%, and the diversity suggested by the trials in the meta‐analysis (Jakobsen 2014; Castellini 2017).

As anticipated intervention effects for the primary outcomes in the Trial Sequential Analysis we will use the following.

  • All‐cause mortality: a relative risk reduction of 20% and the observed incidence of mortality in the control group

  • Health‐related quality of life: observed SD divided by 2

  • Serious adverse events: a relative risk reduction of 20% and the observed proportion of serious adverse events in the control group

Secondary outcomes

We will estimate the diversity‐adjusted required information size (Wetterslev 2009), based on the proportion of patients with an outcome in the control group when analysing dichotomous outcomes, and we will use the observed SD when analysing continuous outcomes. We will use an alpha of 0.02 because of the four secondary outcomes, a beta of 10%, and the diversity suggested by the trials in the meta‐analysis (Jakobsen 2014; Castellini 2017).

As anticipated intervention effects for the secondary outcomes in the Trial Sequential Analysis, we will use the following relative risk reductions or increases.

  • Infection‐related or bleeding‐related mortality: a relative risk reduction of 10%

  • Early re‐bleeding: a relative risk reduction of 10%

  • Proportion of participants with infection: a relative risk reduction of 20%

  • Non‐serious adverse events: a relative risk reduction of 20%.

Assessment of imprecision

In order to have a better judgement of imprecision in the included trials, we will compare GRADE and TSA results regarding our Primary outcomes and Secondary outcomes (Castellini 2018) in a sensitivity analysis.

Assessment of significance

We will assess the intervention effects with both random‐effects (DerSimonian 1986) and fixed‐effect meta‐analyses (DeMets 1987). We will assess significance using the more conservative point estimate of the two, comprised by the estimate closest to zero effect (Jakobsen 2014). If the two estimates are comparable, we will use the estimate with the widest CI. For analysis of three primary outcomes, we will consider significant a P value less than 0.025 (Jakobsen 2014), as this will secure a family wise error rate (FWER) below 0.05. We will apply an eight‐step procedure to assess if the results from the meta‐analyses have passed the thresholds for significance (Jakobsen 2014).

Subgroup analysis and investigation of heterogeneity

We plan to assess the differences in the effect estimates between the following subgroups.

  • Trials at low risk of bias compared to trials at high risk of bias

  • Severity of liver disease (Child‐pugh score)

  • Aetiology of liver cirrhosis

  • Following the type of antibiotic used for prophylaxis

  • Presence of other decompensation events (e.g. ascites, hepatic encephalopathy)

  • Treatment applied to control bleeding event (pharmacological, endoscopic, or both)

  • Route of administration of the antibiotic

We may perform further subgroup analysis if deemed necessary (Higgins 2011).

Sensitivity analysis

In addition to the sensitivity analysis described in the 'Dealing with missing data' section and Assessment of imprecision (see above), we plan to perform sensitivity analysis on trials at low risk of bias. We may perform further sensitivity analysis if deemed necessary (Higgins 2011; Jakobsen 2014).

'Summary of findings' table

We will create 'Summary of findings' tables for all clinically relevant outcomes (all‐cause mortality, serious adverse events, health‐related quality of life, infection‐related or bleeding‐related mortality, early re‐bleeding, proportion of participants with infection and non‐serious adverse events) reported in the review using GRADE Interactive software (GRADEpro). The GRADE approach appraises the quality of a body of evidence based on the extent to which one can be confident that an estimate of effect or association reflects the item being assessed. The quality of a body of evidence considers within‐study risk of bias (methodological quality), indirectness of the evidence (population, intervention, control, outcomes), unexplained heterogeneity or inconsistency of results (including problems with subgroup analyses); imprecision of effect estimates (wide CIs), and a high probability of publication bias (Balshem 2011; Mustafa 2013). We will define the levels of evidence as 'high', 'moderate', 'low', or 'very low'.

These grades are defined as follows.

  • High quality: this research provides a very good indication of the likely effect; the likelihood that the effect will be substantially different is low.

  • Moderate quality: this research provides a good indication of the likely effect; the likelihood that the effect will be substantially different is moderate.

  • Low quality: this research provides some indication of the likely effect; however, the likelihood that it will be substantially different is high.

  • Very low quality: this research does not provide a reliable indication of the likely effect; the likelihood that the effect will be substantially different is very high.