Interventions for preventing diarrhoea‐associated haemolytic uraemic syndrome

Abstract

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

Figure 1 shows the conceptual framework to show the objective of the review. Our primary objective was

To assess the effectiveness and safety of intervention for secondary prevention of morbidity and mortality from diarrhoea associated HUS in children and adults, compared to no secondary intervention use.

Background

Description of the condition

Haemolytic uraemic syndrome (HUS) is a serious condition caused by abnormal destruction of red blood cells and kidney damage and diagnosed clinically as a triad of microangiopathic haemolytic anaemia, thrombocytopenia and acute kidney injury (Fakhouri 2017; Mele 2014). HUS can be divided into two types; primary and secondary. Primary HUS is mostly related to infections with about 90% cases developing after diarrhoeal disease due to Shiga toxin‐producing Escherichia coli (STEC). Secondary HUS happens in patients with known diseases or conditions such as organ transplant, autoimmune disease, cancer, pregnancy, and certain cytotoxic drugs (Jokiranta 2017). Children are most commonly affected (Mody 2015; Talarico 2016), however cases of adults with HUS have been reported (Gould 2011; Mele 2014). Available evidence points to endothelial cell damage as a primary event in the pathogenesis of HUS, mediated by Shiga‐toxin in case of STEC infection and complement activation in atypical and secondary causes of HUS (Corrigan 2001; Fakhouri 2017). Additionally, Shiga toxin‐producing organisms infect the gastrointestinal tract, and induce diarrhoea that may progress to haemorrhagic colitis (Melton‐Celsa 2014).

This review focuses on HUS associated with diarrhoeal disease due to STEC. STEC infection causes about 2,800,000 illnesses annually across the globe and leads to about 3890 cases of HUS, 270 cases of end‐stage kidney disease (ESKD), and 230 deaths (Majowicz 2014). Incubation periods last anywhere from one to 12 days and symptoms can include nausea, vomiting, cramping, abdominal pain, and watery diarrhoea that then turns bloody within two to three days (Bell 1994; Keir 2015; Riley 1983). After seven to 10 days from the onset of symptoms, 10% to 15% of those infected continue to develop HUS and 30% of these patients develop serious complications such as ESKD and neurological sequelae and death (Garg 2003; Keir 2015; Rowe 1998; Siegler 1994).

Description of the intervention

Prevention of diarrhoea associated HUS can be in the form of primary or secondary prevention. Primary prevention relies on identifying and modifying predisposing risk factors for STEC infection, such as food safety, hand‐washing, and waste disposal (see conceptual framework in Figure 1). Secondary prevention relies on taking actions to reduce the risk of developing HUS once the disease, in this case, infectious diarrhoea, has been diagnosed. Some examples of intervention for secondary prevention of HUS include antibiotics, monoclonal antibodies against Shiga toxin, Shiga toxin binding proteins (i.e. Synsorb PK), and supportive therapies such as aggressive hydration (Grisaru 2017; Thomas 2013).

Figure 1.

Conceptual framework to show the objective of the review

How the intervention might work

Use of antibiotics to treat STEC infection to prevent HUS is debatable (Fakhouri 2017). The Centers for Disease Control and Prevention and American Gastroenterology Association both recommend against the use of antibiotics to treat STEC to prevent HUS, due to concerns that antibiotic can potentially increase risk of HUS after STEC infection (CDC 2018; Riddle 2016). These recommendations are mostly based on findings from observational studies and randomised controlled trials (RCTs) that did not show any increased or protective effect of antibiotics with relation to HUS (Freedman 2016; Thomas 2013; Wong 2000). Monoclonal antibodies against Shiga toxin is another potential novel approach for clinical detection of the toxin (Skinner 2016). Anti‐Shiga toxin monoclonal antibodies have been investigated as potential treatments in animal models and in healthy volunteers, yet the evidence for their interventional efficacy is still inconclusive (Bitzan 2009; Dowling 2005; Lopez 2010; Mejias 2016; Melton‐Celsa 2014). Monoclonal antibodies against Shiga toxins 1 and 2 may be used as a preventative strategy in preventing the onset of HUS (Melton‐Celsa 2014; Thomas 2013). Synsorb PK is a silicon dioxide compound containing the trisaccharide part of Gb3 that serves as binding protein to prevent the absorption of Shiga toxin from the gastrointestinal system (Armstrong 1991; Trachtman 2003). In addition to Synsorb PK, several other Shiga toxin receptor analogues have been developed including STARFISH, Daisy, SUPER TWIG, Gb3 polymers, Ac‐PPPtet, and probiotic with a Shiga toxin binder (Melton‐Celsa 2014). These developments can potentially prevent HUS by neutralizing the action of Shiga‐toxins (Melton‐Celsa 2014).

Why it is important to do this review

Treatment strategies for HUS have been discussed in a prior Cochrane review (Michael 2009); however, no Cochrane review has focused on secondary prevention of diarrhoea‐associated HUS. Other non‐Cochrane reviews have focused on selective interventions (Freedman 2016; Grisaru 2017) and no meta‐analyses were performed in these reviews (Thomas 2013). We therefore aim to synthesize up to date evidence regarding secondary preventative strategies for diarrhoea‐associated HUS.

Objectives

Figure 1 shows the conceptual framework to show the objective of the review. Our primary objective was

To assess the effectiveness and safety of intervention for secondary prevention of morbidity and mortality from diarrhoea associated HUS in children and adults, compared to no secondary intervention use.

Methods

Criteria for considering studies for this review

Types of studies

All RCTs and quasi‐RCTs (RCTs in which allocation to treatment was obtained by alternation, use of alternate medical records, date of birth or other predictable methods) looking at secondary prevention strategies of diarrhoea‐associated HUS will be included. We will include randomised studies if the randomisation was done at the individual or the cluster level. We will also include cross‐over RCTs but only include data from first part of the study. We will exclude all other observational studies such as cohort, case‐control, case series, and case reports.

Types of participants

Paediatric and adult patients with diarrhoea who are at risk of developing HUS, such as those infected with STEC including both O157 and non O157 serotypes. We will include studies with participants at risk of developing diarrhoea‐associated HUS regardless of a particular setting, educational status, gender, race, geographic location, or socioeconomic status of the participants.

We will exclude studies with patients that are at risk of non‐diarrhoea‐associated HUS such as those associated with Streptococcus pneumonia infections, disorders of complement regulation, ADAMTS13 deficiency, cancer, organ transplant and pregnancy. This is because pathophysiology of non‐diarrhoea‐associated HUS is thought to be different than diarrhoea‐associated HUS and it is hard to predict occurrence of HUS in non‐diarrhoea‐associated cases (Fakhouri 2017). We anticipate that randomisation of patients will happen after the diagnosis of STEC infection is made as rapid, culture independent diagnostic test are available to diagnose STEC infections in the clinical settings (Freedman 2016).

Types of interventions

We will evaluate the following interventions used to prevent diarrhoea associated HUS.

• Antibiotics

• Anti‐Shiga toxin monoclonal antibodies

• Shiga toxin binding protein (i.e. Synsorb PK)

• Aggressive hydration.

We will include studies regardless of the type of antibiotics used, mode of delivery of intervention (oral versus intravascular/intramuscular), and frequency of intervention.

We will exclude studies that evaluate interventions delivered after the diagnosis of HUS, given that these interventions are outside the scope of this review. We will also exclude studies in which the intervention is given as a primary form of prevention for diarrhoea itself, as these interventions are also outside the scope of the review.

Eligible comparison groups will include placebo and standard of care conditions. We will include studies with multiple treatment arms such as factorial design trials, as long as the study reports contrasts in a way whereby the only difference between two groups was the intervention.

Types of outcome measures

Primary outcomes

• Incidence of HUS in patients with diarrhoea

HUS will be defined as a triad of microangiopathic haemolytic anaemia, thrombocytopenia, and acute kidney injury that happened within three weeks of the diarrhoeal episode. The laboratory evidence might include anaemia with microangiopathic changes such as presence of schistocytes, burr cells, or helmet cells on peripheral blood smear and kidney injury evidenced by haematuria, proteinuria or elevated creatinine or blood urea nitrogen (CDC 1996). We will also include cases of thrombotic thrombocytopenic purpura (TTP) after a diarrhoeal episode. The definition of TTP includes the triad of HUS plus central nervous system involvement and fever. If the definition of the primary outcomes was not given explicitly in the study, we will contact the authors for further information. If no information is available on how the HUS was defined, we will still include the data from that study but do a sensitivity analysis to assess if the inclusion/exclusion of the study alters the results significantly.

Secondary outcomes

1. Oligoanuric kidney failure defined as urine output < 1 mL/kg/h in infants, < 5 mL/kg/h in children, and < 400 mL daily in adults

2. Need for acute renal replacement therapy (RRT; dialysis)

3. Need for prolonged dialysis for one to three months post HUS acute phase, or develop dialysis dependent ESKD needing kidney transplant

4. All‐cause mortality

5. Adverse events or any serious acute phase complications such as bowel perforation or obstruction, peritonitis, sepsis, cardiac injury, or pancreatitis

6. Need for blood transfusion and platelet transfusions

7. Incidence of neurological complications (e.g. stroke, seizures).

Search methods for identification of studies

Electronic searches

We will search the Cochrane Kidney and Transplant Register of Studies through contact with the Information Specialist using search terms relevant to this review. The Register contains studies identified from the following sources.

1. Monthly searches of the Cochrane Central Register of Controlled Trials (CENTRAL)

2. Weekly searches of MEDLINE OVID SP

3. Handsearching of kidney‐related journals and the proceedings of major kidney conferences

4. Searching of the current year of EMBASE OVID SP

5. Weekly current awareness alerts for selected kidney and transplant journals

6. Searches of the International Clinical Trials Register (ICTRP) Search Portal and ClinicalTrials.gov.

Studies contained in the Register are identified through searches of CENTRAL, MEDLINE, and EMBASE based on the scope of Cochrane Kidney and Transplant. Details of these searches, as well as a list of handsearched journals, conference proceedings and current awareness alerts, are available in the "Specialised Register" section of information about Cochrane Kidney and Transplant.

There will be no language restrictions. The literature search will include all journals, including but not limited to kidney journals, infectious disease journals, and general paediatric journals.

See Appendix 1 for search terms used in strategies for this review.

Searching other resources

1. Reference lists of review articles, relevant studies and clinical practice guidelines.

2. Letters seeking information about unpublished or incomplete RCTs to investigators known to be involved in previous studies.

3. Grey literature from clinicaltrials.gov and the Conference Proceeding Citation Index database (hosted on Web of Science).

4. Manually search reference lists of potentially included studies and previous reviews on topic.

Data collection and analysis

Selection of studies

The search strategy described will be used to obtain titles and abstracts of studies that may be relevant to the review. The titles and abstracts will be screened independently by two authors and they will select titles for full text review. Any conflict between the two authors will be resolved by discussion or contacting a senior author. Two authors will independently assess retrieved full text and made a decision about inclusion of a study. If there is no consensus on inclusion/exclusion of a study between two authors, and a third author will be consulted for a final decision.

Data extraction and management

Data extraction will be carried out independently by two authors using standard data extraction forms. Studies reported in non‐English language journals will be translated before assessment. Where more than one publication of one study exists, reports will be grouped together and the publication with the most complete data will be used in the analyses. Where relevant outcomes are only published in earlier versions these data will be used. Any discrepancy between published versions will be highlighted in the text of the review.

For eligible studies, at least two authors will extract the data and discrepancies will be resolved based on discussion with a third author.

A codebook will be used to define and describe all the variables abstracted from included studies. Using this codebook, a data collection sheet will be designed in an online data collection website. We plan to abstract the information on the following variables: study type, study site, baseline mortality and morbidity, inclusion/exclusion criteria, details of the intervention (e.g. type, route, frequency) risk of bias, attrition, coverage of intervention, characteristics of participants (e.g. age, race, gender, socioeconomic status), place of living (home versus facility), and outcome data.

When information regarding any of the above is unclear, we will attempt to contact authors of the original reports to provide further details. If authors had not performed an analysis for a particular variable, we will ask authors to perform that analysis or provide the original dataset so that we can perform the analysis for that outcome.

Assessment of risk of bias in included studies

The following items will be independently assessed by two authors using the Cochrane risk of bias assessment tool (Higgins 2011) (see Appendix 2). All risk of bias items will be coded as high, low, or unclear risk of bias, with additional textual support for each item.

• Was there adequate sequence generation (selection bias)?

• Was allocation adequately concealed (selection bias)?

• Was knowledge of the allocated interventions adequately prevented during the study?

  • Participants and personnel (performance bias)

  • Outcome assessors (detection bias)

• Were incomplete outcome data adequately addressed (attrition bias)?

• Are reports of the study free of suggestion of selective outcome reporting (reporting bias)?

• Was the study apparently free of other problems that could put it at a risk of bias?

Two review authors will independently assess the risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will resolve any disagreement by discussion or by involving a third author.

Measures of treatment effect

For dichotomous outcomes (incidence of HUS, need for acute dialysis, dialysis‐dependent ESKD) results will be expressed as risk ratio (RR) with 95% confidence intervals (CI). Where continuous scales of measurement are used to assess the effects of treatment, the mean difference (MD) will be used, or the standardised mean difference (SMD) if different scales have been used.

When estimating effect sizes from studies that included multiple comparison groups, comparison groups will be combined into a single pair‐wise comparison.

Unit of analysis issues

We will combine data from individual and cluster RCTs in the same meta‐analysis. We will use cluster‐adjusted values when reported by authors. If authors do not appropriately adjust for their cluster designs, we will conduct our own adjustments by inflating the standard error (SE) of the effect size estimate by multiplying it by the square root of design effect as described Cochrane handbook (Higgins 2011). If the design effect cannot be estimated for a primary study (e.g. if the cluster sizes, intra‐class correlation coefficients, or both are not reported), a design effect from similar study will be considered.

For studies using cross‐over designs, we will only include data from first phase of the study.

Dealing with missing data

Attrition is an important factor in RCTs and differential loss to follow‐up may lead to biased results. We will therefore extract information on attrition and report missing data, including dropouts and reasons for dropout as reported by authors. We will contact authors if data are missing, there are no reasons are provided for missing data, or both. When authors report data for completers as well as controlling for dropout (e.g. imputed using regression methods), we will extract the latter. We will also contact authors to obtain data if a study did not report data for a primary or secondary outcome of this review.

Data will be included based on an intention‐to‐treat analysis, i.e., all participants randomised to each group in the analyses will be analysed based on initial allocation, regardless of whether or not they received the allocated intervention.

Assessment of heterogeneity

We will first assess the heterogeneity by visual inspection of the forest plot. We will quantify statistical heterogeneity using the I² statistic, which describes the percentage of total variation across studies that is due to heterogeneity rather than sampling error (Higgins 2003). A guide to the interpretation of I² values will be as follows.

• 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.

The importance of the observed value of I² depends on the magnitude and direction of treatment effects and the strength of evidence for heterogeneity (e.g. P‐value from the Chi² test, or a confidence interval for I²) (Higgins 2011).

Clinical heterogeneity will be described in terms of the different types, durations, and frequencies of the included interventions. Methodological heterogeneity will be described in terms of the prevalence of individual versus cluster‐randomised trials.

Assessment of reporting biases

If 10 or more studies are included in a meta‐analysis, we will investigate reporting bias such as publication bias using funnel plots. We intended to assess funnel plot asymmetry visually. If asymmetry is suggested by a visual assessment, we will then perform additional analyses to investigate it by using an Egger regression test to quantify the magnitude of asymmetry, and a trim and fill analysis to assess the potential effect of funnel plot asymmetry on the estimated mean effect size.

Data synthesis

We will combine data from individual trials for meta‐analysis when the interventions, patient groups, and outcomes are sufficiently similar (as determined by consensus). We will synthesize effect sizes in the meta‐analysis using a random effects model, and use the restricted maximum likelihood estimator for the random‐effects variance component. The primary assumption underlying the random effects model is that mean effect of the intervention varies in the population and the available studies are a sample of all possible studies on this topic. The random effects model therefore allows for heterogeneity in intervention effects in the population, which is a reasonable assumption in this literature given the variability in the interventions, participants, and outcome measures we plan to include. All the meta‐analysis will be performed with software review manager (RevMan 2014).

'Summary of findings' tables

We will present the main results of the review in 'Summary of findings' tables. These tables present key information concerning the quality of the evidence, the magnitude of the effects of the interventions examined, and the sum of the available data for the main outcomes (Schünemann 2011a). The 'Summary of findings' tables also include an overall grading of the evidence related to each of the main outcomes using the GRADE (Grades of Recommendation, Assessment, Development and Evaluation) approach (GRADE 2008; Guyatt 2011). The GRADE approach defines the quality of a body of evidence as the extent to which one can be confident that an estimate of effect or association is close to the true quantity of specific interest. The quality of a body of evidence involves consideration of within‐trial risk of bias (methodological quality), directness of evidence, heterogeneity, precision of effect estimates and risk of publication bias (Schünemann 2011b). We plan to present the following outcomes in the 'Summary of findings' tables.

• Incidence of HUS in patients with diarrhoea

• Adverse events

• Need for acute dialysis

• Incidence of neurological complications

• All‐cause mortality

Subgroup analysis and investigation of heterogeneity

We plan to conduct the following subgroup analyses.

• STEC versus other causes of diarrhoea‐associated HUS

• Children (< 18 years) versus adults (≥ 18 years)

• Outbreak settings versus non‐outbreak settings

• Hospital setting versus community‐based studies versus mixed/undefined settings

• Low and middle‐income countries versus high‐income countries.

Sensitivity analysis

We also plan to perform sensitivity analyses to explore the influence of the following factors on the estimated mean effect sizes.

• Repeating the analysis excluding unpublished studies

• Repeating the analysis excluding studies with high risk of bias on sequence generation

• Repeating the analysis excluding any small sample size studies.

We will also carry out sensitivity analyses to investigate the effect of missing data.

• 5% to 10% missing data

• 10% to 20% missing data

• 20% or more missing data.

Appendices

Appendix 1. Electronic search strategies

Database	Search terms
CENTRAL	MeSH descriptor: [Hemolytic‐Uremic Syndrome] this term only MeSH descriptor: [Shiga‐Toxigenic Escherichia coli] explode all trees STEC‐HUS:ti,ab,kw (Word variations have been searched) "D+HUS":ti,ab,kw (Word variations have been searched) "O157:H7":ti,ab,kw (Word variations have been searched) diarrh*ea‐associated h*emolytic ur*emic:ti,ab,kw (Word variations have been searched) {or #1‐#6}
MEDLINE	Hemolytic‐Uremic Syndrome/ STEC‐HUS.tw. exp Shiga‐Toxigenic Escherichia coli/ diarrh?ea‐associated h?emolytic ur?emic.tw. D+HUS.tw. shiga‐toxin$.tw. "O157:H7".tw. or/1‐7
EMBASE	diarrh?ea‐associated h?emolytic ur?emic.tw. hemolytic uremic syndrome/ STEC‐HUS.tw. shiga toxin producing escherichia coli/ "O157:H7".tw. D+HUS.tw. or/1‐6

Appendix 2. Risk of bias assessment tool

Potential source of bias	Assessment criteria
Random sequence generation Selection bias (biased allocation to interventions) due to inadequate generation of a randomised sequence	Low risk of bias: Random number table; computer random number generator; coin tossing; shuffling cards or envelopes; throwing dice; drawing of lots; minimisation (minimisation may be implemented without a random element, and this is considered to be equivalent to being random).
	High risk of bias: Sequence generated by odd or even date of birth; date (or day) of admission; sequence generated by hospital or clinic record number; allocation by judgement of the clinician; by preference of the participant; based on the results of a laboratory test or a series of tests; by availability of the intervention.
	Unclear: Insufficient information about the sequence generation process to permit judgement.
Allocation concealment Selection bias (biased allocation to interventions) due to inadequate concealment of allocations prior to assignment	Low risk of bias: Randomisation method described that would not allow investigator/participant to know or influence intervention group before eligible participant entered in the study (e.g. central allocation, including telephone, web‐based, and pharmacy‐controlled, randomisation; sequentially numbered drug containers of identical appearance; sequentially numbered, opaque, sealed envelopes).
	High risk of bias: Using an open random allocation schedule (e.g. a list of random numbers); assignment envelopes were used without appropriate safeguards (e.g. if envelopes were unsealed or non‐opaque or not sequentially numbered); alternation or rotation; date of birth; case record number; any other explicitly unconcealed procedure.
	Unclear: Randomisation stated but no information on method used is available.
Blinding of participants and personnel Performance bias due to knowledge of the allocated interventions by participants and personnel during the study	Low risk of bias: No blinding or incomplete blinding, but the review authors judge that the outcome is not likely to be influenced by lack of blinding; blinding of participants and key study personnel ensured, and unlikely that the blinding could have been broken.
	High risk of bias: No blinding or incomplete blinding, and the outcome is likely to be influenced by lack of blinding; blinding of key study participants and personnel attempted, but likely that the blinding could have been broken, and the outcome is likely to be influenced by lack of blinding.
	Unclear: Insufficient information to permit judgement
Blinding of outcome assessment Detection bias due to knowledge of the allocated interventions by outcome assessors.	Low risk of bias: No blinding of outcome assessment, but the review authors judge that the outcome measurement is not likely to be influenced by lack of blinding; blinding of outcome assessment ensured, and unlikely that the blinding could have been broken.
	High risk of bias: No blinding of outcome assessment, and the outcome measurement is likely to be influenced by lack of blinding; blinding of outcome assessment, but likely that the blinding could have been broken, and the outcome measurement is likely to be influenced by lack of blinding.
	Unclear: Insufficient information to permit judgement
Incomplete outcome data Attrition bias due to amount, nature or handling of incomplete outcome data.	Low risk of bias: No missing outcome data; reasons for missing outcome data unlikely to be related to true outcome (for survival data, censoring unlikely to be introducing bias); missing outcome data balanced in numbers across intervention groups, with similar reasons for missing data across groups; for dichotomous outcome data, the proportion of missing outcomes compared with observed event risk not enough to have a clinically relevant impact on the intervention effect estimate; for continuous outcome data, plausible effect size (difference in means or standardised difference in means) among missing outcomes not enough to have a clinically relevant impact on observed effect size; missing data have been imputed using appropriate methods.
	High risk of bias: Reason for missing outcome data likely to be related to true outcome, with either imbalance in numbers or reasons for missing data across intervention groups; for dichotomous outcome data, the proportion of missing outcomes compared with observed event risk enough to induce clinically relevant bias in intervention effect estimate; for continuous outcome data, plausible effect size (difference in means or standardized difference in means) among missing outcomes enough to induce clinically relevant bias in observed effect size; ‘as‐treated’ analysis done with substantial departure of the intervention received from that assigned at randomisation; potentially inappropriate application of simple imputation.
	Unclear: Insufficient information to permit judgement
Selective reporting Reporting bias due to selective outcome reporting	Low risk of bias: The study protocol is available and all of the study’s pre‐specified (primary and secondary) outcomes that are of interest in the review have been reported in the pre‐specified way; the study protocol is not available but it is clear that the published reports include all expected outcomes, including those that were pre‐specified (convincing text of this nature may be uncommon).
	High risk of bias: Not all of the study’s pre‐specified primary outcomes have been reported; one or more primary outcomes is reported using measurements, analysis methods or subsets of the data (e.g. sub‐scales) that were not pre‐specified; one or more reported primary outcomes were not pre‐specified (unless clear justification for their reporting is provided, such as an unexpected adverse effect); one or more outcomes of interest in the review are reported incompletely so that they cannot be entered in a meta‐analysis; the study report fails to include results for a key outcome that would be expected to have been reported for such a study.
	Unclear: Insufficient information to permit judgement
Other bias Bias due to problems not covered elsewhere in the table	Low risk of bias: The study appears to be free of other sources of bias.
	High risk of bias: Had a potential source of bias related to the specific study design used; stopped early due to some data‐dependent process (including a formal‐stopping rule); had extreme baseline imbalance; has been claimed to have been fraudulent; had some other problem.
	Unclear: Insufficient information to assess whether an important risk of bias exists; insufficient rationale or evidence that an identified problem will introduce bias.

Contributions of authors

1. Draft the protocol: AI, TS

2. Study selection: AI, TS

3. Extract data from studies: AI, TS, DH

4. Enter data into RevMan: AI, TS

5. Carry out the analysis: AI

6. Interpret the analysis: AI, ETS, OG, DH

7. Draft the final review: AI, TS

8. Disagreement resolution: OG, ETS, DH

9. Update the review: AI

Declarations of interest

All authors declare they do not have any conflict of interest. None of the authors listed in this review have any present or past affiliations or other involvement in any organization or entity with an interest in the outcome of the review. There have been no reported relationships present during the past 36 months, including, but not restricted to, financial remuneration for lectures, consultancy, travel or authorship of a study that might be included in this review.

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