Antimicrobial sutures for the prevention of surgical site infection

Justin CR Wormald; Henry A Claireaux; Alexander J Baldwin; James K-K Chan; Jeremy N Rodrigues; Jonathan A Cook; Daniel Prieto-Alhambra; Mike J Clarke; Matthew L Costa

doi:10.1002/14651858.CD014719

. 2022 Jun 21;2022(6):CD014719. doi: 10.1002/14651858.CD014719

Antimicrobial sutures for the prevention of surgical site infection

Justin CR Wormald ^1,^✉, Henry A Claireaux ², Alexander J Baldwin ³, James K-K Chan ⁴, Jeremy N Rodrigues ¹, Jonathan A Cook ¹, Daniel Prieto-Alhambra ⁵, Mike J Clarke ⁶, Matthew L Costa ¹

Editor: Cochrane Wounds Group

PMCID: PMC9212211

Objectives

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

To compare the effects of antimicrobial sutures versus standard sutures for the prevention of SSI.

Background

Description of the condition

Surgical site infection (SSI) is infection at the site of an operative procedure within a timeframe that implicates causality. SSI is commonly classified (including by the National Institute for Health and Care Excellence (NICE) and the World Health Organization (WHO)) using the Centers for Disease Control (CDC) Criteria: "infection within 30 days of an operation, or up to 90 days if an implant is left in place and the infection is related to an operative procedure" (Condon 1992).SSI is further classified by the CDC into the following types.

Superficial incisional, affecting the skin and subcutaneous tissue. These infections may be indicated by localised signs such as redness, pain, heat, or swelling at the site of the incision, or by the drainage of pus.
Deep incisional, affecting the fascial and muscle layers. These infections may be indicated by the presence of pus or an abscess, fever with tenderness of the wound, or a separation of the edges of the incision exposing the deeper tissues.
Organ or space infection, which involves any part of the anatomy other than the incision that is opened or manipulated during the surgical procedure, for example joint or peritoneum. These infections may be indicated by the drainage of pus or the formation of an abscess detected by histopathological or radiological examination or during re‐operation. Organ infection is not included within the scope of this guideline.

The risk of SSI can differ depending on the contamination status of the wound. The CDC have classified wounds by level of contamination into four types: clean, clean/contaminated, contaminated and dirty (Condon 1992). The risk of SSI in a clean wound is less than the risk of SSI in a clean/contaminated, contaminated or dirty wound, although there is variability in the exact risk depending on anatomical location and type of wound.

SSI is the most frequently researched and reported type of healthcare‐associated infection (HAI) in low‐middle income countries (LMICs) and affects up to one third of people who have undergone surgery (WHO 2018). At a procedural level, the pooled incidence of SSI was 11.8 per 100 surgical procedures in LMICs (Allegranzi 2011).Overall, SSI is likely to account for 20% of all HAIs (Anderson 2014). In the UK, NICE estimates that at least 2‐5% of patients undergoing surgery develop an SSI, although this is accepted to be an underestimation (NICE 2019a). Patients who develop an SSI are likely to have a prolonged hospital stay, a higher chance of needing intensive care, and nearly twice the mortality rate compared with those who do not (O'Brien 2020).SSIs are therefore associated with substantial healthcare and health services costs (Kirkland 1999).

Description of the intervention

Surgical sutures are used to repair organs and tissues during operations. In planned or elective surgery, a controlled incisional wound is made by the surgeon through the skin to gain access to deeper tissues or organs so that they can perform the required operative procedure (e.g. removing a liver tumour or replacing a knee joint). In emergency surgery where the skin is intact (e.g. emergency laparotomy) the process of incision is similar. For emergency surgery where the skin is broken, such as an open fracture or soft‐tissue laceration, an incision may not always be required to access the injured subcutaneous structures. Instead, the existing traumatic wound may be debrided (damaged tissue removed from the wound) or excised (surgical removal of the wound in its entirety) as the initial part of the operative procedure. Once the operative procedure is complete, the surgeon then uses surgical sutures to reconstruct the tissue layers they have had to cut through to reach the relevant tissues or organs. The surgical wound is closed 'layer‐by‐layer' finishing with the skin wound closure. The number of layers will depend on the anatomical location of the operation. For example, the abdomen has several layers of tissue between the viscera and the skin, and usually all layers are repaired before the skin is closed. In contrast, the hand has only one layer between the skin and the deeper structures, with only the skin requiring closure with sutures.

A variety of suture materials are available to tackle the variability in surgical wound requirements. Sutures may be absorbable or non‐absorbable, braided or a single filament (known as monofilament), and range in gauge as well as chemical and physical properties (Rose 2021). Although many more variations of suture type exist, these four characteristics are fundamental. Antimicrobial sutures consist of surgical sutures that are coated in, or impregnated with, a substance that is toxic to bacteria. Antimicrobial coatings prevent bacterial adherence and proliferation on the suture material by interrupting essential cell mechanisms within the bacterial cells. Several products have been introduced to the market during the past decade, including triclosan‐coated polydioxanone antimicrobial sutures (PDS Plus; Ethicon, Johnson & Johnson, Livingston, Scotland, UK; Diener 2014).The vast majority of antimicrobial sutures are coated in triclosan. Triclosan has been in use in healthcare settings since the 1970s but was only introduced as a suture coating in 2002 (De Jonge 2017). It is generally considered a safe product in the context of antimicrobial sutures, and is commonly used in commercial and environmental processes (Barbolt 2002).Triclosan (5‐chloro‐2 (2, 4‐dichlorophenoxyphenol)) is a broad‐spectrum antiseptic that is active against both gram‐positive and gram‐negative bacteria, through interference with microbial lipid synthesis (Bhargava 1996; Jones 2000). This causes reduced bacterial growth and inhibits bacterial colonisation of the suture material, demonstrated in both in‐vivo and in‐vitro studies (Katz 1981; Ming 2008).Antimicrobial sutures feel and handle the same as standard sutures, which makes double‐blind clinical trials feasible.

How the intervention might work

The effect of suture material on the pathogenesis of SSIs was demonstrated in the 1950s, with subsequent historical studies defining its role (Elek 1957). Sutures may be particularly prone to increase the risk of infection in emergency surgical conditions where there is associated tissue damage or devascularised tissue (Edlich 1968). This may explain the reported differences in effectiveness of antimicrobial sutures in emergency versus planned surgery in high quality studies (De Jonge 2017). There are many ways for bacteria to infiltrate in and around a surgical wound, including from normal skin bacterial flora, intestinal contents during emergency bowel surgery, and environmental contamination in open wounds. Although there are innate immune defences against bacterial wound infection, once foreign material, such as surgical sutures, become contaminated with bacteria, these mechanisms fail. This is primarily due to bacterial biofilm formation (Kathju 2014; Mingmalairak 2011). Coating surgical sutures in triclosan inhibits the local growth of bacteria, prevents bacteria from adhering to the surface of the sutures, and prevents bacterial biofilm formation in vitro and in vivo (Edmiston 2004; Ming 2008).There have been numerous randomised controlled trials (RCT) of antimicrobial sutures, with meta‐analysis indicating they could reduce SSI risk by around 28% (risk ratio (RR) 0.72, 95% confidence interval (CI) 0.60 ‐ 0.86; I² = 30%; De Jonge 2017).

Why it is important to do this review

Despite existing RCTs and subsequent meta‐analyses, there is still uncertainty regarding the clinical effectiveness of antimicrobial sutures. Meta‐analyses have not contextualised statistical analyses in terms of risk of bias and GRADE recommendations, and have lacked consistent methodological rigour (Ahmed 2019; De Jonge 2017; Sandini 2016; Wu 2017). This review question aligns with NICE Recommended Research on the use of topical antimicrobial agents and WHO research recommendations for further evaluation of antimicrobial sutures (Allegranzi 2016; NICE 2019a; NICE 2019b). In 2019, NICE recommended further research on "which closure method or technique is the most effective for reducing SSI in patients undergoing emergency surgery", recognising that the evidence for antimicrobial sutures is lacking in clinical practice (NICE 2019b). In 2020, NICE produced guidelines relating to interventions for SSI reduction, including guidance that clinicians should "consider" antimicrobial sutures for wound closure (NICE 2020). NICE, however, indicated that evidence for antimicrobial sutures remained inconclusive, with significant uncertainties due to heterogeneity of study populations and outcome measures (NICE 2020). In June 2021, NICE published guidance recommending adoption of antimicrobial sutures as part of a bundle of care to reduce SSIs (NICE 2021). This was based on an observed effect from a meta‐analysis of 31 RCTs across a variety of study populations with inconsistent delivery of the intervention (NICE 2021). When only high‐quality studies were included in the analysis, this effect was not observed. Since this guideline was produced, further data have become available from at least one RCT that has furthered the uncertainty regarding the effectiveness of antimicrobial sutures (Ademuyiwa 2021). The aim of this Cochrane review is to provide a comprehensive and contemporary summary of the evidence to aid evidence based decision making, inform guidelines, and inform policy. Additionally we hope that it will serve as a live review which we intend to regularly update as new evidence becomes available.

Objectives

To compare the effects of antimicrobial sutures versus standard sutures for the prevention of SSI.

Methods

Criteria for considering studies for this review

Types of studies

We will include published and unpublished RCTs, including cluster RCTs, irrespective of language of report. We will exclude studies using quasi‐randomisation.

Types of participants

We will consider all study participants who have undergone surgery to be eligible for inclusion, including both adult and paediatric populations. Participants undergoing surgery for infected wounds will not be included. For studies that include a mixed population of participants with and without infected wounds at baseline, we will obtain data for those without infected wounds. If this is not possible, we will only include the study if over 50% of the cohort do not have infected wounds at baseline. We will consider all healthcare settings, indications for surgery, conditions, and demographics in this review.

Types of interventions

The intervention of interest is antimicrobial sutures of any kind. This may include absorbable, non‐absorbable, monofilament, or braided antimicrobial sutures. We will exclude studies of antimicrobial wound closure materials other than sutures (e.g. staples). The comparator/control of interest is standard sutures of any kind. This may include absorbable, non‐absorbable, monofilament, or braided standard sutures. We will exclude studies that compare antimicrobial sutures with other, non‐suture wound closure materials (e.g. staples). The intervention/control must be delivered during the operative procedure for the study to be eligible for inclusion. We will include delayed wound closures after primary wound debridement and dressing, where primary wound closure has not been attempted (e.g. delayed closure of open abdomen or open fracture). We will exclude secondary wound closures after failed primary wound closure, as suture material will already be present in the wound.

Types of outcome measures

Primary outcomes

There are two co‐primary outcomes.

SSI by 30 days
SSI by 90 days if an implant has been used in the primary procedure

This subdivision is in accordance with the CDC classification of SSI (Condon 1992). If study definitions differ from the CDC definition, we will record this alongside the timeframe used. We will measure occurrence of SSI by clinical assessments of the study participants, performed by members of the primary study clinical and/or research teams. The clinical assessment will include the presence of various factors that indicate the type of SSI if present, including:

superficial incisional SSI ‐ symptoms or signs of infection (e.g. erythema, heat, increased swelling, increased pain) involving only skin and subcutaneous tissue of incision;
deep incisional SSI ‐ symptoms or signs of infection involving deep tissues, such as fascial and muscle layers; this also includes infection involving both superficial and deep incision sites and organ/space SSI draining through the incision; and
organ/space SSI ‐ symptoms or signs of infection involving any part of the anatomy that was opened or manipulated during surgery, in organs and spaces other than the incision.

SSI may also be measured using SSI‐specific scales or questionnaires that map directly to the above CDC definitions. If microbiological evidence of infection is reported, we will also include this as primary outcome data. If microbiological evidence (e.g. swab results) is the only indicator of infection, we will consider this as a surrogate outcome for SSI.

Secondary outcomes

All‐cause mortality
Incidence of wound dehiscence
Re‐operation for SSI
Hospital length of stay (mean, days)
Health care costs and cost‐effectiveness measures including relative cost‐effectiveness
Adverse events ‐ we will report the number of participants with an adverse event in each group.

We will assess secondary outcomes at the longest available follow‐up. We will collect data on adverse events and present this descriptively with a narrative synthesis.

Search methods for identification of studies

Electronic searches

We will search the following electronic databases to retrieve reports of relevant RCTs.

Cochrane Wounds Specialised Register
Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library (latest issue)
Ovid MEDLINE (1946 onwards)
Ovid Embase (1974 onwards)
EBSCO Cumulative Index to Nursing and Allied Health Literature (CINAHL Plus; 1937 onwards)

The draft search strategy for CENTRAL can be found in Appendix 1. We will adapt this strategy to search Ovid MEDLINE, Ovid Embase and EBSCO CINAHL Plus. We will combine the Ovid MEDLINE search with the Cochrane Highly Sensitive Search Strategy for identifying RCTs in MEDLINE: sensitivity‐maximising version (2008 revision; Lefebvre 2021). We will combine the Embase search with the Ovid Embase filter developed by the UK Cochrane Centre (Lefebvre 2021).We will combine the CINAHL Plus search with the trial filter developed by Glanville 2019. We will not apply restrictions with respect to language, date of publication, or study setting. We will also search the following clinical trials registries.

US National Institutes of Health Ongoing Trials Register ClinicalTrials.gov (www.clinicaltrials.gov)
World Health Organization (WHO) International Clinical Trials Registry Platform (https://trialsearch.who.int/)

Searching other resources

We will contact corresponding authors and the manufacturers and distributors of antimicrobial sutures. We will try to identify other potentially eligible trials or ancillary publications by searching the reference lists of retrieved included trials as well as relevant systematic reviews, meta‐analyses, and health technology assessment reports. We will search grey literature via the OpenGrey and GreyLit platforms.

Data collection and analysis

Selection of studies

Two review authors will independently assess the titles and abstracts of the citations retrieved by the searches for relevance. After this initial assessment we will obtain full‐text copies of all studies considered to be potentially relevant. Two review authors will independently check the full papers for eligibility; they will resolve disagreements by discussion and, where required, the input of a third review author. Where required and possible, we will contact study authors where the eligibility of a study is unclear. We will record all reasons for exclusion of studies for which we obtain full text. We will complete a PRISMA flowchart to summarise this process (Liberati 2009). Where studies have been reported in multiple publications/reports, we will obtain all publications. While we will include the study only once in the review, we will extract data from all reports to ensure that we obtain maximal relevant data. We will contact the authors of such studies by email to ensure there has been no duplication of data or double counting of participants.

Data extraction and management

We will extract and summarise details of the eligible studies using a data extraction sheet. Two authors will independently pilot the data extraction sheet on two studies and will rectify any issues with the sheet following the pilot. Two review authors will extract data independently and will resolve disagreements by discussion, drawing on a third review author where required. Where data are missing from reports, we will attempt to contact the study authors to obtain this information. Where a study with more than two intervention arms is included, we will extract only data from intervention and control groups that meet the eligibility criteria. We will extract the following data where possible by treatment group for the pre‐specified interventions and outcomes in this review. We will collect outcome data for the relevant time points as described above.

Country of origin
Unit of randomisation (e.g. participant)
Unit of analysis
Trial design (e.g. parallel, cluster)
Care setting
Number of participants randomised to each trial arm
Eligibility criteria and key baseline participant data
Details of treatment regimen received by each group
Number of operative procedures
Details of intervention delivery (e.g. sutures in deep tissue closure, superficial tissue closure, all tissue closure)
Details of any co interventions, such as wound dressings
Primary and secondary outcomes (with definitions)
Outcome data for primary and secondary outcomes (by group)
Duration of follow‐up
Number of withdrawals (by group)
Publication status of study
Presence of trial registration
Presence of published trial protocol
Source of funding for trial

Assessment of risk of bias in included studies

Two review authors will independently assess included studies using the Revised Cochrane risk‐of‐bias tool for randomised trials (RoB 2; Higgins 2021a; Sterne 2019; Appendix 2). The domains in the RoB 2 tool are: bias arising from the randomisation process; bias due to deviations from intended intervention; bias due to missing outcome data; bias in measurement of the outcome; and bias in selection of the reported results. In this review, we will record issues with unit of analysis, for example, where a cluster trial has been undertaken but analysed at the individual level in the study report. We will assess blinding and completeness of outcome data for each of the review outcomes separately. We note that, since wound healing is a subjective, clinical outcome, it can be at high risk of measurement bias when outcome assessment is not blinded. We will present our assessment of risk of bias using two risk of bias summary figures; one which will be a summary of bias for each item across all studies, and a second which will show a cross‐tabulation of each trial by all the risk of bias items. We will classify studies as being at 'high risk' of bias if there is apparent bias within any of the following: the randomisation process, deviation from the intended interventions, missing outcome data, bias in outcome measurement, and bias in selection of the reported results. For trials using cluster randomisation or within‐participant randomisation, we will also consider the risk of bias in terms of: recruitment bias, baseline imbalance, loss of clusters, incorrect analysis, and comparability with trials randomising participants (Higgins 2021b; Appendix 3).

Measures of treatment effect

SSI is most commonly presented as a proportion of the study groups and will therefore be dichotomous data. We will analyse dichotomous data to produce an RR with a 95% CI. We will use the same strategy for secondary outcomes with dichotomous data (e.g. reoperation for SSI). For continuous secondary outcome data (e.g. hospital stay), we will use the mean difference (MD) with a 95% CI, if all trials use the same or similar assessment scales. If trials use different assessment scales measuring the same outcome, we will use the standardised mean difference (SMD) with a 95% CI. We expect some variability in definitions of SSI in studies. Where possible we will convert these into the CDC classifications of SSI. If this is not possible then we will contact authors for further clarification of the details of how SSI was classified in an individual study.

Unit of analysis issues

We consider the participant as the unit of analysis of key interest. Some wound care trials randomise at the participant level, using the allocated treatment on multiple wounds per participant and then analyse outcomes per wound. This type of approach (split body design) should be treated in a similar way to a cluster or within‐participant trials, alongside standard cluster designs (e.g. delivery of interventions at an organisational level). Where a trial has been conducted and correctly analysed, effect estimates and their standard errors may be meta‐analysed using the generic inverse variance method in Review Manager 5 (Review Manager 2020). We will record where a cluster RCT has been conducted but incorrectly analysed as part of the risk of bias assessment. If possible, we will approximate the correct analyses based on the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021b) using information on:

the number of clusters (or groups) randomised to each intervention group, or the mean size of each cluster;
the outcome data, ignoring the cluster design for the total number of participants (e.g. number or proportion of participants with events, or means and standard deviations); and
an estimate of the intracluster (or intraclass) correlation coefficient (ICC).

If the study data cannot be analysed correctly, we will extract and present outcome data but not analyse them further. We will also note when randomisation has been undertaken within participant or at the wound level, that is, a split‐site or split‐body design. We will assess whether the correct analysis has been undertaken and record any issues in the risk of bias section. If an incorrect analysis has been undertaken, we will contact authors to attempt to obtain the original data or try to approximate the correct analysis if the required data are available. If this is not possible, we will extract and present the relevant outcome data but not analyse them further or pool them.

Dealing with missing data

It is common to have data missing from trial reports. Excluding participants post‐randomisation from the analysis, or ignoring those participants who are lost to follow‐up, compromises the randomisation, and potentially introduces bias into the trial. Where there are missing data we think should be included in the analyses, we will contact the relevant study authors to request these data if available. Where data remain missing for the ’proportion of wounds healed’ outcome, for analysis we will assume that if randomised participants were not included in an analysis, they did not develop an SSI. For continuous variables (e.g. length of hospital stay), and for all secondary outcomes, we will present available data from the study reports/study authors and do not plan to impute missing data. Where measures of variance are missing, we will calculate these wherever possible. If calculation is not possible, we will contact the study authors. Where these measures of variance are not available, we will exclude the study from any relevant meta‐analyses that are conducted.

Assessment of heterogeneity

Assessment of heterogeneity can be a complex, multi‐faceted process. First, we will consider clinical and methodological heterogeneity. that is, the degree to which the included studies vary in terms of participant, intervention, outcome, and characteristics such as length of follow‐up. We will supplement this assessment of clinical and methodological heterogeneity with information regarding statistical heterogeneity assessed using the Chi² test (we will consider a significance level of P < 0.10 to indicate statistically significant heterogeneity) in conjunction with the I² measure (Higgins 2003). The I² measure examines the percentage of total variation across RCTs that is due to heterogeneity rather than chance (Higgins 2003). In general, I² values of 25% or less may mean a low level of heterogeneity (Higgins 2003), and values of more than 75% indicate very high heterogeneity (Deeks 2021). Where there is evidence of high heterogeneity, we will attempt to explore this further: see Data synthesis.

Assessment of reporting biases

Reporting biases arise when the dissemination of research findings is influenced by the nature and direction of results. Publication bias is one of a number of possible causes of 'small‐study effects,' that is, a tendency for estimates of the intervention effect to be more beneficial in smaller RCTs. Funnel plots allow a visual assessment of whether small‐study effects may be present in a meta‐analysis. A funnel plot is a simple scatter plot of the intervention effect estimates from individual RCTs against some measure of each trial’s size or precision (Sterne 2011). We plan to present funnel plots for meta‐analyses comprising 10 RCTs or more using Review Manager 5 (Review Manager 2020). We will also cross‐reference reports with published protocols in an attempt to assess completeness of data reporting, omissions, and potential resultant bias.

Data synthesis

We will combine details of included studies in a narrative review according to type of comparator, possibly by location/type of wound, and then by outcomes by time period. We will consider clinical and methodological heterogeneity and will undertake pooling when studies appear appropriately similar in terms of wound type, intervention type, duration of follow‐up and outcome type. In terms of meta‐analytical approach, we are unable to pre‐specify the amount of clinical, methodological, and statistical heterogeneity in the included studies, but it might be extensive. Thus, we anticipate using a random‐effects approach for meta‐analysis. Conducting meta‐analysis with a fixed‐effect model in the presence of even minor heterogeneity may provide overly narrow confidence intervals. We will only use a fixed‐effect approach when clinical and methodological heterogeneity is assessed to be minimal and the assumption that a single underlying treatment effect is being estimated holds. We will use Chi² and I² to quantify heterogeneity but not to guide choice of model for meta‐analysis. We will exercise caution when meta‐analysed data are at risk of small study effects because a random‐effects model may be unsuitable. In this case or where there are other reasons to question the selection of a fixed‐effect or random‐effects model, we will assess the impact of the approach using sensitivity analyses to compare results from alternate models. We will report any evidence that suggests that the use of a particular model might not be robust. We may meta‐analyse even when there is thought to be extensive heterogeneity. We will attempt to explore the causes behind this using meta‐regression, if possible (Thompson 1999). We will present data using forest plots where possible. For dichotomous outcomes, we will present the summary estimate as an odds ratio (OR) with a 95% CI. Where continuous outcomes are measured in the same way across studies, we plan to present a pooled MD with a 95% CI; we plan to pool SMD estimates where studies measure the same outcome using different methods. We will obtain pooled estimates of treatment effects using Cochrane Review Manager 5 software (Review Manager 2020).

Subgroup analysis and investigation of heterogeneity

We will assess potential heterogeneity across the following areas specifically; where there is evidence of between‐trial heterogeneity, we envisage that we will conduct subgroup analyses as follows (for SSI only).

Emergency versus elective operations
LMIC study settings versus non‐LMIC study settings
According to CDC wound contamination status: clean, clean/contaminated, contaminated and dirty (Condon 1992)
Anatomical site of surgical wound (e.g. abdominal, groin, cardiothoracic, limb, head and neck, and breast)

Sensitivity analysis

Where possible, we will consider the following sensitivity analyses to explore the effect of the following criteria.

Blinding (blinded studies versus non‐blinded studies)
Risk of bias (high risk studies excluded versus all studies, as determined by the RoB2 tool, with any one domain at high risk resulting in an overall classification of high risk; Sterne 2019)
Trial registration (pre‐registered trials versus unregistered trials)

Summary of findings and assessment of the certainty of the evidence

Two review authors will independently use the GRADE approach to assess the certainty of the evidence for each outcome to determine confidence in the estimate of the observed effects (GRADE Handbook). These two review authors will independently rate the outcomes as high, moderate, low, or very low certainty evidence. We will achieve consensus on ratings by involvement of a third review author if needed. We will present the results for important outcomes 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 2021). The certainty 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 2021b). We plan to present the following outcomes in the summary of findings tables.

Surgical site infection by 30 days
Surgical site infection by 90 days
All cause mortality
Wound dehiscence
Adverse events

Elements of this Methods section are based on the standard Cochrane Wounds protocol template.

Acknowledgements

The authors would like to thank the following peer reviewers for providing feedback on the protocol: Julie Bruce, Beryl De Souza, and Ann E Fonfa. They would also like to thank Carloyn Wayne for copy‐editing the protocol.

Appendices

Appendix 1. CENTRAL (Cochrane Central Register of Controlled Trials in the Cochrane Library) Search Strategy

#1 MeSH descriptor: [Surgical Wound Infection] explode all trees

#2 MeSH descriptor: [Surgical Wound Dehiscence] explode all trees

#3 (surg* near/5 infect*):ti,ab,kw

#4 (surg* near/5 wound*):ti,ab,kw

#5 (surg* near/5 site*):ti,ab,kw

#6 (surg* near/5 incision*):ti,ab,kw

#7 (surg* near/5 dehisc*):ti,ab,kw

#8 (wound* near/5 dehisc*):ti,ab,kw

#9 (wound* near/5 infect*):ti,ab,kw

#10 (wound* near/5 disrupt*):ti,ab,kw

#11 (wound near/5 complication*):ti,ab,kw

#12 ((postoperative or post‐operative) near/5 (wound infection*)):ti,ab,kw

#13 SSI:ti,ab,kw

#14 #1 or #2 or #3 or #4 or #5 or #6 or #7 or #8 or #9 or #10 or #11 or #12 or #13

#15 MeSH descriptor: [Sutures] explode all trees

#16 (sutur*):ti,ab,kw

#17 #15 or #16

#18 MeSH descriptor: [Anti‐Infective Agents] explode all trees

#19 ((triclosan* or chlorhexidine* or polyhexamethylene biguanide or PHMB or octenidine*) near/5 (coat* or impregnat* or contain*)):ti,ab,kw

#20 ((antibiotic* or anti‐biotic* or antimicrobial* or anti‐microbial* or antiseptic* or anti‐septic* or antibacterial* or anti‐bacterial) near/5 (coat* or impregnat* or contain*)):ti,ab,kw

#21 #18 or #19 or #20

#22 #17 and #21

#23 (PDS Plus or Monocryl Plus or Vicryl Plus or Atramat Plus):ti,ab,kw

#24 #22 or #23

#25 #14 and #24

Appendix 2. Revised Cochrane risk‐of‐bias tool for randomized trials (RoB 2)

The response options for the signalling questions are:

Yes

Probably yes

Probably no

No information

Not applicable

Domain 1: Risk of bias arising from the randomisation process

Signalling questions

1.1 Was the allocation sequence random?

1.2 Was the allocation sequence concealed until participants were enrolled and assigned to interventions?

1.3 Did baseline differences between intervention groups suggest a problem with the randomisation process?

Risk‐of‐bias judgement Low / High / Some concerns

Optional: What is the predicted direction of bias arising from the randomisation process? NA / Favours experimental / Favours comparator / Towards null /Away from null / Unpredictable

Domain 2: Risk of bias due to deviations from the intended interventions (effect of assignment to intervention)

Signalling questions Comments Response options

2.1. Were participants aware of their assigned intervention during the trial?

2.2. Were carers and people delivering the interventions aware of participants' assigned intervention during the trial?

2.3. If Y/PY/NI to 2.1 or 2.2: Were there deviations from the intended intervention that arose because of the trial context?

2.4 If Y/PY to 2.3: Were these deviations likely to have affected the outcome?

2.5. If Y/PY/NI to 2.4: Were these deviations from intended intervention balanced between groups?

2.6 Was an appropriate analysis used to estimate the effect of assignment to intervention?

2.7 If N/PN/NI to 2.6: Was there potential for a substantial impact (on the result) of the failure to analyse participants in the group to which they were randomised?

Risk‐of‐bias judgement Low / High / Some concerns

Optional: What is the predicted direction of bias due to deviations from intended interventions? NA / Favours experimental / Favours comparator / Towards null /Away from null / Unpredictable

Domain 2: Risk of bias due to deviations from the intended interventions (effect of adhering to intervention)

Signalling questions Comments Response options

2.1. Were participants aware of their assigned intervention during the trial?

2.2. Were carers and people delivering the interventions aware of participants' assigned intervention during the trial?

2.3. [If applicable:] If Y/PY/NI to 2.1 or 2.2: Were important non‐protocol interventions balanced across intervention groups?

2.4. [If applicable:] Were there failures in implementing the intervention that could have affected the outcome?

2.5. [If applicable:] Was there non‐adherence to the assigned intervention regimen that could have affected participants’ outcomes?

2.6. If N/PN/NI to 2.3, or Y/PY/NI to 2.4 or 2.5: Was an appropriate analysis used to estimate the effect of adhering to the intervention?

Risk‐of‐bias judgement Low / High / Some concerns

Optional: What is the predicted direction of bias due to deviations from intended interventions? NA / Favours experimental / Favours comparator / Towards null /Away from null / Unpredictable

Domain 3: Missing outcome data

Signalling questions Comments Response options

3.1 Were data for this outcome available for all, or nearly all, participants randomised?

3.2 If N/PN/NI to 3.1: Is there evidence that the result was not biased by missing outcome data?

3.3 If N/PN to 3.2: Could missingness in the outcome depend on its true value?

3.4 If Y/PY/NI to 3.3: Is it likely that missingness in the outcome depended on its true value?

Risk‐of‐bias judgement Low / High / Some concerns

Optional: What is the predicted direction of bias due to missing outcome data? NA / Favours experimental / Favours comparator / Towards null /Away from null / Unpredictable

Domain 4: Risk of bias in measurement of the outcome

Signalling questions Comments Response options

4.1 Was the method of measuring the outcome inappropriate?

4.2 Could measurement or ascertainment of the outcome have differed between intervention groups?

4.3 If N/PN/NI to 4.1 and 4.2: Were outcome assessors aware of the intervention received by study participants?

4.4 If Y/PY/NI to 4.3: Could assessment of the outcome have been influenced by knowledge of intervention received?

4.5 If Y/PY/NI to 4.4: Is it likely that assessment of the outcome was influenced by knowledge of intervention received?

Risk‐of‐bias judgement Low / High / Some concerns

Optional: What is the predicted direction of bias in measurement of the outcome? NA / Favours experimental / Favours comparator / Towards null /Away from null / Unpredictable

Domain 5: Risk of bias in selection of the reported result

Signalling questions Comments Response options

5.1 Were the data that produced this result analysed in accordance with a pre‐specified analysis plan that was finalized before unblinded outcome data were available for analysis?

Is the numerical result being assessed likely to have been selected, on the basis of the results, from...

5.2. ... multiple eligible outcome measurements (e.g. scales, definitions, time points) within the outcome domain?

5.3 ... multiple eligible analyses of the data?

Risk‐of‐bias judgement Low / High / Some concerns

Optional: What is the predicted direction of bias due to selection of the reported result? NA / Favours experimental / Favours comparator / Towards null /Away from null / Unpredictable

Overall risk of bias

Risk‐of‐bias judgement Low / High / Some concerns

Optional: What is the overall predicted direction of bias for this outcome? NA / Favours experimental / Favours comparator / Towards null /Away from null / Unpredictable

Appendix 3. Revised Cochrane risk‐of‐bias tool for cluster‐randomized trials (RoB 2 CRT)

The response options for the signalling questions are:

Yes

Probably yes

Probably no

No information

Not applicable

Domain 1a: Risk of bias arising from the randomisation process

1a.1 Was the allocation sequence random?

1a.2 Was the allocation sequence concealed until clusters were enrolled and assigned to interventions?

1a.3 Did baseline differences between intervention groups suggest a problem with the randomisation process?

Risk‐of‐bias judgement Low / High / Some concerns

Optional: What is the predicted direction of bias arising from the randomisation process? NA / Favours experimental / Favours comparator / Towards null /Away from null / Unpredictable

Domain 1b: Risk of bias arising from the timing of identification or recruitment of participants in a cluster‐randomized trial

1b.1 Were all the individual participants identified and recruited (if appropriate) before randomisation of clusters?

1b.2 If N/PN/NI to 1b.1: Is it likely that selection of individual participants was affected by knowledge of the intervention assigned to the cluster?

1b.3 Were there baseline imbalances that suggest differential identification or recruitment of individual participants between intervention groups?

Risk‐of‐bias judgement Low / High / Some concerns

Optional: What is the predicted direction of bias arising from the timing of identification and recruitment of participants? NA / Favours experimental / Favours comparator / Towards null /Away from null / Unpredictable

Domain 2: Risk of bias due to deviations from the intended interventions (effect of assignment to intervention)

2.1a Were participants aware that they were in a trial?

2.1b. If Y/PY/NI to 2.1a: Were participants aware of their assigned intervention during the trial?