Types of studies

Published or unpublished RCTs that evaluate the effects of any brand of NPWT in the treatment of diabetic foot wounds, irrespective of publication status or language of publication.

Types of participants

Trials recruiting people with Type 1 or Type 2 DM, with foot wounds below the ankle, regardless of underlying aetiology (i.e. ischaemic, neuropathic or neuroischaemic). This includes diabetic foot ulcers, or wounds resulting from amputation or other surgical treatment, or both. We included trials involving people of any age and from any setting.

Where trials with broad inclusion criteria have recruited participants with diabetic foot wounds as part of a larger chronic wound study population e.g. alongside participants with pressure ulcers or leg ulcers, these trials were excluded unless the results for the subgroup of participants with diabetic foot wounds were reported separately or were available from authors on request.

Types of interventions

Any brand of NPWT (including studies that investigated homemade or ad hoc negative pressure devices) compared with standard care (such as advanced wound dressings and gauze) or other treatments, so that NPWT was the only difference between trial arms.

Types of outcome measures

Electronic searches

In July 2013, we searched the following databases to identify reports of RCTs:

• Cochrane Wounds Group Specialised Register (searched 30 July 2013);

• The Cochrane Central Register of Controlled Trials (CENTRAL) (2013, Issue 7);

• The Database of Abstracts of Reviews of Effects (DARE) (2013, Issue 7);

• The NHS Economic Evaluation Database (2013, Issue 7);

• Ovid MEDLINE (1946 to January Week 30 2013);

• Ovid MEDLINE (In‐Process & Other Non‐Indexed Citations July 29, 2013);

• Ovid EMBASE (1974 to 2013 Week );

• EBSCO CINAHL (1982 to 26 July 2013).

The following search strategy was used in the Cochrane Central Register of Controlled Trials (CENTRAL):

#1 MeSH descriptor: [Foot Ulcer] explode all trees437
#2 MeSH descriptor: [Diabetic Foot] explode all trees390
#3 (diabet* near/3 ulcer*):ti,ab,kw 524
#4 (diabet* near/5 (foot or feet)):ti,ab,kw 847
#5 (diabet* near/5 wound*):ti,ab,kw 171
#6 (diabet* near/3 defect*):ti,ab,kw 14
#7 MeSH descriptor: [Amputation] explode all trees299
#8 MeSH descriptor: [Amputation Stumps] explode all trees44
#9 (diabetic near/3 amputat*):ti,ab,kw 31
#10 MeSH descriptor: [Debridement] explode all trees411
#11 (debrid* or slough* or deslough*):ti,ab,kw 1141
#12 #1 or #2 or #3 or #4 or #5 or #6 or #7 or #8 or #9 or #10 or #11 2336
#13 MeSH descriptor: [Negative‐Pressure Wound Therapy] explode all trees62
#14 MeSH descriptor: [Suction] explode all trees719
#15 MeSH descriptor: [Vacuum] explode all trees115
#16 MeSH descriptor: [Drainage] explode all trees1896
#17 ("negative pressure" or negative‐pressure or TNP):ti,ab,kw 502
#18 (sub‐atmospheric or subatmospheric):ti,ab,kw 20
#19 ((seal* next surface*) or (seal* next aspirat*)):ti,ab,kw 17
#20 (wound near/2 suction*):ti,ab,kw 62
#21 ((foam next suction) or (suction next dressing*)):ti,ab,kw 0
#22 (vacuum assisted closure or VAC):ti,ab,kw 234
#23 ((vacuum next therapy) or (vacuum next dressing*) or (vacuum next seal*) or (vacuum next assist*) or (vacuum near closure) or (vacuum next compression) or (vacuum next pack*) or (vacuum next drainage)):ti,ab,kw 162
#24 #13 or #14 or #15 or #16 or #17 or #18 or #19 or #20 or #21 or #22 or #23 87386
#25 #12 and #24 403

We adapted this strategy to search Ovid MEDLINE, Ovid EMBASE and EBSCO CINAHL. We combined the Ovid MEDLINE search with the Cochrane Highly Sensitive Search Strategy for identifying randomised trials in MEDLINE: sensitivity‐ and precision‐maximising version (2008 revision) (Lefebvre 2011). We combined the EMBASE search with the Ovid EMBASE filter developed by the UK Cochrane Centre (Lefebvre 2011). We combined the CINAHL searches with the trial filters developed by the Scottish Intercollegiate Guidelines Network (SIGN 2012). We did not restrict studies with respect to language, date of publication or study setting.

We searched the following clinical trials registries:

• ClinicalTrials,gov (http://www.clinicaltrials.gov/) Febuary 2013;

• WHO International Clinical Trials Registry Platform (http://apps.who.int/trialsearch/Default.aspx) Febuary 2013;

• Current Controlled Trials (http://www.controlled‐trials.com/); Febuary 2013.

Searching other resources

We were keen to explore sources of unpublished data. To maximise identification of unpublished or studies that were not located during the search stage we searched the reference lists of the included studies and of previous systematic reviews. We also examined the content of European Wound Management conference proceedings (2012‐2013) and systematic reviews in the field that might refer to data we had not found, and contacted key manufacturers (KCI, and Smith & Nephew) to ask about unpublished (as well as on‐going) work. We also contacted key authors in the field.

Selection of studies

Two review authors independently assessed the titles and abstracts of retrieved studies for relevance. After this initial assessment, we obtained full copies of all studies felt to be potentially relevant. Two review authors independently checked the full papers for eligibility; disagreements were resolved by discussion and, where required, the input of a third review author. We recorded all reasons for exclusion of studies for which we had obtained full copies. We completed a PRISMA flowchart to summarise this process (Liberati 2009).

Data extraction and management

We extracted and summarised details of the eligible studies using a data extraction sheet. Two review authors extracted data independently and resolved disagreements by discussion, drawing on a third reviewer where required. Where data were missing from reports, we attempted to contact the study authors to obtain this information. We included studies published in duplicate once, but extracted the maximal amount of data. We extracted the following data, where possible:

• country of origin;

• participants' type of DM;

• wound aetiology (e.g. PAD)

• type of wound, including site on foot;

• unit of investigation (per patient) ‐ single wound, or foot, or patient, or multiple wounds on the same patient;

• care setting;

• number of participants randomised to each trial arm;

• eligibility criteria and key baseline participant data;

• details of the dressing/treatment regimen received by each group;

• details of any co‐interventions;

• number of post‐amputation/debridement wounds closed surgically;

• primary and secondary outcome(s) (with definitions);

• outcome data for primary and secondary outcomes (by group);

• duration of follow‐up;

• number of withdrawals (by group);

• adverse events;

• publication status of study; and,

• source of funding for trial.

Assessment of risk of bias in included studies

Two review authors independently assessed each included study using the Cochrane Collaboration tool for assessing risk of bias (Higgins 2011). This tool addresses six specific domains, namely, sequence generation, allocation concealment, blinding, incomplete outcome data, selective outcome reporting and other issues (e.g. extreme baseline imbalance, issues with unit of investigation). We assessed blinding of participants and health professionals, and blinded outcome assessment separately. We were aware that blinding of participants and health professionals to treatment received would not be possible, but it was important to understand if, and how, studies had compensated for this where required, i.e. where outcomes such as wound closure and amputation could be at risk of performance bias. We completed a 'Risk of Bias' table for each eligible study. Disagreements about risk of bias assessment were resolved by discussion. Where possible, when a lack of reported information resulted in an unclear decision, authors were contacted for clarification.

We classified trials as being at high risk of bias if they were rated 'high' for one or more of three key criteria, namely, randomisation sequence, allocation concealment and blinded outcome assessment. We also considered the potential for performance and measurement bias for each primary and secondary outcome extracted.

Measures of treatment effect

Where possible, studies were grouped according to wound type. Where possible, we presented the outcome results for each trial with 95% confidence intervals (CI). We reported estimates for dichotomous outcomes (e.g. ulcers healed during a particular time period) as risk ratios (RR). We used the RR rather than odds ratio (OR), since, when event rates are high, as is the case for many trials reporting wound healing, ORs (when interpreted as RR) can give an inflated impression of the effect size (Deeks 2002). We planned to report outcomes relating to continuous data (e.g. percentage change in ulcer area) as mean differences (MD) and overall effect size (with 95% CI). Where a study reported data on time‐to‐healing (the probability of healing over a consecutive time period) we planned to report and plot these data (where possible) using hazard ratio estimates. However, where the hazard ratio was not reported, but data regarding the number of events and the P value for a log rank test (reported to at least two significant figures) were reported, we employed methods proposed by Parmar 1998 to calculate the hazard ratio indirectly. Where log rank test P values were published to only one significant figure the robustness of the calculated hazard ratio for the highest possible P value was investigated to test robustness of estimates. Hazard ratios and associated 95% CIs were then calculated using the inverse variance option in RevMan (RevMan 2011).

Unit of analysis issues

We recorded whether trials presented outcomes in relation to a wound, a foot, a participant or as multiple wounds on the same participant. We also recorded occasions where multiple wounds on a participant were (incorrectly) treated as independent within a study, rather than having within‐patient analysis methods applied. This was recorded as part of the risk of bias assessment. For wound healing and amputation, unless otherwise stated, where the number of wounds appeared to equal the number of participants, we treated the wound as the unit of analysis. For other adverse event outcomes, in order to facilitate further analyses, we aimed to establish whether data were presented at the level of the participant, because in this area there is potential for data to refer to multiple events occurring to a single person (or wound per person), which means that data cannot be analysed further without violating the assumption of independence.

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. In individual studies, where data on the proportion of ulcers healed were presented, we assumed that if randomised participants were not included in an analysis, their wound did not heal (i.e. they would be considered in the denominator but not the numerator). Where a trial did not specify participant group numbers prior to drop‐out, we presented only complete case data. In a time‐to‐healing analysis using survival analysis methods, drop‐outs should be accounted for as censored data. Hence all participants contributed to the analysis. Such analysis assumes that drop‐outs are missing at random (i.e. not associated with time‐to‐healing). We present data for area change, and for all secondary outcomes, as a complete case analysis.

Assessment of heterogeneity

We considered both clinical and statistical heterogeneity. Wherever appropriate, that is, where studies appeared similar in terms of wound type, intervention type, duration and outcome type, we planned to pooled data using meta‐analysis (conducted using RevMan 5.1 (RevMan 2011)). We planned to assess statistical heterogeneity using the Chi² test (a significance level of P less than 0.1 was considered to indicate heterogeneity) and the I² estimate (Higgins 2003). The I² estimate examines the percentage of total variation across studies due to heterogeneity rather than to chance. Values of I² higher than 50% indicate a high level of heterogeneity. In the absence of clinical heterogeneity and in the presence of statistical heterogeneity (I² over 50%), we envisioned using a random‐effects model, however, we did not anticipate pooling studies where heterogeneity was very high (I² over 75%). Where there was no clinical or statistical heterogeneity we used a fixed‐effect model.

Data synthesis

We combined studies using a narrative overview, with meta‐analyses of outcome data where appropriate (in RevMan 5). The decision to include studies in a meta‐analysis depended on the availability of treatment effect data and assessment of heterogeneity. For time‐to‐event data, we planned to use the inverse variance method on the estimated hazard ratio and standard error, when reported or calculated from available data.

Where relevant, and possible, we planned to conduct sensitivity analyses to investigate the potential impact of studies at high risk of bias on pooled results.

Subgroup analysis and investigation of heterogeneity

We considered whether there was potential heterogeneity between wounds types, i.e. foot ulcers and surgical wounds resulting from surgical debridement of an ulcer, or amputation on any part of a diabetic foot. Where there was evidence of between‐trial heterogeneity in trial‐level co‐interventions, especially off‐loading, we envisaged a sub‐group analysis being conducted based on variations in co‐interventions, e.g. all trial participants reported to receive adequate offloading protocol/advice being compared with trial participants who received unclear advice about offloading ‐ however, this was not required. Finally, depending on the number and heterogeneity of included studies, we considered using meta‐regression to investigate wound aetiology as a possible explanatory variable.