Types of study designs

We will include reports of prospective and retrospective longitudinal studies that investigate whether the prognostic factor, protease activity, is associated with healing of VLUs. We will not include studies that examine prediction models. We will include RCTs analysed as cohort studies, provided interventions are taken into account in the analysis. We will include case‐control studies if there are no cohort studies for each type of protease. We will not include cross‐sectional studies or case reports. We will include studies with any follow‐up period.

We will consider evidence separately within the different phases of prognostic factor investigation: Phase 1 (exploratory), and Phase 2 (confirmatory) studies, which provide different levels of evidence (Hayden 2008). Exploratory studies identify associations of many potential prognostic factors and outcomes. These studies measure associations between single factors and the outcome, ('univariable' or 'univariate' associations) and provide the least conclusive information regarding the independence of a variable as a valid prognostic factor. Confirmatory studies aim to measure the independent effect of a prognostic factor on the relevant outcome while controlling for other factors. We will include all eligible studies in the review but, if possible, we will restrict the results summary and any meta‐analyses to results from multivariable analyses. Failing this, we will include the results of univariable analyses and will take account of the phase of investigation in the 'Risk of bias' assessment and GRADE rating.

Types of participants

We will include studies that include people with a VLU, who are managed in any care setting. We expect the method of diagnosis of venous ulceration to vary, so we will accept definitions as used in the included studies.

We will include studies in people with VLUs, alongside people with other types of wounds (e.g. arterial ulcers, pressure ulcers, diabetic foot ulcers), provided the results for people with venous ulcers are presented separately, or if most participants (at least 75%) had leg ulcers of venous aetiology. Where wounds are described only as leg ulcers without information as to aetiology, we will assume they are venous in origin because this is the most common type of leg ulcer.

We will include studies that involve participants at any stage in their treatment pathway, and we will record, where available, baseline data on the time since diagnosis and treatments previously given, together with ongoing treatments. We will include studies that involve participants with any infection status at baseline: we will record any available data on this.

We will exclude studies conducted solely in vitro and animal studies.

Types of prognostic factors: defining protease activity

The prognostic factor will be protease activity. Any type of protease or combination of proteases will be eligible, either from the host or from bacteria, including:

• MMPs: gelatinases MMP‐2 and MMP‐9; collagenases MMP‐1, MMP‐8 and MMP‐13; stromelysins MMP‐3, MMP‐10 and MMP‐11; metalloelastase MMP‐12; matrilysins MMP‐7 and MMP‐26; membrane‐type MMPs MMP‐14, MMP‐15, MMP‐16, MMP‐17, MMP‐24 and MMP‐25; and other MMPs MMP‐19, MMP‐20, MMP‐23 and MMP‐28;

• serine proteases: mast cell tryptases and chymases; human nucleophil elastase; cathepsin G; plasmin; u‐PA; tissue‐activating plasminogen thrombin (t‐PA);

• proteases combined with other biological species: ADAM; ADAMTS.

Where possible, we will combine these three major categories in the analyses, taking into consideration cut‐off points used (see 'Data synthesis' section below).

We will also include and report separately studies that investigate as biomarkers the ratio of proteases and their respective inhibitors, e.g. MMP‐2 (protease) and TIMP‐2 (inhibitor).

The prognostic factor may be examined as a continuous or categorical variable and any cut‐off point will be permitted.

We will permit any approach to obtaining samples from wounds, and any method of measurement of protease activity. We will not include measures of proteases in the blood.

We will report all unadjusted (simple) and adjusted (multivariable) associations between the prognostic factor and healing, with details on any adjustment factors used.

Types of outcome measures

Study selection

Two review authors will independently assess the titles and abstracts of retrieved records against the inclusion criteria. We will obtain all potentially relevant studies in full and two review authors will independently assess these for eligibility. We will resolve any disagreements at each stage through discussion and, where appropriate, we will consult a third review author. Where there is doubt over the eligibility of a study, we will attempt to contact the study authors in order to resolve the uncertainty. Where studies are reported in multiple publications/reports, we will obtain all publications. Whilst we will include a study only once in the review, we will extract data from all reports to ensure we obtain all available relevant data. The study selection process will be illustrated in a PRISMA diagram. All studies excluded after full‐text assessment will be listed in a ‘Characteristics of excluded studies’ table with their reasons for exclusion.

Data extraction and management

We will collect all associations reported between the prognostic factor and outcomes, with details on any adjustment factors used.

We will extract and record data from included studies using a data extraction sheet. One review author will extract data, which will then be checked by a second review author. If key data are missing from reports we will attempt to contact the study authors.

We will extract the following data for the pre‐specified prognostic factors and outcomes in this review. We will collect outcome data as described in the ‘Types of outcome measures’ section.

We will extract the following data:

• country and setting in which study was conducted;

• study design;

• eligibility criteria;

• participant details;

• ulcer details, including duration of ulcer, ulcer size, number of ulcers and ulcer history, and ulcer severity (with the system used to classify this) as reported by the study authors;

• treatment details, including compression and debridement (including type and frequency);

• method of obtaining wound fluid for prognostic factor measurement, including duration of collection;

• method of measurement of protease activity (e.g. Gelatin zymography);

• details of each prognostic factor: type of protease/combination of factors, measurement of prognostic factor, type of data (e.g. continuous/any cut‐off points and if so, whether they were pre‐defined and what the justification was for that cut‐off point);

• details of each outcome: measurement, type of data (e.g. continuous/any cut‐off points);

• duration of follow‐up;

• type of analysis: e.g. explanatory/confirmatory (including the presence of a pre‐defined protocol and study registration); logistic regression/Cox regression;

• any adjustment factors considered in the analysis;

• association statistics for each prognostic factor for primary and secondary outcomes (e.g. odds ratios (ORs), hazard ratios (HRs) and their confidence intervals (CIs)/variances/standard errors);

• loss to follow‐up, and reasons.

Assessment of risk of bias in included studies

Two review authors will independently appraise the included studies using a standardised approach. In the case of discrepancies, the review authors will attempt to reach consensus; if necessary a third review author will resolve any disagreements. The review authors will not be blinded to study authors, institution or journal of publication due to feasibility.

We will assess risk of bias using an approach based on the Quality In Prognosis Studies (QUIPS) tool, which is appropriate for prognostic factor review questions (Hayden 2013; Hayden 2014). Our approach also draws on the ROBINS I tool (ROBINS‐I 2017) which assesses the risk of bias for non‐randomised intervention studies (Sterne 2016). We have provided further details in Appendix 3. This approach assesses risk of bias per study for each prognostic factor‐outcome combination, by considering six domains: study participation (selection bias), study attrition, prognostic factor measurement, outcome measurement, adjustment, and statistical analysis and reporting. Each domain is rated as having high, moderate or low risk of bias. We will define an all‐domain risk of bias per study for each prognostic factor‐outcome combination, taking into account all of the above domains, but especially focusing on the key domains of selection bias, study attrition, adjustment, and statistical analysis and reporting. As a guide, we will assign risk of bias as follows: low risk of bias if all (or all key) domains have low risk of bias; moderate risk of bias if there is high risk of bias for one key domain or moderate risk of bias for at least two key domains (with the rest as low risk of bias); high risk of bias if there is high risk of bias for at least two key domains. We will perform sensitivity analysis considering only studies at low all‐domain risk of bias (Hayden 2013).

Measures of association

We will extract all unadjusted and adjusted measures of association from included studies and convert effect sizes, as necessary, in order to avoid possible selection bias, thus allowing us to use data from as many studies as possible. HRs are the preferred measure of the relationship between individual prognostic factors and healing outcomes, and we will separately extract and analyse HRs for studies providing this measure of association. However, we anticipate that results will also be reported as ORs and RRs; we will use ORs as the common measure of the association, and RRs and HRs to estimate ORs at a particular time point (Symons 2002).

For consistency, we will re‐calculate associations to be in the same direction, as necessary, with associations above 1 indicating better prognosis for positive binary outcomes (e.g. a healed wound). We will appropriately transform the individual study associations (e.g. ORs) and their 95% confidence limits to their natural logarithms to normalise their distributions and then will calculate the standard error. We will contact study authors regarding missing or unusable data, as necessary.

Where the outcomes are reported as continuous measures (e.g. change in ulcer size), we will analyse the regression coefficients with their standard errors.

If we cannot obtain OR values from the published papers or from the study authors, we will report correlation coefficients or any other measure of association.

Unit of analysis issues

The prognostic factor (protease activity) and outcome (complete wound healing) are both considered at the ulcer level. A possible unit of analysis issue could arise when there is more than one ulcer per person and multivariable analysis has been conducted with adjustment factors measured at the individual level (e.g. patient age). This represents clustered data and analyses should be conducted using hierarchical methods. Where studies include clustered data of this type we will report this, noting whether data are analysed correctly. We will record this as part of the 'Risk of bias' assessment. We will consider including such studies in any meta‐analysis, taking into account the associated risk of bias.

Dealing with missing data

We will include studies that investigate the relationship between protease activity and healing regardless of whether or not there are missing data and even if limited evidence is provided about the size of the effect (for example, if the factor is mentioned only as being ’non‐significant’ in the analyses). If necessary, we will contact study authors for clarification and to attempt to retrieve any missing information.

Assessment of heterogeneity

We will consider the clinical heterogeneity of included studies based on the population, measure of the prognostic factor, cut‐off points used, outcome measurement and methodological heterogeneity due to study design/potential biases. We will synthesise associations as appropriate within clinically‐relevant subgroups, grouping studies regardless of type of protease, duration of ulcer and presence of infection. We will examine the latter two features in subgroup analyses where there is heterogeneity.

Reporting bias

We will examine publication bias for each meta‐analysis, provided there are 10 or more studies, by visually examining asymmetry on funnel plots and testing for asymmetry at the 10% level, using Egger’s test for HRs, and Peters’ test for ORs (Sterne 2011).

Data synthesis

Synthesising data in prognostic factor studies requires us to recognise the many different ways of reporting and analysing results, and, where possible, to request alternative data or to transform results to a common format (see 'Measures of association' section). We will consider the impact of data transformations using sensitivity analyses.

We expect that most studies will measure and present data about prognostic factors for the healing of VLUs, such that the outcome variable is analysed as dichotomous data, but the prognostic factor may be reported as continuous data (e.g. protease activity) or values may be dichotomised with different cut‐off points or there may be more than two categorical levels to the prognostic factor.

After carrying out any appropriate transformations, where possible we will group together studies with similar prognostic factor cut‐off points/similar analytical approaches, and will represent results for different analytical approaches as subgroups on a forest plot for that prognostic factor‐outcome combination, even if we do not conduct a meta‐analysis. Where possible and appropriate, we will conduct meta‐analyses. We will include in the forest plots details of any adjustment factors considered in the study analyses.

We will conduct meta‐analysis if valid data are available assessing associations between individual prognostic factors and an outcome of interest for sufficiently homogeneous subgroups of studies. We will define 'sufficiently homogeneous' subgroups according to population, measures of the prognostic factor and outcome measurement.

For prognostic factors that are analysed as continuous measures, we will combine the data for all proteases, regardless of the source or type of protease; for prognostic factors analysed as dichotomous measures, we will take cut‐off points into consideration as described above. We will analyse separately ratios of proteases and their inhibitors.

We will conduct separate meta‐analyses of HRs and ORs (at similar follow‐up points), and will also transform the measures, where possible, so that one analysis (OR) can be conducted. We will conduct meta‐regression analysis if there are more than 10 studies providing sufficient data.

We will conduct meta‐analyses using STATA (StataCorp version 14) with a random‐effects restricted maximum likelihood (REML) meta‐analysis model, which accounts for any between‐study heterogeneity in the prognostic effect (Cornell 2014). Such heterogeneity is common in prognostic factor studies. Unless the heterogeneity (as assessed above) is too extensive for appropriate pooling, we will summarise the meta‐analysis by the pooled estimate (the average prognostic factor effect), its Hartung‐Knapp 95% CI, the estimate of Tau² (between‐study variance), and a 95% prediction interval for the prognostic effect in a single population (Riley 2011).

'Summary of findings' table and GRADE assessment

We will use an approach modified from the GRADE framework (Guyatt 2011a) to assess the overall quality (certainty) of evidence for each prognostic factor‐outcome combination (Hayden 2014;Iorio 2015). We will rate the overall strength of evidence as either ‘high’, ‘moderate’, ‘low’ or ‘very low’ considering the phase of prognostic study (confirmatory/explanatory or exploratory), the within‐study risk of bias, the directness of evidence, heterogeneity, precision of effect estimates and risk of publication bias (Iorio 2015;Schünemann 2011b). We will also consider two 'upgrading' factors ‐ large effect and dose effect ‐ although we note that high risk of bias may artificially lead to large effects (see Appendix 4 for further details).

We will present the main results of the review in 'Summary of findings' tables. These tables present key information concerning the certainty of the evidence, the magnitude of the associations examined, and the sum of the available data (Schünemann 2011a). 'Summary of findings' tables also include an overall grading of the evidence. This defines the certainty 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.

We will present evidence for the various prognostic factors in rows in 'Summary of findings' tables.

If it is not appropriate to combine results using meta‐analysis (too much clinical heterogeneity), we will present the results qualitatively, considering the strength and consistency of results using the following schema:

• strong evidence of effect: consistent findings (defined as > 75% of studies showing the same direction of effect) in multiple low risk of bias studies;

• moderate evidence of effect: consistent findings in multiple high risk of bias and/or one study with low risk of bias;

• limited evidence of effect: one study available;

• conflicting evidence of effect: inconsistent findings across studies;

• no effect: no association between patient expectations and the outcome of interest.

We will calculate absolute risk differences for the effect of the prognostic factor using estimates of baseline risk from the literature where possible.

Subgroup and sensitivity analyses

We will use sensitivity analyses to explore the impact of study all‐domain risk of bias, firstly restricting the analysis to studies rated as having low risk of bias, and if this is not feasible, restricting to low or moderate risk of bias.

If there is heterogeneity, we will investigate it using the following pre‐specified subgroup analyses, provided there are at least two studies per subgroup:

• baseline duration of ulcer (up to 24 weeks; ≥ 24 weeks); duration may be a proxy for a non‐healing wound, in which protease activity may be associated differently with healing;

• presence or absence of infection.

We will not conduct subgroup analyses by type of protease because this would introduce too high a level of complexity for this review, but will record the type of protease measured.

We will consider subgroup or sensitivity analyses to explore the impact of types of measurement approaches for assessing prognostic factors.

This Methods section is based on the exemplar Cochrane prognosis review protocol for prognostic factors (Hayden 2014).