Alternating pressure (active) air surfaces for preventing pressure ulcers

Objectives

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

To assess the effects of alternating pressure (active) air beds or mattresses compared with any alternative surface on the incidence of pressure ulcers in any setting and population.

Background

Description of the condition

Pressure ulcers (also known as pressure injuries, pressure sores, decubitus ulcers and bed sores) are localised injuries to the skin or underlying soft tissue, or both, caused by unrelieved pressure, shear or friction (NPIAP 2016). Pressure ulcer severity is generally classified using the National Pressure Injury Advisory Panel (NPIAP) system (NPIAP 2016).

• Stage 1: intact skin with a local appearance of non‐blanchable erythema

• Stage 2: partial‐thickness skin loss with exposed dermis

• Stage 3: full‐thickness skin loss

• Stage 4: full‐thickness skin and tissue loss with visible fascia, muscle, tendon, ligament, cartilage or bone

• Unstageable pressure injury: full‐thickness skin and tissue loss that is obscured by slough or eschar so that the severity of injury cannot be confirmed.

• A deep tissue pressure injury: local injury of persistent, non‐blanchable deep red, maroon, purple discolouration or epidermal separation revealing a dark wound bed or blood‐filled blister.

These above stages of pressure ulcer are consistent with those described in another commonly used system: the International Classification of Diseases for Mortality and Morbidity Statistics of the World Health Organization 2019.

Pressure ulcers are relatively common, complex wounds affecting people across different care settings. A systematic review found that prevalence estimates for people affected by pressure ulcers in communities of the UK, USA, Ireland, and Sweden ranged from 5.6 to 2300 per 10,000 depending on the nature of the population surveyed (Cullum 2016), and a subsequent cross‐sectional survey of people receiving community health services in one city in the UK estimated that 1.8 people per 10,000 have a pressure ulcer (Gray 2018).

Pressure ulcers confer a heavy burden in terms of personal impact and health service resource use. Having a pressure ulcer may impair physical, social and psychological activities (Gorecki 2009). Ulceration impairs health‐related quality of life (Essex 2009); can result in longer institution stays (Theisen 2012); and increases the risk of systemic infection (Espejo 2018). There are also substantial impacts on health systems: a 2015 systematic review of 14 studies across a range of care settings in Europe and North America, showed that pressure ulcer‐related treatment costs ranged between EUR 1.71 and 470.49 per person, per day (Demarré 2015). In the UK, the annual average National Health Service cost attributable to managing one person with a pressure ulcer in the community was estimated to be GBP 1400 for a Stage 1 pressure ulcer and more than GBP 8500 for more severe stages (2015/2016 prices; Guest 2018). In Australia, the annual cost of treating pressure ulcers was estimated to be AUD 983 million (95% confidence interval (CI) 815 to 1151 million) at 2012/13 prices (Nguyen 2015). The serious consequences of pressure ulceration have led to an intensive focus on their prevention.

Description of the intervention

Pressure ulcers are considered largely preventable. Support surfaces are specialised medical devices designed to relieve or redistribute pressure on the body, or both, in order to prevent pressure ulcers (NPIAP S3I 2007). Types of support surface include, but are not limited to, integrated bed systems, mattresses and overlays (NPIAP S3I 2007).

Classification of support surface type can now be based on the NPIAP Support Surface Standards Initiative (S3I) terms and definitions related to support surfaces (NPIAP S3I 2007). According to the NPIAP S3I terms and definitions support surfaces may:

• be powered (i.e. require electrical power to function) or non‐powered;

• passively redistribute body weight (i.e. reactive pressure redistribution), or mechanically alternate the pressure on the body to reduce the duration of pressure (i.e. active pressure redistribution);

• be made of a range of materials including but not limited to: air‐cells, foam materials, fibre materials, gel materials, sheepskin for medical use, and water‐bags;

• be constructed of air‐filled cells that have small holes on the surface for blowing out air to dry skin (i.e. low‐air‐loss feature) or have fluid‐like characteristics via forcing filtered air through ceramic beads (i.e. air‐fluidised feature), or have neither of these features.

Full details of support surface classifications are listed in Appendix 1. A widely used type of support surface is the alternating pressure (active) air bed or mattress (traditionally termed alternating pressure, or dynamic air bed or mattress). Examples of types of alternating pressure air beds or mattresses include:

• powered active air mattresses (e.g. Nimbus II, MicroPulse, large‐celled ripple);

• powered active low‐air‐loss mattresses;

• powered hybrid system air mattresses (e.g. TheraPulse);

• powered hybrid system low‐air‐loss mattresses.

These mattresses are made of air‐cells that intermittently inflate and deflate via electrically powered pumps (Clark 2011; NPIAP S3I 2007). Additionally, these active, alternating pressure air mattresses can have an integrated reactive element to create so‐called 'hybrid' mattresses (Fletcher 2015). Alternating pressure (active) air mattresses can have low‐air‐loss features designed to influence the microclimate environment by keeping the skin dry (since moisture is thought to potentially increase friction on skin and increase the risk of skin damage) (Clark 2011;Wounds International 2010).

How the intervention might work

Support surfaces that can prevent pressure ulceration aim to redistribute pressure beneath the body, increasing blood flow to tissues and relieving skin and soft tissue distortion (Wounds International 2010). Active support surfaces achieve pressure redistribution by frequently changing the points of contact between the surface and body, reducing the duration of the pressure applied to specific anatomical sites (Clark 2011; NPIAP S3I 2007). This contrasts with the mode of action of reactive support surfaces, which is more passive and includes immersion (i.e. 'sinking' of the body into a support surface) and envelopment (i.e. conforming of a support surface to the irregularities in the body); these devices distribute the pressure over a greater area, thereby reducing the magnitude of the pressure at specific sites (Clark 2011).

Why it is important to do this review

Support surfaces are widely used for pressure ulcer prevention and are the focus of recommendations in international and national guidelines (EPUAP/NPIAP/PPPIA 2019; NICE 2014). Since the publication of the Cochrane Review, 'Support surfaces for pressure ulcer prevention' (McInnes 2015), there has been a substantial increase in the number of relevant randomised controlled trials (RCTs) published; recognition of the NPIAP S3I 2007 terms and definitions related to support surfaces, and new Cochrane methodological requirements, such as the use of GRADE assessments (Guyatt 2008). These developments mean that it is important to update this review.

In considering this update we took into account the size and complexity of 'Support surfaces for pressure ulcer prevention' (McInnes 2015), which includes all support surface types. An alternative approach is to split the review into multiple new titles each with a narrower focus. We consulted on this splitting option via an international survey in August 2019. The potential new titles suggested were based on clinical use, the new terms and definitions related to support surfaces (NPIAP S3I 2007), a relevant network meta‐analysis (Shi 2018a), and current Clinical Practice Guidelines (EPUAP/NPIAP/PPPIA 2019; NICE 2014). We received responses from 29 health professionals involved in pressure ulcer prevention activity in several countries (Australia, Belgium, China, Italy, the Netherlands and the UK). In total 83% of respondents supported splitting the review into suggested titles and 17% were unsure (no respondent voted against splitting). The proposed new review titles are:

• Alternating pressure (active) air surfaces for preventing pressure ulcers;

• Foam surfaces for preventing pressure ulcers;

• Reactive air surfaces for preventing pressure ulcers; and

• Alternative reactive support surfaces (non‐foam or air‐filled) for preventing pressure ulcers.

We will bring the results of these new reviews together in an overview with a network meta‐analysis (Salanti 2012), in order to compare simultaneously all support surfaces and to rank them based on the probabilities of each being the most effective for preventing pressure ulcers.

This particular review will compare alternating pressure (active) air beds or mattresses with any alternative surface.

Objectives

To assess the effects of alternating pressure (active) air beds or mattresses compared with any alternative surface on the incidence of pressure ulcers in any setting and population.

Methods

Criteria for considering studies for this review

Types of studies

We will include published and unpublished RCTs (including multi‐arm studies, cluster‐RCTs and cross‐over trials) in the review, regardless of the language in which they are reported. We will exclude studies using quasi‐random allocation methods (e.g. alternation).

Types of participants

We will include studies in any populations including those defined as being at risk of ulceration, as well as those with existing pressure ulcers at baseline (when the study measured pressure ulcer incidence).

Types of interventions

This review focuses on alternating pressure (active) air beds or mattresses in general. Eligible experimental interventions must include a specific bed or mattress having the capability of active pressure redistribution (or alternating pressure), which include, but are not limited to, specific active mattresses identified in Shi 2018a, that is:

• powered active air mattresses; or

• powered active low‐air‐loss mattresses; or

• powered hybrid system air mattresses; or

• powered hybrid system low‐air‐loss mattresses.

In this review we will consider hybrid mattresses to be systems that incorporate both active and reactive pressure redistribution modes into a stand‐alone unit and can apply either of the two modes as patients require. This is because, for people using such hybrid mattresses, pressure ulcer risk reduction may result from both modes that are interchangeably practised over time, rather than being the result of constantly applying a single mode.

We will include studies where two or more support surfaces are used sequentially over time or in combination where the support surface(s) of interest are included in one of the study arms.

We will include studies comparing eligible beds or mattresses against any comparator that can be defined as a support surface (e.g. reactive air surfaces, foam mattresses, reactive gel surfaces). We will include studies in which co‐interventions (e.g. repositioning) are delivered, provided that we can assume that the co‐interventions between a study’s arms are the same (i.e. interventions randomised are the only systematic difference).

Types of outcome measures

We will consider the following primary and secondary outcomes. If a study is otherwise eligible (i.e. eligible study design, participants and interventions) but does not report any review‐relevant outcomes, we will contact the study authors where possible to clarify whether they measured a relevant outcome but did not report it. We will consider the study in 'Studies awaiting classification' if we cannot establish whether it measured an outcome or not. We will exclude the study if the study authors confirm that they did not measure any review‐relevant outcomes.

For a study that measured an outcome at multiple time points, we will consider outcome measures at three months as the primary interest of this review (Schoonhoven 2007), regardless of the time points specified as being of primary interest by the study. If the study does not report three‐month outcome measures, we will consider those closest to three months in this review. Where a study only reports a single time point, we will consider the reported one in this review. Where the study does not specify a time point for their outcome measurement, we will assume that its time point is the length of follow‐up.

Primary outcomes

We will include studies that report pressure ulcer incidence as the clinical outcome measure. We will record two outcome measures (the proportion of participants developing a new pressure ulcer; and time to pressure ulcer incidence) where available. However, we will consider the proportion of participants developing a new pressure ulcer as the primary outcome for this review. Time to pressure ulcer incidence is our preferred measure, however we do not expect it to be reported in many studies; we will extract and analyse time‐to‐event data but focus on the binary outcome in our conclusions. We will accept any of the outcome definitions reported in included studies regardless of which pressure ulcer severity classification system they use to measure or grade new pressure ulcers. We will also consider the outcome of pressure ulcer incidence irrespective of whether studies report ulcers by stages or as a non‐stratified figure.

We will not consider subjective outcome measures (e.g. 'better' or 'worse' skin condition) as measures of pressure ulcer incidence.

Secondary outcomes

• Patient support‐surface‐associated comfort. We will consider patient comfort outcome data in this review only if the evaluation of patient comfort is pre‐planned and is systematically conducted across all participants in the same way in a study. The definition and measurement of this outcome may vary from one study to another; for example, the proportion of participants who report comfort, or comfort measured by a scale with continuous (categorical) numbers. We will include these data with different measurements in separate meta‐analyses.

• All reported adverse events (measured using survey/questionnaire/data capture process or visual analogue scale). Study authors should specify a clear method for collecting adverse event data; and where available, we will extract data on all serious and all non‐serious adverse events as an outcome. This methodology should make it clear whether events were reported at the participant level or whether multiple events per person were reported, in which case appropriate adjustments need to be made for data clustering (Higgins 2019a). We will consider the assessment of any event in general defined as adverse by participants, health professionals, or both.

• Health‐related quality of life (measured using a standardised generic questionnaire such as EQ‐5D (Herdman 2011), 36‐item Short Form (SF‐36; Ware 1992), or pressure ulcer‐specific questionnaires such as the PURPOSE Pressure Ulcer Quality of Life (PU‐QOL) questionnaire (Gorecki 2013), at noted time points). We will not include ad hoc measures of quality of life because these measures are unlikely to be validated.

• Cost effectiveness: within‐trial cost‐effectiveness analysis comparing mean differences in effects with mean cost differences between the two arms: we will extract data on incremental mean cost per incremental gain in benefit (incremental cost‐effectiveness ratio (ICER)). We will also consider other measures of relative cost‐effectiveness (e.g. net monetary benefit, net health benefit).

Search methods for identification of studies

Electronic searches

We will search the following databases to retrieve reports of relevant trials:

• the Cochrane Wounds Specialised Register (to present);

• the Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library (to latest issue);

• Ovid MEDLINE (from 1946 to present);

• Ovid Embase (from 1974 to present);

• EBSCO CINAHL Plus (Cumulative Index to Nursing and Allied Health Literature; from 1937 to present).

We have devised a draft search strategy for CENTRAL, which is displayed in Appendix 2. We will adapt this strategy to search the Cochrane Wounds Specialised Register, Ovid MEDLINE, Ovid Embase and EBSCO CINAHL Plus. We will combine 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 2019). We will combine the Embase search with the Ovid Embase filter terms developed by the UK Cochrane Centre (Lefebvre 2019). We will combine the CINAHL Plus search with the randomised trial filter developed by the Scottish Intercollegiate Guidelines Network (SIGN 2019). We will not restrict the searches with respect to language, date of publication or study setting.

We will also search the following clinical trials registries for ongoing studies and completed studies that may not have been published:

• ClinicalTrials.gov (clinicaltrials.gov);

• World Health Organization (WHO) International Clinical Trials Registry Platform (apps.who.int/trialsearch).

Searching other resources

For previous versions of this review we contacted experts in the field of wound care to enquire about potentially relevant, ongoing, and recently published, studies. In addition, we contacted manufacturers of support surfaces for details of any studies they were conducting. This approach did not yield any additional studies therefore we will not repeat it.

We will try to identify other potentially eligible studies or ancillary publications by searching the reference lists of retrieved included studies, as well as relevant systematic reviews, meta‐analyses and health technology assessment reports.

Data collection and analysis

Selection of studies

One review author (CS) will re‐check the RCTs included in the original version of this review for eligibility. Two review authors will independently assess the titles and abstracts of the new search results for relevance using Covidence and then independently inspect the full text of all potentially eligible studies. The two review authors will resolve disagreements by discussion and by involving a third review author if necessary.

Data extraction and management

One review author (CS) will check data from the studies included in the original version of this review and extract additional data where necessary. A second review author will check any new data extracted.

For new included studies, two review authors will independently extract data. Any disagreements will be resolved by discussion and, if necessary, with the involvement of a third review author. Where necessary, we will contact the authors of included studies to clarify data.

We will extract the following data using a pre‐prepared data extraction form:

• basic characteristics of studies (first author, publication type, publication year, and country);

• funding sources;

• care setting;

• characteristics of participants (trial eligibility criteria, average age in each arm or in a study, proportions of participants by gender, and participants’ baseline skin status);

• support surfaces being compared (including their descriptions);

• details on any co‐interventions;

• follow‐up duration;

• the number of participants enrolled;

• the number of participants randomised to each arm;

• the number of participants analysed;

• participant withdrawals with reasons;

• the number of participants developing new ulcers (by ulcer stages where possible);

• time to pressure ulceration outcome data;

• patient support‐surface‐associated comfort;

• adverse event outcome data;

• health‐related quality of life outcome data; and

• cost‐effectiveness outcome data.

We will classify specific support surfaces in the included studies into intervention groups using the NPIAP S3I terms and definitions related to support surface (NPIAP S3I 2007). Therefore, to accurately assign specific support surfaces to intervention groups, we will extract full descriptions of support surfaces from included studies, and when necessary will supplement the information with that from external sources such as other publications about the same support surface, manufacturers’ or product websites, and expert clinical opinion (Shi 2018b).

Assessment of risk of bias in included studies

Two review authors will independently assess risk of bias of each included study using the Cochrane 'Risk of bias' tool (see Appendix 3). This tool has seven specific domains: sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete data (attrition bias), selective outcome reporting (reporting bias), and other issues (Higgins 2017). We will assess performance bias, detection bias, and attrition bias for each of the review outcomes separately (Higgins 2017). We note that it is often impossible to blind participants and personnel in device trials. In this case, performance bias may be introduced if knowledge of treatment allocation results in deviations from intended interventions, differential use of co‐interventions or care between groups not specified in the study protocol that may influence outcomes. We will attempt to understand if, and how, included studies compensated for challenges in blinding, for example, implementing strict protocols to maximise consistency of co‐interventions between groups to reduce the risk of performance bias. We also note that pressure ulcer incidence is a subjective outcome; and compared with blinded assessment, non‐blinded assessment of subjective outcomes tends to be associated with more optimistic effect estimates of experimental interventions in RCTs (Hróbjartsson 2012). Therefore, we will judge non‐blinded outcome assessment as high risk of detection bias. In this review we will include the issues of differential diagnostic activity and unit of analysis under the domain of 'other issues'. For example, unit of analysis issues will occur where a cluster‐randomised trial has been undertaken but analysed at the individual level in the study report.

For the studies included in the previous version of this review, one review author (CS) will check the 'Risk of bias' judgements and, where necessary, update them. A second review author will check any updated judgement. We will assign each 'Risk of bias' domain a judgement of high, low, or unclear risk of bias. We will resolve any discrepancy between two review authors by discussion and by involving a third review author where necessary. Where possible, when a lack of reported information results in a judgement of unclear risk of bias, we will contact study authors for clarification.

We will present our assessment of risk of bias for the proportion of participants developing a new pressure ulcer outcome using two 'Risk of bias' summary figures; one will be a summary of bias for each item across all studies, and the second will show a cross‐tabulation of each study by all of the 'Risk of bias' items.

Once we have given our judgements for all 'Risk of bias' domains, we will judge the overall risk of bias for each outcome across studies as:

• low risk of bias, if we judge all domains to be at low risk of bias;

• unclear risk of bias, if we judge one or more domains to be at unclear risk of bias and other domains are at low risk of bias but no domain is at high risk of bias; or

• high risk of bias, as long as we judge one or more domains as being at high risk of bias, or all domains have unclear 'Risk of bias' judgements, as this can substantially reduce confidence in the result.

We will resolve any discrepancy between review authors by discussion and by involving another review author where necessary.

For studies using cluster randomisation, we will consider the risk of bias in relation to recruitment bias, baseline imbalance, loss of clusters, incorrect analysis and comparability with individually randomised studies (Eldridge 2019; Higgins 2019b; Appendix 3).

Measures of treatment effect

For meta‐analysis of pressure ulcer incidence data, we will present the risk ratio (RR) with its 95% confidence interval (CI). For continuous outcome data, we will present the mean difference (MD) with 95% CIs for studies that use the same assessment scale. If studies reporting continuous data use different assessment scales, we will report the standardised mean difference (SMD) with 95% CIs.

For time‐to‐event data (time to pressure ulcer incidence), we will present the hazard ratio (HR) with its 95% CI. If included studies reporting time‐to‐event data do not report an HR, then, when feasible, we will estimate this using other reported outcomes, such as numbers of events, through employing available statistical methods (Parmar 1998; Tierney 2007).

Unit of analysis issues

We will note whether studies present outcomes at the level of cluster (e.g. ward, research site) or at the level of participants. We will also record whether the same participant is reported as having multiple pressure ulcers.

Unit of analysis issues may occur if studies randomise at the cluster level but the incidence of pressure ulcers is observed and data are presented and analysed at the level of participants (clustered data). We will note whether data regarding participants within a cluster were (incorrectly) treated as independent within a study, or were analysed using within‐cluster analysis methods. If clustered data are incorrectly analysed, we will record this as part of the 'Risk of bias' assessment.

If a cluster‐RCT was not correctly analysed, where possible, we will use available information (see below) to adjust for clustering ourselves, in accordance with guidance in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2019b):

• the number of clusters randomly assigned to each intervention; or the average (mean) number of participants per cluster;

• outcome data ignoring the cluster design for the total number of participants; and

• estimate of the intra‐cluster (or intra‐class) correlation coefficient (ICC).

Cross‐over trials

For cross‐over trials, we will only consider outcome data at the first intervention phase (i.e. prior to cross‐over) as eligible.

Studies with multiple treatment groups

If a study has more than two eligible study groups, we will combine results across all eligible groups of alternating pressure (active) air surfaces and compare them with, where available, the combined results across all eligible control groups, and make single pair‐wise comparisons (Higgins 2019b).

Dealing with missing data

Data are commonly missing from study reports. Reasons for missing data could be the exclusion of participants after randomisation, withdrawal of participants from a study, or loss to follow‐up. The exclusion of these data from analysis may break the randomisation and potentially introduces bias.

Where there are missing data, and where relevant, we will contact study authors to pose specific queries about these data. In the absence of other information, for pressure ulcer incidence we will assume that participants with missing data did not develop new pressure ulcers for the main analysis (i.e. we will add missing data to the denominator but not the numerator). We will examine the impact of this assumption through undertaking a sensitivity analysis (see Sensitivity analysis).

Note that when a study does not specify the number of randomised participants prior to dropout, we will use the available number of participants as the number randomised.

Assessment of heterogeneity

Assessing heterogeneity can be a complex, multifaceted process. Firstly, we will consider clinical and methodological heterogeneity; that is, the extent to which the included studies vary in terms of participant, intervention, outcome, and other characteristics including duration of follow‐up, clinical settings, and overall study‐level 'Risk of bias' judgement (Deeks 2019). In terms of the duration of follow‐up, in order to assess the relevant heterogeneity, we will record and class assessment of outcome measures from:

• up to eight weeks as short term;

• more than eight weeks to 16 weeks as medium term; and

• more than 16 weeks as long term.

We will supplement this assessment of clinical and methodological heterogeneity with information regarding statistical heterogeneity assessed using the Chi² test. We will consider a P value less than 0.10 to indicate statistically significant heterogeneity given that the Chi² test has low power, particularly in the case where studies included in a meta‐analysis have small sample size. We will carry out this statistical assessment in conjunction with the I² statistic (Higgins 2003), and the use of prediction intervals for random‐effects meta‐analyses (Borenstein 2017; Riley 2011).

The I² statistic is the percentage of total variation across studies due to heterogeneity rather than chance (Higgins 2003). Very broadly we will consider that I² values of 25% or less may indicate a low level of heterogeneity and values of 75% or more may indicate very high heterogeneity (Higgins 2003). For random‐effects models where the meta‐analysis has more than 10 included studies and no clear funnel plot asymmetry we will also present 95% prediction intervals (Deeks 2019). We will calculate prediction intervals following methods proposed by Borenstein 2017.

Random‐effects analyses produce an average treatment effect, with 95% confidence intervals indicating where the true population average value is likely to lie. Prediction intervals quantify variation away from this average due to between‐study heterogeneity. The interval conveys where a future study treatment effect estimate is likely to fall based on the data analysed to date (Riley 2011). As prediction intervals consider a wider scope of variation they are always wider than confidence intervals (Riley 2011).

It is important to note that prediction intervals will reflect heterogeneity of any source including from methodological issues as well as clinical variation. For this reason some authors have suggested that prediction intervals are best calculated for studies at low risk of bias to ensure intervals that have meaningful clinical interpretation (Riley 2011). However, we will not consider the risk of bias of the studies here because the intervals are to assess heterogeneity, and we will perform subgroup analysis by risk of bias as detailed below.

Assessment of reporting biases

We will follow the systematic framework recommended by Page 2019 to assess risk of bias due to missing results (non‐reporting bias) in the meta‐analysis of pressure ulcer incidence data. To make an overall judgement about risk of bias due to missing results, we will:

• identify whether pressure ulcer incidence data are unavailable by comparing the details of outcomes in trials registers, protocols or statistical analysis plans, if available, with reported results. If the above information sources are unavailable we will compare outcomes in the conference abstracts or in the methods section of the publication, or both, with the reported results. If we find non‐reporting of study results, we will then judge whether the non‐reporting is associated with the nature of findings by using the 'Outcome Reporting Bias In Trials' (ORBIT) system (Kirkham 2018);

• assess the influence of definitely missing pressure ulcer incidence data on meta‐analysis; and

• assess the likelihood of bias where a study has been conducted but not reported in any form. For this assessment, we will consider whether the literature search is comprehensive and will produce a funnel plot for meta‐analysis for seeking more evidence about the extent of missing results, provided there are at least 10 included studies (Peters 2008; Salanti 2014).

Data synthesis

We will summarise the included studies narratively and synthesise data using meta‐analysis where applicable. We will structure comparisons according to type of comparator and then by outcomes ordered by follow‐up period.

We will consider clinical and methodological heterogeneity and undertake pooling when studies appear appropriately similar in terms of participants, support surfaces, and outcome type. Where statistical synthesis of data from more than one study is not possible or considered inappropriate, we will conduct a narrative review of eligible studies.

Once the decision to pool is made we anticipate using a random‐effects model, which will estimate an underlying average treatment effect from studies. Conducting meta‐analysis with a fixed‐effect model in the presence of even minor heterogeneity may provide overly narrow confidence intervals. We will use the Chi² test and I² statistic to quantify heterogeneity but not to guide choice of model for meta‐analysis (Borenstein 2009). We will exercise caution when meta‐analysed data are at risk of small study effects because use of a random‐effects model may be unsuitable here. In this case, or where there are other reasons to question the choice 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 (Thompson 1999).

We will perform meta‐analyses largely using Review Manager 5.3 (Review Manager 2014), and using Stata: Release 14 (Stata 2015), or R (R Core Team 2019), where necessary. We will present data using forest plots where possible. For dichotomous outcomes, we will present the summary estimate as an RR with 95% CI. Where continuous outcomes were measured, we will present an MD with 95% CI; we will report SMD estimates where studies measured the same outcome using different methods. For time‐to‐event data, we will present the summary estimates as HRs and 95% CIs.

Subgroup analysis and investigation of heterogeneity

Investigation of heterogeneity

When important heterogeneity occurs, we will follow steps proposed by Cipriani 2013 to investigate further. We will:

• check the data extraction and data entry for errors and possible outlying studies;

• if outliers exist, perform sensitivity analysis by removing them; and

• if heterogeneity is still present, perform subgroup analyses for study‐level characteristics (see below) in order to explain heterogeneity as far as possible.

Subgroup analysis

We will investigate heterogeneity using the methods described in the Cochrane Handbook for Systematic Reviews of Interventions (Deeks 2019). We will perform subgroup analyses for binary and categorical factors (or meta‐regression for continuous factors) to determine whether the size of treatment effects is influenced by the following four study‐level characteristics:

• risk of bias (binary: low or unclear risk of bias; and high risk of bias; Schulz 1995);

• settings (categorical: acute care and other hospital settings; long‐term care settings; operating theatre setting; and intensive care unit);

• baseline skin status (categorical: participants at risk, mixed skin status or non‐reporting; non‐blanchable erythema; existing ulcers of Stage 2 or serious; Shi 2018c); and

• follow‐up duration (continuous).

We will not perform subgroup analysis/meta‐regression when the number of studies included in the meta‐analysis is not reasonable (e.g. fewer than 10).

We will compare subgroups using the analysis option of the 'Test for Subgroup Differences’ in Review Manager 5.3 (Review Manager 2014).

Sensitivity analysis

We will assess the robustness of meta‐analysis of pressure ulcer incidence data through doing sensitivity analyses as follows.

• Impact of considering specific alternating pressure (active) air surfaces as different surfaces rather than as a general group. We will undertake a sensitivity analysis to examine whether disentangling specific alternating pressure (active) air surfaces that are listed in Types of interventions from alternating pressure (active) air surface as a single intervention affects the meta‐analysis results.

• Impact of considering time to pressure ulcer incidence as the primary outcome measure. We will do this by considering time to pressure ulcer incidence as the primary outcome measure in this review (i.e. analysing the included data on the proportion of participants developing a new pressure ulcer for the main analysis, followed by a repeated analysis with time to pressure ulcer incidence data).

• Impact of missing data. We will do this by repeating the analysis using complete case data (instead of assuming that participants with missing data did not develop new pressure ulcers, as in the main analysis).

• Impact of altering the effects model used. We will do this by repeating the analysis using a different effects model (i.e. using a random‐effects model for the main analysis, followed by a repeated analysis with a fixed‐effect model).

Summary of findings and assessment of the certainty of the evidence

We will present the main, pooled results of the review in 'Summary of findings' tables. These tables present key information concerning the certainty of evidence, the magnitude of the effects of the interventions examined and the sum of available data for the main outcomes (Schünemann 2019). These tables also include an overall grading of the certainty of the evidence associated with each of the main outcomes that we will assess using the GRADE approach via GRADEpro GDT software. The GRADE approach 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.

The GRADE assessment involves consideration of five factors: within‐trial risk of bias, directness of evidence, heterogeneity, precision of effect estimates, and risk of publication bias (Schünemann 2019). The certainty of evidence can be assessed as being: high, moderate, low or very low; RCT evidence has the potential to be high certainty. Where applicable (e.g. for pressure ulcer incidence outcomes), we will not downgrade the certainty of evidence for risk of bias if blinding of participants and personnel is the only domain resulting in our judgement of overall high risk of bias for those studies in which it is impossible to blind participants and personnel.

We will present a separate 'Summary of findings' table for each comparison evaluated in this review. We will present the following outcomes in the 'Summary of findings' tables:

• proportion of participants developing a new pressure ulcer

• time to pressure ulcer incidence

• patient support‐surface‐associated comfort

• all reported adverse events

• health‐related quality of life

• cost effectiveness

We will prioritise the time points and method of outcome measurement specified in Types of outcome measures for presentation in ‘Summary of findings’ tables’. Where we do not pool data for some outcomes within a comparison, we will conduct a GRADE assessment for each of these outcomes and present these assessments in a narrative format within the Results section, without presenting them in separate 'Summary of findings' tables.

History

Protocol first published: Issue 5, 2020

Appendices

Appendix 1. Full details of classifications of support surfaces

Overarching class of support surface (as used in this review)	Corresponding subclasses of support surfaces used inShi 2018a	Descriptions of support surfaces	Selected examples (with example brands where possible)
Reactive air surfaces	Powered/non‐powered reactive air surfaces	A group of support surfaces constructed of air‐cells, which redistribute body weight over a maximum surface area (i.e. has reactive pressure redistribution mode), with or without the requirement for electrical power	Static air mattress overlay, dry flotation mattress (e.g. Roho, Sofflex), static air mattress (e.g. EHOB), and static mode of Duo 2 mattress
	Powered/non‐powered reactive low‐air‐loss air surfaces	A group of support surfaces made of air‐cells, which have reactive pressure redistribution modes and a low‐air‐loss function, with or without the requirement for electrical power	Low‐air‐loss hydrotherapy
	Powered reactive air‐fluidised surfaces	A group of support surfaces made of air‐cells, which have reactive pressure redistribution modes and an air‐fluidised function, with the requirement for electrical power	Air‐fluidised bed (e.g. Clinitron)
Foam surfaces	Non‐powered reactive foam surfaces	A group of support surfaces made of foam materials, which have a reactive pressure redistribution function, without the requirement for electrical power	Convoluted foam overlay (or pad), elastic foam overlay (e.g. Aiartex, microfluid static overlay), polyether foam pad, foam mattress replacement (e.g. MAXIFLOAT), solid foam overlay, viscoelastic foam mattress/overlay (e.g. Tempur, CONFOR‐Med, Akton, Thermo)
Alternative reactive support surfaces (non‐foam or air‐filled): reactive fibre surfaces	Non‐powered reactive fibre surfaces	A group of support surfaces made of fibre materials, which have a reactive pressure redistribution function, without the requirement for electrical power	Silicore (e.g. Spenco) overlay/pad
Alternative reactive support surfaces (non‐foam or air‐filled): reactive gel surfaces	Non‐powered reactive gel surfaces	A group of support surfaces made of gel materials, which have a reactive pressure redistribution function, without the requirement for electrical power	Gel mattress, gel pad used in operating theatre
Alternative reactive support surfaces (non‐foam or air‐filled): reactive sheepskin surfaces	Non‐powered reactive sheepskin surfaces	A group of support surfaces made of sheepskin, which have a reactive pressure redistribution function, without the requirement for electrical power	Australian Medical Sheepskins overlay
Alternative reactive support surfaces (non‐foam or air‐filled): reactive water surfaces	Non‐powered reactive water surfaces	A group of support surfaces based on water, which has the capability of a reactive pressure redistribution function, without the requirement for electrical power	Water mattress
Alternating pressure (active) air surfaces	Powered active air surfaces	A group of support surfaces made of air‐cells, which mechanically alternate the pressure beneath the body to reduce the duration of the applied pressure (mainly via inflating and deflating to alternately change the contact area between support surfaces and the body; i.e. alternating pressure (or active) mode), with the requirement for electrical power	Alternating pressure‐relieving air mattress (e.g. Nimbus II, Cairwave, Airwave, MicroPulse), large‐celled ripple
	Powered active low‐air‐loss air surfaces	A group of support surfaces made of air‐cells, which have the capability of alternating pressure redistribution as well as low air loss for drying local skin, with the requirement for electrical power	Alternating pressure low‐air‐loss air mattress
	Powered hybrid system air surfaces	A group of support surfaces made of air‐cells, which offer both reactive and active pressure redistribution modes, with the requirement for electrical power	Foam mattress with dynamic and static modes (e.g. Softform Premier Active)
	Powered hybrid system low‐air‐loss air surfaces	A group of support surfaces made of air‐cells, which offer both reactive and active pressure redistribution modes as well as a low‐air‐loss function, with the requirement for electrical power	Stand‐alone bed unit with alternating pressure, static modes and low air‐loss (e.g. TheraPulse)
Standard hospital surfaces	Standard hospital surfaces	A group of support surfaces made of any materials, used as‐usual in a hospital and without reactive or active pressure redistribution capabilities, nor any other functions (e.g. low air loss, or air‐fluidised).	Standard hospital (foam) mattress, National Health Service Contract hospital mattress, standard operating theatre surface configuration, standard bed unit and usual care

Appendix 2. Cochrane Central Register of Controlled Trials (CENTRAL) draft search strategy

#1MeSH descriptor: [Beds] explode all trees
#2mattress*:ti,ab,kw
#3(foam or transfoam):ti,ab,kw
#4overlay*:ti,ab,kw
#5"pad" or "pads":ti,ab,kw
#6"gel":ti,ab,kw
#7(pressure next relie*):ti,ab,kw
#8(pressure next reduc*):ti,ab,kw
#9(pressure next alleviat*):ti,ab,kw
#10("low pressure" near/2 device*):ti,ab,kw
#11("low pressure" near/2 support):ti,ab,kw
#12(constant near/2 pressure):ti,ab,kw
#13"static air":ti,ab,kw
#14(alternat* next pressure):ti,ab,kw
#15(air next suspension*):ti,ab,kw
#16(air next bag*):ti,ab,kw
#17(water next suspension*):ti,ab,kw
#18sheepskin:ti,ab,kw
#19(turn* or tilt*) next (bed* or frame*):ti,ab,kw
#20kinetic next (therapy or table*):ti,ab,kw
#21(net next bed*):ti,ab,kw
#22{or #1‐#21}
#23MeSH descriptor: [Pressure Ulcer] explode all trees
#24(pressure next (ulcer* or sore* or injur*)):ti,ab,kw
#25(decubitus next (ulcer* or sore*)):ti,ab,kw
#26((bed next sore*) or bedsore*):ti,ab,kw
#27{or #23‐#26}
#28(#22 and #27) in Trials

Appendix 3. Risk of bias

1 'Risk of bias' assessment in individually randomised controlled trials

1. Was the allocation sequence randomly generated?

Low risk of bias

The study authors describe a random component in the sequence generation process such as referring to a random number table, using a computer random number generator, coin tossing, shuffling cards or envelopes, throwing dice, drawing of lots.

High risk of bias

The study authors describe a non‐random component in the sequence generation process. Usually, the description would involve some systematic, non‐random approach, for example, sequence generated by odd or even date of birth, sequence generated by some rule based on date (or day) of admission, sequence generated by some rule based on hospital or clinic record number.

Unclear

Insufficient information about the sequence generation process to permit judgement of low or high risk of bias.

2. Was the treatment allocation adequately concealed?

Low risk of bias

Participants and study authors enrolling participants could not foresee assignment because one of the following, or an equivalent method, was used to conceal allocation: 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

Participants or investigators enrolling participants could possibly foresee assignments and thus introduce selection bias, such as allocation based on 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

Insufficient information to permit judgement of low or high risk of bias. This is usually the case if the method of concealment is not described or not described in sufficient detail to allow a definite judgement, for example if the use of assignment envelopes is described, but it remains unclear whether envelopes were sequentially numbered, opaque and sealed.

3. Blinding: was knowledge of the allocated interventions by participants and personnel adequately prevented during the study?

Low risk of bias

Any one of the following.

• No 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

Any one of the following.

• 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.

• Either participants or some key study personnel were not blinded, and the non‐blinding of others likely to introduce bias.

Unclear

Any one of the following.

• Insufficient information to permit judgement of low or high risk of bias.

• The study did not address this outcome.

4. Blinding: was knowledge of the allocated interventions by outcome assessors adequately prevented during the study?

Low risk of bias

Any one of the following.

• No blinding, 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

Any one of the following.

• No blinding or incomplete blinding, and the outcome measurement is likely to be influenced by lack of blinding.

• Blinding of outcome assessment attempted, but likely that the blinding could have been broken.

Unclear

Any one of the following.

• Insufficient information to permit judgement of low or high risk of bias.

• The study did not address this outcome.

5. Were incomplete outcome data adequately addressed?

Low risk of bias

Any one of the following.

• 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

Any one of the following.

• 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 standardised 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

Any one of the following.

• Insufficient reporting of attrition/exclusions to permit judgement of low or high risk of bias (e.g. number randomised not stated, no reasons for missing data provided).

• The study did not address this outcome.

6. Are reports of the study free of suggestion of selective outcome reporting?

Low risk of bias

Any of the following.

• The study protocol is available and all of the study’s prespecified (primary and secondary) outcomes that are of interest in the review have been reported in the prespecified way.

• The study protocol is not available but it is clear that the published reports include all expected outcomes, including those that were prespecified (convincing text of this nature may be uncommon).

High risk of bias

Any one of the following.

• Not all of the study’s prespecified primary outcomes have been reported.

• One or more primary outcomes are reported using measurements, analysis methods or subsets of the data (e.g. subscales) that were not prespecified.

• One or more reported primary outcomes were not prespecified (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 of low or high risk of bias. It is likely that the majority of studies will fall into this category.

7. Other sources of potential bias

Low risk of bias

The study appears to be free of other sources of bias.

High risk of bias

There is at least one important risk of bias. For example, the study:

• had a potential source of bias related to the specific study design used; or

• has been claimed to have been fraudulent; or

• had some other problem.

Unclear

There may be a risk of bias, but there is either:

• insufficient information to assess whether an important risk of bias exists; or

• insufficient rationale or evidence that an identified problem will introduce bias

2 'Risk of bias' assessment in cluster‐randomised controlled trials (cluster‐RCTs)

1. Recruitment bias

Recruitment bias (or identification bias) is the bias that occurs in cluster‐RCTs if the personnel recruiting participants know individuals’ allocation, even when the allocation of clusters has been concealed appropriately. The knowledge of the allocation of clusters may lead to bias because the individuals' recruitment in cluster trials is often behind the clusters' allocation to different interventions; and the knowledge of allocation can determine whether individuals are recruited selectively.

This bias can be judged through considering the following questions.

• Were all the individual participants identified/recruited before randomisation of clusters?

• Is it likely that selection of participants was affected by knowledge of the intervention?

• Were there baseline imbalances that suggest differential identification or recruitment of individual participants between arms?

2. Baseline imbalance

Baseline imbalance between intervention groups can occur due to chance, problems with randomisation, or identification/recruitment bias. The issue of recruitment bias has been considered above.

In terms of study design, the risk of chance baseline imbalance can be reduced by the use of stratified or pair‐matched randomisation. Minimisation — an equivalent technique to randomisation — can be used to achieve better balance in cluster characteristics between intervention groups if there is a small number of clusters.

Concern about the influence of baseline imbalance can be reduced if studies report the baseline comparability of clusters, or statistical adjustment for baseline characteristics.

3. Loss of clusters

Similar to missing outcome data in individually randomised trials, bias can occur if clusters are completely lost from a cluster‐RCT, and are omitted from the analysis.

The amount of missing data, the reasons for missingness and the way of analysing data given the missingness should be considered in assessing the possibility of bias.

4. Incorrect analysis

Data analyses, which do not take the clustering into account, in cluster‐RCTs will be incorrect. Such analyses lead to a 'unit of analysis error' and over‐precise results (too small standard error) and too small P values. Though these analyses will not result in biased estimates of effect, they (if not correctly adjusted) will lead to too much weight allocated to cluster trials in a meta‐analysis.

Note that the issue of analysis may not lead to concern any more and will not be considered substantial if approximate methods are used by review authors to address clustering in data analysis.

5. Comparability with individually randomised trials

In the case that a meta‐analysis includes, for example, both cluster and individually randomised trials, potential differences in the intervention effects between different trial designs should be considered. This is because the 'contamination' of intervention effects may occur in cluster‐RCTs, which would lead to underestimates of effect. The contamination could be known as a 'herd effect': that is, within clusters, individuals' compliance with using an intervention may be enhanced, which in return affects the estimation of effect.

Contributions of authors

Chunhu Shi: conceived the review question; developed the protocol; co‐ordinated the protocol development; produced the first draft of the protocol; contributed to writing or editing the protocol; advised on the protocol; approved the final version of the protocol prior to submission; is guarantor of the protocol.

Jo Dumville: conceived the review question; developed the protocol; co‐ordinated the protocol development; secured funding; produced the first draft of the protocol; contributed to writing or editing the protocol; advised on the protocol; approved the final version of the protocol prior to submission.

Nicky Cullum: conceived the review question; developed the protocol; co‐ordinated the protocol development; contributed to writing or editing the protocol; advised on the protocol; approved the final version of the protocol prior to submission.

Sarah Rhodes: developed the protocol; contributed to writing or editing the protocol; advised on the protocol; approved the final version of the protocol prior to submission.

Elizabeth McInnes: conceived the review question; developed the protocol; contributed to writing or editing the protocol; advised on the protocol; approved the final version of the protocol prior to submission.

Contributions of the editorial base

Gill Norman (Editor): edited the protocol; advised on methodology, interpretation and content; approved the final protocol prior to submission.
Gill Rizzello (Managing Editor): coordinated the editorial process; advised on content; edited the protocol.
Sophie Bishop (Information Specialist): designed the search strategy and edited the search methods section.
Tom Patterson (Editorial Assistant): edited the reference section of the protocol.

Sources of support

Internal sources

• Division of Nursing, Midwifery and Social Work, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, UK

External sources

• National Institute for Health Research (NIHR), UK

  This research is independent research funded by the National Institute for Health Research (Research for Patient Benefit, Evidence synthesis for pressure ulcers prevention and treatment, PB‐PG‐1217‐20006). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care.

• NIHR Manchester Biomedical Research Centre (BRC), UK

  This research was co‐funded by the NIHR Manchester BRC. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care.

• National Institute for Health Research (NIHR), UK

  This project was supported by the National Institute for Health Research, via Cochrane Infrastructure funding to Cochrane Wounds. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

• National Institute for Health Research Collaboration for Leadership Applied Health Research and Care (NIHR CLAHRC), Greater Manchester, UK

  Nicky Cullum and Jo Dumville’s work on this project was partly funded by the NIHR CLAHRC, Greater Manchester. The funder had no role in the decision to publish, or preparation of the manuscript. However, the review may be considered to be affiliated to the work of the NIHR CLAHRC Greater Manchester. The views expressed herein are those of the authors and not necessarily those of the NHS, NIHR or the Department of Health and Social Care.

Declarations of interest

Chunhu Shi: I received research funding from the National Institute for Health Research (Research for Patient Benefit, Evidence synthesis for pressure ulcer prevention and treatment, PB‐PG‐1217‐20006). I received support from the Tissue Viability Society to attend conferences unrelated to this work. The Doctoral Scholar Awards Scholarship and Doctoral Academy Conference Support Fund (University of Manchester) also supported a PhD and conference attendance respectively, both were unrelated to this work.

Jo Dumville: I received research funding from the National Institute for Health Research (NIHR) UK for the production of systematic reviews focusing on high‐priority Cochrane Reviews in the prevention and treatment of wounds. This research was co‐funded by the NIHR Manchester Biomedical Research Centre and partly funded by the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care (NIHR CLAHRC) Greater Manchester.
Nicky Cullum: I received research funding for wounds‐related research and systematic reviews from the NIHR for the production of systematic reviews focusing on high‐priority Cochrane Reviews in the prevention and treatment of wounds. This research was co‐funded by the NIHR Manchester Biomedical Research Centre, and partly funded by the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care (NIHR CLAHRC) Greater Manchester.

My previous employer received research grant funding from the NHS Research and Development programme, Health Technology Assessment Programme for systematic reviews of pressure relief, and for an RCT comparing different alternating pressure air surfaces for pressure ulcer prevention. This RCT (for which I was the Chief Investigator) would be eligible for inclusion in this review.

Sarah Rhodes: my salary is funded from three NIHR grants and a grant from Greater Manchester Cancer.

Elizabeth McInnes: none known.

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