Acellular dermal matrices for breast reconstruction surgery
Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:
To assess the effects of acellular dermal matrices in prosthetic breast reconstructions following cancer and prophylactic breast surgery.
Background
Description of the condition
Breast cancer is a common condition and one in eight women will be diagnosed with breast cancer during their lifetime (Breastcancer.org 2015). The annual incidence is approximately 1.67 million cases worldwide and it is estimated to rise to 2.1 million by 2030 (Globocan 2012). Surgical and adjuvant chemotherapy and radiotherapy is the mainstay treatment of breast cancer. The exact surgical approach is determined by the size, grade and tumour staging. If the tumour is multifocal, multicentric, or of significant volume, mastectomy will be the preferred approach. A mastectomy will remove the tumour and surrounding healthy tissue. Patients are routinely offered reconstructive surgery to restore the lost tissue and cosmetic appearance. Reconstructive surgery can be performed following mastectomy in a single procedure (immediate reconstruction) or can be performed months or years later (delayed reconstruction). In the United Kingdom, for example, 21 percent of women opted for immediate reconstruction after mastectomy when given the choice (based on a national data audit from 2008 to 2009; NHS Information Centre 2011).
Reconstructive procedures can be divided into prosthetic and autologous techniques. A prosthetic technique involves reconstruction either with a single‐stage immediate implant prothesis or a two‐stage delayed expander (inflatable breast implant designed to stretch the muscle in immediate reconstruction or the skin in delayed reconstruction) then implant reconstruction. The success of these techniques in restoring form and symmetry following mastectomy depends largely on the adequacy of soft tissue cover for the prosthesis. Implants are placed beneath the chest wall muscle and soft tissue (submuscular). The aim of submuscular placement is to ensure a greater coverage of the implant with the patient's tissue. This can help with the overall cosmetic appearance of the breast and help prevent implant‐related complications. Complications with implant reconstruction include: chest wall pain, implant infection and haematoma formation, skin flap necrosis (i.e. death of localised tissue or cells due to poor blood supply), formation of fibrous tissue around the implant leading to a distortion in breast shape (i.e. capsular contracture) and implant migration (Chun 2010). Adjuvant radiotherapy may increase the risk of capsular contracture (Lam 2013). These complications may compromise the aesthetic outcome of the reconstruction and may necessitate further revision surgery (Zhong 2013).
Autologous reconstruction involves harvesting tissue from other areas on the patient's body as a pedicled or free tissue flap performed by plastic surgeons, which replaces the breast tissue removed by mastectomy in the place of an implant. Autologous reconstructions are based upon the concept that a patient's own tissue is most likely to mimic breast tissue lost during mastectomy. Most commonly, abdominal tissue is utilised as the texture and consistency of the subcutaneous tissue matches well to breast tissue, however tissue may be harvested from other sites depending on patient factors. For example, the deep inferior epigastric perforators flap (DIEP) uses excess tissue from the abdomen. Harvesting tissue for a soft tissue flap for breast reconstruction can result in donor‐site related morbidity such as scarring, pain, wound healing problems, and hernias (Kaya 2013). Autologous reconstructions can also be complicated with failure of flap blood supply (flap necrosis), skin necrosis, infection, seroma and haematoma which are higher in patients with body mass indexes of over 35 (Thiruchelvam 2013).
Description of the intervention
Acellular dermal matrices (ADM) are often referred to as biological tissue meshes. The biological meshes are derived from a layer of the skin (dermis) from human or animal sources. The harvested dermis is then processed via a number of techniques, for example gamma‐irradiation, to allow it to be used safely in breast reconstruction. Breast and plastic surgeons use these meshes intra‐operatively to provide additional soft tissue cover for prosthetic breast reconstructions. The aim of the intervention is to provide extra cover for the implant and to reduce implant‐related complications. There has been a rapid increase in the number of breast reconstructions utilising this new technology since 2007. A recent survey of American plastic surgeons revealed half of respondents who are performing prosthetic breast reconstructions used ADM (Gurunluoglu 2013). ADM are designed to provide a scaffold for tissue regeneration and healing (Connor 2009) for the body to form a layer of soft tissue over the implant in the area it is used. There are data published to support its use to reduce implant‐related complications such as capsular contracture and migration (Hester 2012; Sbitany 2011) by preventing fibrosis around the implant and providing a hammock of support for the implant. It has also been suggested that ADM can eliminate the need to use a tissue expander, resulting in a single‐stage implant reconstruction (Cayci 2013; Salzberg 2011). There are several ADM products on the market available for surgeons to use, which vary in composition and manufacturing techniques.
How the intervention might work
The ADM provide soft tissue coverage of prosthetic breast reconstructions (implant or tissue‐expander based) without the need to harvest autologous tissue. Removing the need to harvest autologous tissue may decrease operating time and eliminate the complications associated with autologous reconstructions. The ADM can be manipulated and shaped more easily compared with autologous tissue, improving cosmesis at the infra‐mammary fold. However, conflicting results on the benefits and complications related to the use of ADM have lead to uncertainty regarding their overall benefit to women undergoing breast reconstruction.
Several studies have suggested that placing dermal matrix results in increased complication rates for patients including seroma formation, infection, skin flap necrosis, haematoma and reconstruction failure (Ho 2012;Kim 2012; Weichman 2012). Other conflicting studies have demonstrated decreased capsular contracture rates (Hester 2012), superior aesthetic results (Forsberg 2014), reduction of extensive dissection to create soft tissue coverage for the prosthesis and faster expander volumes post implant reconstruction (Sbitany 2009).
Why it is important to do this review
A number of systematic reviews have been published in this field thus far (Hoppe 2011; Ho 2012; Sbitany 2011; Valdatta 2014) which reflects the importance of the topic for review. The reviews have taken different approaches in methodology, such as in trial quality assessment. For example, Ho 2012 has assessed the quality of the evidence (without 'Risk of bias' assessment) whereas Hoppe 2011 and Sbitany 2011 do not appear to make any formal assessment of study quality. The review by Valdatta 2014 focused on outcome measures in implant reconstruction with ADM in the context of radiotherapy.
The outcome measures also differ between the reviews. The majority report clinical measures of infection, seroma, haematoma and reconstruction failure. There appears to be no assessment of patient‐related outcome measures such as reported pain and quality of life. Neither have the reviews examined cancer‐related outcome measures in patients with ADM‐assisted prosthetic reconstruction.
The outcomes in our proposed review are clearly defined and encompass all aspects of the intervention in question. We aim to provide a detailed and definitive review of the available evidence. In this review we will assess and summarise the current evidence comparing reconstructive techniques which utilise ADM versus breast reconstruction without the aid of ADM. With such a wide variety of different surgical interventions available, the comparisons within the review should assist patients and their clinicians to choose surgical breast reconstruction techniques with a better knowledge of the current evidence.
Objectives
To assess the effects of acellular dermal matrices in prosthetic breast reconstructions following cancer and prophylactic breast surgery.
Methods
Criteria for considering studies for this review
Types of studies
We will include published randomised controlled trials (RCTs). If no RCTs are found, we will expand our criteria to include non‐randomised comparative studies (cohort and case‐control).
Types of participants
Women undergoing prosthetic reconstructive surgery following mastectomy for breast malignancy and/or women undergoing prophylactic mastectomy with immediate prosthetic reconstruction. All stages of breast disease will be included.
Types of interventions
Trials will be included if they compare any prosthetic reconstructive technique utilising acellular dermal matrices (ADM) with another prosthetic reconstructive technique.
Adjuvant radiotherapy and neoadjuvant chemotherapy can be administered in the no‐treatment arm or in both treatment arms of the included study.
Types of outcome measures
Primary outcomes
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Reconstruction failure (defined as loss of implant or expander): time‐to‐event data will be collected.
Secondary outcomes
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Skin flap necrosis requiring surgical debridement (defined as full thickness necrosis: less than 2 cm for conservative treatment, more than 2 cm for surgical intervention) within 30 days.
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Seroma requiring drainage (radiologically or surgically) within 30 days.
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Implant infection requiring antibiotic therapy (intravenous or oral) within 30 days.
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Haematoma requiring surgical evacuation under general anaesthesia within 30 days.
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Capsular contracture: defined by the Baker classification, up to 5 years post operatively.
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Post‐operative pain: defined by validated visual analogue scoring (VAS) systems.
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Cancer‐related outcomes: local and distant recurrence of disease demonstrated on follow‐up imaging (mammogram; magnetic resonance imaging (MRI); computed tomography (CT)); time‐to‐event data for recurrence during the follow‐up period.
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Patient‐related outcomes: defined by validated quality of life questionnaires (for example Breast‐Q).
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Surgeon‐related outcomes: defined by qualitative blinded aesthetic scoring systems.
Search methods for identification of studies
Electronic searches
We will search the following electronic bibliographical databases:
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The Cochrane Breast Cancer Group's (CBCG's) Specialised Register. Details of the search strategies used by the Group for the identification of studies and the procedure used to code references are outlined in information about the Group on The Cochrane Library(http://www.mrw.interscience.wiley.com/cochrane/clabout/articles/BREASTCA/frame.html). Trials with the key words "acellular dermis, breast neoplasms, and surgery" will be extracted and considered for inclusion in the review.
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MEDLINE (via OvidSP), see Appendix 1.
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EMBASE (via EMBASE.com), see Appendix 2.
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Cochrane Central Register of Controlled Trials (CENTRAL), see Appendix 3.
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The WHO International Clinical Trials Registry Platform (ICTRP) search portal (http://apps.who.int/trialsearch/Default.aspx) for all prospectively registered and ongoing trials, see Appendix 4.
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Clinicaltrials.gov (http://clinicaltrials.gov/), see Appendix 5.
Searching other resources
We will handsearch reference lists of articles retrieved by the search and contact experts in the field to obtain additional data.
Data collection and analysis
Selection of studies
Two review authors (RR, JD) will independently screen the titles and abstracts retrieved by the search. The full texts of all potentially eligible studies will be retrieved. Two review authors will independently examine these full‐text articles for compliance with the inclusion criteria and select studies eligible for inclusion in the review. There will be no language restriction. We will correspond with the study investigators, as required, to clarify study eligibility or to seek further data where necessary. Disagreements will be resolved by discussion and with a third author (SM). We will document the selection process with a 'PRISMA' flow chart (Moher 2009). Excluded studies will be listed in the 'Characteristics of excluded studies' table.
Data extraction and management
Two review authors (RR, JD) will independently extract the data from eligible studies using a data extraction form designed and pilot‐tested by the review authors. Any disagreements will be resolved by discussion and with a third author (SM). Data to be extracted will include study characteristics (i.e. study design, participants, setting, interventions, follow‐up, sources of funding) and outcome data (including outcome definitions). Where studies have multiple publications, the main trial report will be used as the reference and additional details will be derived from the secondary papers. We will correspond with study investigators for further information as required.
Assessment of risk of bias in included studies
We will follow the Cochrane Collaboration's tool for assessing risk of bias as described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011), the Cochrane EPOC Group's 'Risk of bias' criteria, and recommendations by Norris (Norris 2013). Assessment of the methodological quality of each study will be undertaken independently by two authors (JD, RR) and risk of bias will be assessed under the following domains: selection bias, performance/detection bias, attrition bias, reporting bias and other bias. For each 'Risk of bias' domain and its associated specific questions outlined below, we will assign a judgement of either 'high risk', 'low risk', or 'unclear risk'.
Selection bias
Was the allocation sequence adequately generated?
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Score 'low risk' if a random component in the sequence generation process is described (e.g. referring to a random number table)
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Score 'high risk' when a non‐random method is used (e.g. performed by date of admission)
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Non‐randomised studies should be scored 'high risk'. Score 'unclear risk' if not specified in the paper
Was the allocation adequately concealed?
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Score 'low risk' if participants and investigators enrolling participants could not foresee assignment (e.g. because a centralised randomisation scheme, an on‐site computer system or sealed opaque envelopes were used)
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Non‐randomised studies should be scored 'high risk'
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Score 'unclear risk' if not specified in the paper
For non‐randomised studies we will also consider the following questions.
Were baseline outcome measurements similar?
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Score 'low risk' if performance or patient outcomes were measured before the intervention, and no important differences were present across study groups
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Score 'high risk' if important differences were present and not adjusted for in the analysis
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Score 'unclear risk' if not specified in the paper
Were baseline characteristics similar?
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Score 'low risk' if baseline characteristics of the study and control providers are reported and similar
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Score 'unclear risk' if it is not clear in the paper (e.g. characteristics are mentioned in the text, but no data were presented)
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Score 'high risk' if there is no report of characteristics in text or tables or if there are differences between control and intervention providers
Was there adequate adjustment for confounding?
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Score 'low risk' if appropriate methods were used to adjust for potential confounding
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Score 'unclear risk' if the methods used to adjust for confounding were not reported in the paper
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Score 'high risk' if potential confounding from the following variables has not been addressed: adjuvant radiotherapy, age, surgical techniques, stage of disease, ductal carcinoma in situ or invasive breast cancer, and chemotherapy
Performance/detection bias
Was knowledge of the allocated interventions adequately prevented during the study?
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Score 'low risk' if the authors state explicitly that the primary outcome variables were assessed blindly, or the outcomes are objective, e.g. length of hospital stay
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Score 'high risk' if the outcomes were not assessed blindly
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Score 'unclear risk' if not specified in the paper
Attrition bias
Were incomplete outcome data adequately addressed?
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Score 'low risk' if missing outcome measures were unlikely to bias the results (e.g. reasons for missingness unlikely to be related to true outcome, missing outcome data balanced in numbers across intervention groups with similar reasons for missing data or missing data have been imputed using appropriate methods)
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Score 'high risk' if missing outcome data was likely to bias the results
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Score 'unclear risk' if not specified in the paper
Reporting bias
Were reports of the study free from selective outcome reporting?
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Score 'low risk' if there is no evidence that outcomes were selectively reported (e.g. the study had a protocol pre‐specifying the outcomes, or all relevant outcomes in the methods section are reported in the results section)
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Score 'high risk' if some pre‐specified outcomes are subsequently omitted from the results
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Score 'unclear risk' if not specified in the paper
Were reports of the study free from selective analysis reporting?
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Score 'low risk' for each outcome if there is no evidence that analyses were selectively reported (e.g. analyses were defined in the methods section of the protocol or paper)
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Score 'high risk' if there is evidence of selective analysis reporting (e.g. multiple adjusted analyses have been carried out and only one reported, or unusual cut‐points have been used for categorising an outcome)
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Score 'unclear risk' if unclear from the paper
Classification of study designs
We will include various study designs and define them as follows.
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Randomised controlled trials: a study in which people are allocated at random to treatment and control arms.
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Prospective cohort study: a group of exposed and non‐exposed individuals that have been followed over time to compare incidence (or rate of death from disease) between the groups (Gordis 1996). In prospective cohort studies, the recruitment, exposure/intervention, and outcomes must all have occurred after setting up the study.
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Retrospective cohort study: a group of exposed and non‐exposed individuals that have been followed over time to compare incidence (or rate of death from disease) between the groups (Gordis 1996). In retrospective cohort studies, outcomes can have occurred prior to setting up the study or be collected afterwards, or both.
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Case‐control study: a study that compares people with a specific outcome of interest (cases) with people from the same source population but without that outcome (controls), to examine the association between the outcome prior exposure.
Measures of treatment effect
We will report time‐to‐event outcomes (e.g. cancer‐related outcomes) as hazard ratios (HRs) with 95% confidence intervals (CIs). If necessary we will estimate HRs using the methods of Parmar 1998. If it is not possible to estimate HRs from all studies, the number of events (e.g. deaths or recurrences) over 5 and 10 years of follow up will be treated as dichotomous outcomes.
We will report dichotomous outcomes (for example surgical revision) as risk ratios (RRs) with 95% CIs. We will pool the data for meta‐analysis using the pooled log‐RR, where appropriate.
The mean difference (MD) will be used as effect measure for continuous outcomes. Some secondary outcomes may be represented by different scales in the individual studies and in this case, the standardised mean difference (SMD) will be used.
Data that will not allow valid analysis will be summarised and presented within an additional summative table.
Unit of analysis issues
We anticipate that the appropriate unit of analysis will be by individual patient, rather than surgical unit, hospital or centre.
Dealing with missing data
These data will be analysed on an intention‐to‐treat basis and we will attempt to obtain missing data from the original trials.
Assessment of heterogeneity
If appropriate, we will assess statistical heterogeneity using the Chi2 statistic (P value less than 0.1). We will also assess heterogeneity between studies using the I2 statistic to examine the percentage of total variation across studies due to heterogeneity rather than chance. An I2 value of 30% to 60% may represent moderate heterogeneity, while values between 50% and 90% may represent substantial heterogeneity, and between 75% and 100% represent considerable heterogeneity (Higgins 2011). If substantial or considerable heterogeneity exists we will use a random‐effects model.
We will investigate the following factors as potential causes of heterogeneity in the included studies using the framework below.
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Clinical diversity: includes study location and setting, full characteristics of participants, co‐morbidity and treatments that participants may be receiving on trial entry. We will consider how outcomes were measured, the definition of outcomes, and how they were recorded. Depending upon the extent of the clinical diversity, we will either analyse studies separately or present the results narratively.
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Methodological diversity: includes assessment of the risk of bias, analytical method, and source of funding.
Assessment of reporting biases
Reporting biases arise when the dissemination of research findings is influenced by the nature and direction of results. Some types of reporting bias (for example publication bias, multiple publication bias, language bias) reduce the likelihood that all studies eligible for a review will be retrieved. If all eligible studies are not retrieved, the review may be biased. In view of the difficulty of detecting and correcting for publication bias and other reporting biases, we will aim to minimise their potential impact by ensuring a comprehensive search for eligible studies and by being alert to duplication of data. If there are 10 or more studies in an analysis, we will use a funnel plot to explore the possibility of small study effects (a tendency for estimates of the intervention effect to be more beneficial in smaller studies). Funnel plots have several limitations, and even when they do not provide any evidence of asymmetry, bias (including publication bias) cannot be excluded. When funnel plots identify potential bias they do not provide a solution.
Data synthesis
We will synthesise data using the Cochrane Collaboration’s statistical software, Review Manager (RevMan 2014). We will include various study designs, and the primary meta‐analysis will differentiate between studies with different study designs. We will base the choice of using fixed‐effect or random‐effects models for data synthesis on a number of factors. Where significant clinical or methodological heterogeneity exists, we will use a random‐effects model. Otherwise we will use the fixed‐effect model. We will combine data using the inverse variance method on the log‐HR scale for time‐to‐event outcomes, on the log‐RR scale for dichotomous outcomes and on the MD or SMD scale for continuous outcomes. For random‐effects meta‐analysis we will use the Dersimonian and Laird method. Where the data are too diverse for combining effect sizes in a meaningful or valid manner, we will present the results of individual studies in table and graphical format and use a narrative approach to summarise the data.
We will create a 'Summary of findings' table using the following outcomes:
Primary outcome:
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Reconstruction failure (defined as loss of implant or expander).
Secondary outcomes:
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Seroma requiring drainage within 30 days.
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Implant infection requiring antibiotic therapy within 30 days.
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Haematoma requiring surgical evacuation under general anaesthesia within 30 days.
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Capsular contracture: defined by the Baker classification, up to 5 years post operatively.
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Cancer‐related outcomes: local and distant recurrence of disease demonstrated on follow‐up imaging (mammogram; MRI; CT); time‐to‐event data for recurrence during the follow‐up period.
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Patient‐related outcomes: defined by validated quality of life questionnaires (for example Breast‐Q).
We will use the GRADEPro GDT software and the GRADE approach (Schünemann 2011) to evaluate the strength of evidence.
Subgroup analysis and investigation of heterogeneity
As we expect to identify a small number of published studies, we do not envisage that it will be possible to perform subgroup analyses. However, where data are available, we plan to undertake the following subgroup analyses:
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High risk participants (for example, Body Mass Index > 30, breast volume > 600g, and active smokers (Lardi 2014)) versus low risk participants.
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Treatment including adjuvant radiotherapy versus treatment not including adjuvant radiotherapy.
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Treatment including neoadjuvant chemotherapy versus treatment not including neoadjuvant chemotherapy.
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Types of ADM including AlloDerm versus NeoForm.
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Differences in comparator groups including a single‐stage implant prothesis versus a two‐stage expander (inflatable breast implant designed to stretch the skin) then implant reconstruction.
If we detect substantial heterogeneity, we plan to explore possible explanations in sensitivity analyses. We plan to take any statistical heterogeneity into account when interpreting the results, especially if there is any variation in the direction of effect.
Sensitivity analysis
We will conduct a sensitivity analyses for the primary outcome to determining whether the conclusions will differ if eligibility is restricted to studies with high methodological quality (low risk of bias).
