Systematic Review and Meta-Analysis in Facial Plastic Surgery




Systematic reviews and meta-analyses hold a unique position in the pyramid of evidence. They can provide transparent and rigorous summaries to answer many clinical questions in facial plastic surgery. They can also identify areas of research deficiency, create new knowledge, and support guidelines or policies. A well-conducted systematic review follows a structured process to minimize bias and ensure reproducibility. When appropriate, a meta-analysis is incorporated to provide a statistical synthesis that combines the results of individual studies. This powerful quantitative method is becoming more prevalent in facial plastic surgery. This article provides a practical framework to understand and conduct this valuable type of research.


Key points








  • Systematic reviews of the literature involve rigorous methods analogous to primary research studies. Investigators collect, analyze, and interpret data in an explicit, reproducible manner to avoid bias.



  • Meta-analysis involves statistical pooling of data derived from multiple studies. To avoid bias in data selection, meta-analyses should be based on an underlying systematic review.



  • Systematic reviews and meta-analyses strengthen the evidence base in facial plastic surgery. Functional rhinoplasty, facial reanimation, facial reconstruction, and wound healing are among several areas with potential for enhancing level of evidence.



  • In facial plastic surgery, accruing well-designed original studies improves the data set available for systematic reviews and meta-analyses.



  • Current challenges include limited numbers of studies, weaknesses of study design/methods, and inconsistency in outcomes and definitions.






Introduction


Facial plastic and reconstructive surgery is a highly specialized but remarkably diverse specialty, ranging from cosmetic rhinoplasty and facial rejuvenation surgery to craniofacial trauma reconstruction, cleft lip and palate surgery, microvascular surgery, and facial reanimation. In an era of evidence-based medicine, this diversity presents unique challenges and opportunities for facial plastic surgeons. Patients, practitioners, policy-makers, and third-party payers all increasingly seek evidence-based answers to specific clinical questions: How prevalent is this clinical problem? What are the risk factors for a particular complication? How effective is one surgical procedure compared with another? Systematic reviews and meta-analyses provide transparent and rigorous summaries of the best available evidence. They are an important addition to the literature because the conclusions play a critical role in developing practice guidelines, identifying gaps in knowledge, defining surgical quality metrics, and allocating resources.




Introduction


Facial plastic and reconstructive surgery is a highly specialized but remarkably diverse specialty, ranging from cosmetic rhinoplasty and facial rejuvenation surgery to craniofacial trauma reconstruction, cleft lip and palate surgery, microvascular surgery, and facial reanimation. In an era of evidence-based medicine, this diversity presents unique challenges and opportunities for facial plastic surgeons. Patients, practitioners, policy-makers, and third-party payers all increasingly seek evidence-based answers to specific clinical questions: How prevalent is this clinical problem? What are the risk factors for a particular complication? How effective is one surgical procedure compared with another? Systematic reviews and meta-analyses provide transparent and rigorous summaries of the best available evidence. They are an important addition to the literature because the conclusions play a critical role in developing practice guidelines, identifying gaps in knowledge, defining surgical quality metrics, and allocating resources.




What is a systematic review


Early efforts to summarize evidence in clinical medicine took the form of narrative expert reviews. They lacked clear structure and were subject to the author’s bias in the selection of the literature and the synthesis of the findings. Conversely, the systematic review follows a structured and reproducible process for searching, selecting, and summarizing the available evidence. This process minimizes bias and provides transparent and reliable answers to clinical questions. The process starts with formulating a focused clinical question and is followed by a comprehensive review of the medical literature. Explicit criteria then determine which studies are used to formulate a clinical summary of the findings. Systematic reviews with meta-analyses can summarize the best available evidence to answer many clinical questions in facial plastic surgery.




How to conduct a systematic review


The systematic review is analogous to primary research in that one reports methods, data collection, and analysis. First, one defines a focused review question and specifies a search strategy of the medical literature that captures most, if not all, of the relevant literature. The review proceeds to identify the eligible studies and evaluate the quality of the available evidence. Frequently, a systematic review is then combined with a meta-analysis, although they are methodologically distinct.


Defining the Research Question


The first, and sometimes most difficult, step is to define the objective of the systematic review. This objective can usually be expressed as a specific clinical question. The acronym PICOT is sometimes used to describe key components of the research question: P opulation, I ntervention, C omparison, O utcome, and T ime. It is advisable to survey the available literature to guide the development of a feasible research question. This consideration is particularly relevant in facial plastic surgery, where the small sample size, difficulty of randomizing surgical patients, and the inconsistent outcome measures limit the research data. It is important to determine whether the research question is dealing with cause, diagnosis, intervention, prognosis, or cost. The type of the research question dictates the most suitable study design and the potential biases that may influence findings. For example, when one wants to evaluate if perioperative steroids decrease perioperative edema and ecchymosis following rhinoplasty, the highest quality studies should be randomized clinical trials (RCTs). However, if the review question is examining which facial nerve outcome scale has the best reliability and validity, the studies are cohorts of patients with facial nerve deficit.


Developing a Search Strategy


Systematic reviews are distinguished from other reviews by the well-structured, explicit, and reproducible search strategy. The strategy is designed based on the PICOT components of the review question. Although the goal is to capture all the relevant studies, increasing the comprehensiveness (or sensitivity) of a search reduces its precision and therefore yields many nonrelevant studies. The search should strike a favorable balance between being comprehensive, yet relevant and manageable. Navigating though databases, such as MEDLINE, EMBASE, or CENTRAL, can be technically demanding, and collaborating with a health care librarian is strongly recommended. Each database has developed specific “controlled vocabulary” and filters to retrieve the studies of interest from millions of publications. It is important that the search is performed in more than one database using controlled vocabulary and regular text words. Filters and limit terms can be added to refine the search, such as a language, publication date, study design, or population age. Although most systematic reviews are limited to the published literature, some review questions call for searching though dissertations, trial registries, meeting abstracts, or even contacting agencies or health providers. This is important in areas were publication bias is thought to heavily influence the results, such as adverse events and complications. Finally, the retrieved articles from several databases and any unpublished articles are merged together in a master library and duplicates removed. Fig. 1 illustrates the value of a comprehensive search strategy that uses more than one database and possibly includes unpublished results.




Fig. 1


Venn diagram showing the interrelationship of several databases that may be used in a systematic review. Note the spread of data, spanning the three databases and the unpublished data, which is not usually retrievable in electronic searches.


Identifying the Evidence


Once the pool of candidate articles has been accumulated, the reviewers then determine which articles meet the defined criteria for inclusion. The inclusion and exclusion criteria should be clearly specified a priori. It is typical for the search to retrieve several hundreds or even thousands of articles that need to be distilled to reach a handful of eligible studies. This process is often done in two stages. First, the reviewers screen the titles and abstracts to identify any potential articles. Subsequently, two independent reviewers evaluate the screened publications using the inclusion and exclusion criteria. Disagreements between the two reviewers are resolved by consensus or by a third adjudicator (kappa statistical can be provided as a measure of disagreement). This provides validity to the selection process by minimizing bias or arbitrary selection. It is helpful when the reviewers perform piloting to ensure that the selection criteria are clear and reproducible. If the criteria are vague and are heavily influenced by subjective interpretation, there may be substantial disagreements that call into question the reliability (reproducibility) of the selection process. Once the eligible studies are finalized, the data are extracted from each study using standardized data extraction forms. Usually the methodologic details, sample size, and numerical results are summarized into a review table. An example data extraction form is available from the Cochrane collaboration ( www.cochrane-renal.org/docs/data_extraction_form.doc ).


Evaluating Quality of Evidence and Bias


Rigorous quality control is an important feature of the systematic review. The methodologic quality of each included study is evaluated with particular focus on the risk of bias. Bias introduces systematic deviation from the truth and can corrupt the results of the study. Experimental and observational studies alike can be subject to the five classic types of bias: (1) reporting bias, (2) selection bias, (3) performance bias, (4) attrition bias, and (5) detection bias. Table 1 summarizes these biases and provides practical examples. Reporting (publication) bias refers to a deviation typically toward favorable results in published studies compared with unpublished studies. Selection bias occurs when each group is selected differently causing incomparable groups with regard to important baseline characteristics and predictors of outcome. In studies of etiology or prognosis, selection bias can lead to a confounding effect. This is a distortion of the association between the exposure and the outcome because the study groups differ with respect to other factors that influence the outcome. Performance bias is a result of major differences in care among groups that influence the outcome. Attrition bias occurs if the rate of withdrawal was unequal between the study groups. Withdrawals from the study lead to incomplete outcome data that influence the analysis. Detection bias is a systematic difference between groups in how the outcome is determined or assessed. There are several instruments developed to evaluate the risk of bias based on the methodologic quality of the study. The QUADAS (quality assessment of diagnostic accuracy studies) and Jadad scale (a brief instrument that evaluates risk of bias in RCTs) are among the commonly used instruments. The risk of bias is dictated by the specifics of the review question. For example, the risk of detection bias (how outcome is evaluated) can range from substantial if the outcome is “soft” (eg, surgeon rating of rhinoplasty outcome) to minimal if the outcome is “hard” (eg, pneumothorax after rib harvest). Accordingly, masking of the individual assessing the outcome is very important in the case of soft outcome but less critical in the case of hard outcome.



Table 1

Summary of five classic types of bias that are evaluated in primary studies


































Type of Bias Definition Example How to Minimize
Reporting bias (publication bias) There is deviation, typically toward favorable results, in published studies compared with unpublished studies. Several RCTs evaluate if a new laser delivery improves facial aging compared with an existing treatment. Only the trials with statistically significant improvement are published; studies that encountered complications might also be suppressed. Inclusion of unpublished data in the systematic review and meta-analysis provides a more balanced review.
Selection bias Each group is selected differently, causing incomparable groups with regard to important baseline characteristics and predictors of outcome. An RCT compares the effect of perioperative steroid vs placebo on decreasing facial edema after septorhinoplasty. Patients with more extensive osteotomies and bone mobilization were allocated to the treatment group. In observational studies, rigorous cohort enrollment and adjustment methods, if required. In RCTs, allocation concealment can prevent biased selection.
Performance bias Substantial differences in care among groups influence the outcome. A cohort study evaluates if antibiotic use after laser resurfacing decreases the risk of infection. The treating physicians were not masked and were more likely to add topical antibiotic treatment to the control group. Masking the health care providers to be unaware of patient allocation and treatment.
Attrition bias The rate of withdrawal is unequal between the study groups, leading to incomplete data that influence the analysis. A study compares repeated filler injection with placebo. Patients receiving placebo injections were more likely to drop out or not comply with follow-up. Masking of patients may decrease aspects related to patient perception.
Detection bias A systematic difference exists between groups in how the outcome is determined or assessed. A study compares two methods of rhinoplasty using surgeon-based outcome. Surgeons’ perception and beliefs influence the evaluation of outcome. Masking the assessors of outcome to minimize the influence of their beliefs and perceptions.

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Feb 8, 2017 | Posted by in General Surgery | Comments Off on Systematic Review and Meta-Analysis in Facial Plastic Surgery
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