Clinical outcomes after small-incision lenticule extraction versus femtosecond laser-assisted LASIK for high myopia: A meta-analysis

Aim To compare postoperative clinical outcomes of high myopia after small-incision lenticule extraction (SMILE) and femtosecond laser-assisted laser in situ keratomileusis (FS-LASIK). Methods From March 2018 to July 2020, PubMed, MEDLINE, Embase, the Cochrane Library, and several Chinese databases were comprehensively searched. The studies meeting the criteria were selected and included; the data were extracted by 2 independent authors. The clinical outcome parameters were analyzed with RevMan 5.3. Results This meta-analysis included twelve studies involving 766 patients (1400 eyes: 748 receiving SMILE and 652 receiving FS-LASIK). Pooled results revealed no significant differences in the following outcomes: the logarithm of the mean angle of resolution (logMAR) of postoperative uncorrected distance visual acuity (weighted mean difference (WMD) = -0.01, 95% confidence interval (CI): -0.02 to 0.00, I2 = 0%, P = 0.07 at 1 mo; WMD = -0.00, 95% CI: -0.01 to 0.01, I2 = 0%, P = 0.83 at 3 mo; WMD = -0.00, 95% CI: -0.01 to 0.00, I2 = 32%, P = 0.33 in the long term), and the postoperative mean refractive spherical equivalent (WMD = -0.03, 95% CI: -0.09 to 0.03, I2 = 13%, P = 0.30). However, the SMILE group had significantly better postoperative corrected distance visual acuity (CDVA) than the FS-LASIK group (WMD = -0.04, 95% CI, -0.05 to -0.02, I2 = 0%, P<0.00001). In the long term, postoperative total higher-order aberration (WMD = -0.09, 95% CI: -0.10 to -0.07, I2 = 7%, P<0.00001) and postoperative spherical aberration (WMD = -0.15, 95% CI: -0.19 to -0.11, I2 = 29%, P<0.00001) were lower in the SMILE group than in the FS-LASIK group; a significant difference was also found in postoperative coma (WMD = -0.05, 95% CI: -0.06 to -0.03, I2 = 30%, P<0.00001). Conclusion For patients with high myopia, both SMILE and FS-LASIK are safe, efficacious and predictable. However, the SMILE group demonstrated advantages over the FS-LASIK group in terms of postoperative CDVA, while SMILE induced less aberration than FS-LASIK. It remains to be seen whether SMILE can provide better visual quality than FS-LASIK; further comparative studies focused on high myopia are necessary.


Rationale
3 Describe the rationale for the review in the context of what is already known, including mention of why a network metaanalysis has been conducted. Objectives 4 Provide an explicit statement of questions being addressed, with reference to participants, interventions, comparisons, outcomes, and study design (PICOS).

Protocol and registration 5
Indicate whether a review protocol exists and if and where it can be accessed (e.g., Web address); and, if available, provide registration information, including registration number. Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale. Clearly describe eligible treatments included in the treatment network, and note whether any have been clustered or merged into the same node (with justification included in the meta-analysis).
Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators. Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made. Geometry of the network S1 Describe methods used to explore the geometry of the treatment network under study and potential biases related to it. This should include how the evidence base has been graphically summarized for presentation, and what characteristics were compiled and used to describe the evidence base to readers. Risk of bias within individual studies 12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

Summary measures 13
State the principal summary measures (e.g., risk ratio, difference in means). Also describe the use of additional summary measures assessed, such as treatment rankings and surface under the cumulative ranking curve (SUCRA) values, as well as modified approaches used to present summary findings from meta-analyses. Planned methods of analysis 14 Describe the methods of handling data and combining results of studies for each network meta-analysis. This should include, but not be limited to:  Handling of multi-arm trials;  Selection of variance structure;  Selection of prior distributions in Bayesian analyses; and  Assessment of model fit.

S2
Describe the statistical methods used to evaluate the agreement of direct and indirect evidence in the treatment network(s) studied. Describe efforts taken to address its presence when found.

Risk of bias across studies 15
Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).

Additional analyses 16
Describe methods of additional analyses if done, indicating which were pre-specified. This may include, but not be limited to, the following: 9#&11#

Study selection 17
Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram. Presentation of network structure

S3
Provide a network graph of the included studies to enable visualization of the geometry of the treatment network. Summary of network geometry

S4
Provide a brief overview of characteristics of the treatment network. This may include commentary on the abundance of trials and randomized patients for the different interventions and pairwise comparisons in the network, gaps of evidence in the treatment network, and potential biases reflected by the network structure. Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.

Risk of bias within studies 19
Present data on risk of bias of each study and, if available, any outcome level assessment.

Results of individual studies 20
For all outcomes considered (benefits or harms), present, for each study: 1) simple summary data for each intervention group, and 2) effect estimates and confidence intervals.
Modified approaches may be needed to deal with information from larger networks.

Synthesis of results 21
Present results of each meta-analysis done, including confidence/credible intervals. In larger networks, authors may focus on comparisons versus a particular comparator (e.g. placebo or standard care), with full findings presented in an appendix. League tables and forest plots may be considered to summarize pairwise comparisons. If additional summary measures were explored (such as treatment rankings), these should also be presented.

Exploration for inconsistency S5
Describe results from investigations of inconsistency. This may include such information as measures of model fit to compare consistency and inconsistency models, P values from statistical tests, or summary of inconsistency estimates from different parts of the treatment network.

Risk of bias across studies 22
Present results of any assessment of risk of bias across studies for the evidence base being studied.

Results of additional analyses 23
Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression analyses, alternative network geometries studied, alternative choice of prior distributions for Bayesian analyses, and so forth).

Summary of evidence 24
Summarize the main findings, including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policymakers). Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review level (e.g., incomplete retrieval of identified research, reporting bias). Comment on the validity of the assumptions, such as transitivity and consistency. Comment 9#&11# Figure 1 Supplementary

Funding 27
Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review. This should also include information regarding whether funding has been received from manufacturers of treatments in the network and/or whether some of the authors are content experts with professional conflicts of interest that could affect use of treatments in the network. PICOS = population, intervention, comparators, outcomes, study design. * Text in italics indicateS wording specific to reporting of network meta-analyses that has been added to guidance from the PRISMA statement. † Authors may wish to plan for use of appendices to present all relevant information in full detail for items in this section.

Box. Terminology: Reviews With Networks of Multiple Treatments
Different terms have been used to identify systematic reviews that incorporate a network of multiple treatment comparisons. A brief overview of common terms follows.
Indirect treatment comparison: Comparison of 2 interventions for which studies against a common comparator, such as placebo or a standard treatment, are available (i.e., indirect information). The direct treatment effects of each intervention against the common comparator (i.e., treatment effects from a comparison of interventions made within a study) may be used to estimate an indirect treatment comparison between the 2 interventions (Appendix Figure 1, A). An indirect treatment comparison (ITC) may also involve multiple links. For example, in Appendix Figure 1, B, treatments B and D may be compared indirectly on the basis of studies encompassing comparisons of B versus C, A versus C, and A versus D.
Network meta-analysis or mixed treatment comparison: These terms, which are often used interchangeably, refer to situations involving the simultaneous comparison of 3 or more interventions. Any network of treatments consisting of strictly unclosed loops can be thought of as a series of ITCs (Appendix Figure 1, A  and B). In mixed treatment comparisons, both direct and indirect information is available to inform the effect size estimates for at least some of the comparisons; visually, this is shown by closed loops in a network graph (Appendix Figure 1, C).
Closed loops are not required to be present for every comparison under study. "Network meta-analysis" is an inclusive term that incorporates the scenarios of both indirect and mixed treatment comparisons.
Network geometry evaluation: The description of characteristics of the network of interventions, which may include use of numerical summary statistics. This does not involve quantitative synthesis to compare treatments. This evaluation describes the current evidence available for the competing interventions to identify gaps and potential bias. Network geometry is described further in Appendix Box 4.

Appendix Box 1. The Assumption of Transitivity for Network Meta-Analysis
Methods for indirect treatment comparisons and network meta-analysis enable learning about the relative treatment effects of, for example, treatments A and B through use of studies where these interventions are compared against a common therapy, C.
When planning a network meta-analysis, it is important to assess patient and study characteristics across the studies that compare pairs of treatments. These characteristics are commonly referred to as effect modifiers and include traits such as average patient age, gender distribution, disease severity, and a wide range of other plausible features.
For network meta-analysis to produce valid results, it is important that the distribution of effect modifiers is similar, for example, across studies of A versus B and A versus C. This balance increases the plausibility of reliable findings from an indirect comparison of B versus C through the common comparator A. When this balance is present, the assumption of transitivity can be judged to hold.
Authors of network meta-analyses should present systematic (and even tabulated) information regarding patient and study characteristics whenever available. This information helps readers to empirically evaluate the validity of the assumption of transitivity by reviewing the distribution of potential effect modifiers across trials.