Efficacy of biologics in root coverage and gingival augmentation therapy: An American Academy of Periodontology best evidence systematic review and network meta-analysis

BACKGROUND

The aim of this systematic review was to assess the efficacy of three biologics, namely autologous blood-derived products (ABPs), enamel matrix derivatives (EMD) and recombinant human platelet-derived growth factor BB (rhPDGF-BB), in root coverage and gingival augmentation therapy.

METHODS

The protocol of this PRISMA 2020-compliant systematic review was registered in PROSPERO (CRD42021285917). After study selection, data of interest were extracted. A network meta-analysis (NMA) was conducted to assess the effect of different surgical interventions on the main clinical outcomes of interest (i.e., mean root coverage [MRC%], complete root coverage [CRC%], keratinized tissue width [KTW], gingival thickness [GT] change, and recession depth [RD] reduction).

RESULTS

A total of 48 trials reported in 55 articles were selected. All studies reported on the treatment of gingival recession defects for root coverage purposes. Forty-six treatment arms from 24 trials were included in the NMA. These arms consisted of treatment with coronally advanced flap (CAF) alone, EMD + CAF, platelet-rich fibrin (PRF) + CAF, and subepithelial connective tissue graft (SCTG) + CAF. Regarding MRC%, SCTG+CAF was associated with a significant higher estimate (13.41%, 95% CI [8.06-18.75], P <  0.01), while EMD+CAF (6.68%, 95% CI [-0.03 to 13.4], P = 0.061) and PRF+CAF (1.03%, 95% CI [-5.65 to 7.72], P = 0.71) failed to show statistically significant differences compared with CAF alone (control group) or with each other. Similarly, only SCTG+CAF led to a significantly higher CRC% (14.41%, 95% CI [4.21 to 24.61], P < 0.01), while treatment arms EMD + CAF (13.48%, 95% CI [-3.34 to 30.32], P = 0.11) and PRF+CAF (-0.91%, 95% CI [-15.38, 13.57], p = 0.81) did not show significant differences compared with CAF alone or with each other. Differences in the CI of PRF+CAF (symmetrical around a zero adjunctive effect) and EMD+CAF (non-symmetrical) suggest that EMD could have some additional value compared with PRF. Treatment with SCTG+CAF led to a statistically significant higher RD reduction (-0.39 mm, 95% CI [-0.55 to 0.22], P < 0.01), however EMD+CAF (-0.13 mm, 95% CI [-0.29 to 0.01], P = 0.08) and PRF+CAF (-0.06 mm, 95% CI [-0.23 to 0.09], P = 0.39) failed to show significant differences compared with CAF or with each other. While SCTG+CAF was associated with a statistically significant higher gain of KTW (0.71 mm, 95% CI [0.48 to 0.93], P < 0.01), EMD+CAF (0.24 mm, 95% CI [-0.02 to 0.51], P = 0.08) and PRF+CAF (0.08 mm, 95% CI [-0.23 to 0.41], P = 0.58) did not result into significant changes compared with CAF alone or with each other. Regarding the use of rhPDGF-BB+CAF, although available studies have reported equivalent results compared with SCTG+CAF, evidence is very limited.

CONCLUSIONS

The use of ABPs, EMD, or rhPDGF-BB in conjunction with a CAF for root coverage purposes is safe and generally promotes significant improvements respective to baseline clinical parameters. However, the adjunctive use of ABPs and EMD does not provide substantial additional improvements in terms of clinical outcomes and patient-reported outcome measures to those achieved using CAF alone, when baseline KTW is >2 mm. Both PRF+CAF and EMD+CAF rendered inferior MRC%, CRC%, RD reduction, and KTW gain compared with SCTG+CAF, which should still be considered the gold-standard in root coverage therapy. Although some studies have reported equivalent results for rhPDGF-BB+CAF compared with the gold-standard intervention, limited evidence precludes formal comparisons with CAF or SCTG+CAF that could be extrapolated to guide clinical practice.

Efficacy of biologics for the treatment of periodontal infrabony defects: An American Academy of Periodontology best evidence systematic review and network meta-analysis

BACKGROUND

A large variety of biomaterials, biologics and membranes have been utilized in the past 40 years for the regenerative treatment of periodontal infrabony defects. Biologic agents have progressively gained popularity among clinicians and are routinely used for periodontal regeneration. In alignment with the goals of the American Academy of Periodontology (AAP) Best Evidence Consensus (BEC) on the use of biologic mediators in contemporary clinical practice, the aim of this sytematic review was to evaluate the effect of biologic agents, specifically autogenous blood-dervied products (ABPs), enamel matrix derivative (EMD) and recombinant human platelet-derived growth factor-BB (rhPDGF-BB), on the regenerative outcomes of infrabony defects.

METHODS

A detailed systematic search was conducted to identify eligible randomized control trials (RCTs) reporting the outcomes of periodontal regenerative therapy using biologics for the treatment of infrabony defects. A frequentist mixed-modeling approach to network meta-analysis (NMA), characterized by the assessment of three individual components for the treatment of an infrabony defect (the bone graft material [BG], the biologic agent, the application of a barrier membrane) was performed to evaluate and compare the relative efficacy of the different components, on the outcomes of different therapeutic modalities of periodontal regeneration.

RESULTS

A total of 153 eligible RCTs were included, with 150 studies contributing to the NMA. The quantitative analysis showed that the addition of biologic agents to bone graft significantly improves the clinical and radiographic outcomes, as compared to BG and flap procedures alone. Barrier membranes enhanced the regenerative outcomes of BG but did not provide further benefits in combination with biologics. The type of BG (autogenous, allogeneic, xenogeneic or alloplastic) and the biologic agent (EMD, platelet-rich fibrin [PRF], platelet-rich plasma [PRP] or rhPDGF-BB) played a significant role on the final outcomes of infrabony defects. Allogeneic and xenogeneic BGs exhibited statistically significantly superior clinical gain than synthetic and autogenous BGs (p < 0.05 in all the comparisons), while rhPDGF-BB and PRF demonstrated significantly higher stability of the gingival margin (p < 0.01) and radiographic bone fill/gain (p < 0.05), together with greater, although not statistically significant, clinical attachment level gain and pocket depth reduction, than EMD and PRP. Overall, rhPDGF-BB exhibited the largest effect size for most parameters, including clinical attachment level gain, pocket depth reduction, less gingival recession and radiographic linear bone gain. Considering the relatively high number of trials presenting an unclear or high risk of bias, the strength of recommendation supporting the use of PRP was judged weak, while the recommendation for EMD, PRF and rhPDGF-BB was deemed in favor.

CONCLUSIONS

Biologics enhance the outcomes of periodontal regenerative therapy. Combination therapies involving BGs + biologics or BGs + barrier membrane demonstrated to be superior to monotherapies. The choice of the type of BG and biologic agent seems to have significant impact on the clinical and radiographic outcomes of infrabony defects.

Aesthetic-And patient-related outcomes following root coverage procedures: A systematic review and network meta-analysis

BACKGROUND

Aim of this systematic review (SR) of randomized controlled trials (RCTs) was to evaluate effect of different flap designs and graft materials for root coverage, in terms of aesthetics, patient satisfaction and self-reported morbidity (post-operative pain/discomfort).

MATERIAL AND METHODS

A comprehensive literature search was performed. A mixed-modelling approach to network meta-analysis was utilized to formulate direct and indirect comparisons among treatments for Root Coverage Esthetic Score (RES), with its individual components, and for subjective patient-reported satisfaction and post-operative pain/discomfort (visual analogue scale (VAS) of 100).

RESULTS

Twenty-six RCTs with a total of 867 treated patients (1708 recessions) were included. Coronally Advanced Flap (CAF) + Connective Tissue Graft (CTG) (0.74 (95% CI [0.24, 1.26], p = .005)), Tunnel (TUN) + CTG (0.84 (95% CI [0.15, 1.53]), p = .01) and CAF + Graft substitutes (GS) (0.55 (95% CI [0.006, 1.094], p = .04)) were significantly associated with higher RES than CAF. No significant difference between CAF + CTG and TUN + CTG was detected (0.09 (95% CI [-0.54, 0.72], p = .77)). Addition of CTG resulted in less natural tissue texture (-0.21 (95% CI [-0.34, -0.08]), p = .003) and gingival colour (-0.06 (95% CI [-0.12, -0.03], p = .03)) than CAF. CTG techniques were associated with increased morbidity.

CONCLUSIONS

Connective tissue graft procedures showed highest overall aesthetic performance for root coverage, although graft integration might impair soft tissue colour and appearance. Additionally, CTG-based techniques were also correlated with a greater patient satisfaction and morbidity.

The Effect of Time on Root Coverage Outcomes: A Network Meta-analysis

The stability of root coverage outcomes has gained a great deal of interest. However, insufficient evidence is available, mainly due to limited direct comparisons among different techniques and the small sample size among clinical trials. Therefore, the aim of this study was to propose a mixed-models network meta-analysis (NMA) that includes the novelty of assessing time on root coverage outcomes while simultaneously comparing different surgical approaches. A literature search was performed by 2 individual reviewers to identify randomized clinical trials (RCTs) reporting the outcomes of root coverage procedures of at least 2 time points to estimate the slopes of different treatment approaches. The primary outcomes were the changes in slopes for recession depth (REC), keratinized tissue width (KTW), and clinical attachment level. Sixty RCTs with a total of 2,554 gingival recessions (1,864 patients) were included in the NMA. Connective tissue graft (CTG) and enamel matrix derivative (EMD) approaches provided superior initial REC reduction compared to flap advancement alone. However, only CTG-based procedures were effective in maintaining the stability of the gingival margin over time, while EMD, acellular dermal matrix, collagen matrix, and flap alone showed a similar tendency for gingival recession recurrence. Baseline REC and KTW at the earliest postoperative recall were predictors for the stability of the gingival margin. In addition, a geographic center effect on the treatment slopes was observed for REC and KTW. While limitations of the present linear mixed-modeling approach should be considered as it refers to estimation and comparison of time slopes based on an examined while linear framework, the designed NMA showed to be an effective tool for the simultaneous comparison of multiple treatment approaches while taking into account the critical element of time.

Estimating the variance for heterogeneity in arm-based network meta-analysis

Network meta-analysis can be implemented by using arm-based or contrast-based models. Here we focus on arm-based models and fit them using generalized linear mixed model procedures. Full maximum likelihood (ML) estimation leads to biased trial-by-treatment interaction variance estimates for heterogeneity. Thus, our objective is to investigate alternative approaches to variance estimation that reduce bias compared with full ML. Specifically, we use penalized quasi-likelihood/pseudo-likelihood and hierarchical (h) likelihood approaches. In addition, we consider a novel model modification that yields estimators akin to the residual maximum likelihood estimator for linear mixed models. The proposed methods are compared by simulation, and 2 real datasets are used for illustration. Simulations show that penalized quasi-likelihood/pseudo-likelihood and h-likelihood reduce bias and yield satisfactory coverage rates. Sum-to-zero restriction and baseline contrasts for random trial-by-treatment interaction effects, as well as a residual ML-like adjustment, also reduce bias compared with an unconstrained model when ML is used, but coverage rates are not quite as good. Penalized quasi-likelihood/pseudo-likelihood and h-likelihood are therefore recommended.

Using structural equation modeling for network meta-analysis

BACKGROUND

Network meta-analysis overcomes the limitations of traditional pair-wise meta-analysis by incorporating all available evidence into a general statistical framework for simultaneous comparisons of several treatments. Currently, network meta-analyses are undertaken either within the Bayesian hierarchical linear models or frequentist generalized linear mixed models. Structural equation modeling (SEM) is a statistical method originally developed for modeling causal relations among observed and latent variables. As random effect is explicitly modeled as a latent variable in SEM, it is very flexible for analysts to specify complex random effect structure and to make linear and nonlinear constraints on parameters. The aim of this article is to show how to undertake a network meta-analysis within the statistical framework of SEM.

METHODS

We used an example dataset to demonstrate the standard fixed and random effect network meta-analysis models can be easily implemented in SEM. It contains results of 26 studies that directly compared three treatment groups A, B and C for prevention of first bleeding in patients with liver cirrhosis. We also showed that a new approach to network meta-analysis based on the technique of unrestricted weighted least squares (UWLS) method can also be undertaken using SEM.

RESULTS

For both the fixed and random effect network meta-analysis, SEM yielded similar coefficients and confidence intervals to those reported in the previous literature. The point estimates of two UWLS models were identical to those in the fixed effect model but the confidence intervals were greater. This is consistent with results from the traditional pairwise meta-analyses. Comparing to UWLS model with common variance adjusted factor, UWLS model with unique variance adjusted factor has greater confidence intervals when the heterogeneity was larger in the pairwise comparison. The UWLS model with unique variance adjusted factor reflects the difference in heterogeneity within each comparison.

CONCLUSION

SEM provides a very flexible framework for univariate and multivariate meta-analysis, and its potential as a powerful tool for advanced meta-analysis is still to be explored.

Node-Splitting Generalized Linear Mixed Models for Evaluation of Inconsistency in Network Meta-Analysis

BACKGROUND

Network meta-analysis for multiple treatment comparisons has been a major development in evidence synthesis methodology. The validity of a network meta-analysis, however, can be threatened by inconsistency in evidence within the network. One particular issue of inconsistency is how to directly evaluate the inconsistency between direct and indirect evidence with regard to the effects difference between two treatments. A Bayesian node-splitting model was first proposed and a similar frequentist side-splitting model has been put forward recently. Yet, assigning the inconsistency parameter to one or the other of the two treatments or splitting the parameter symmetrically between the two treatments can yield different results when multi-arm trials are involved in the evaluation.

OBJECTIVES

We aimed to show that a side-splitting model can be viewed as a special case of design-by-treatment interaction model, and different parameterizations correspond to different design-by-treatment interactions.

METHODS

We demonstrated how to evaluate the side-splitting model using the arm-based generalized linear mixed model, and an example data set was used to compare results from the arm-based models with those from the contrast-based models.

RESULTS & CONCLUSIONS

The three parameterizations of side-splitting make slightly different assumptions: the symmetrical method assumes that both treatments in a treatment contrast contribute to inconsistency between direct and indirect evidence, whereas the other two parameterizations assume that only one of the two treatments contributes to this inconsistency. With this understanding in mind, meta-analysts can then make a choice about how to implement the side-splitting method for their analysis.

Statistical Models and Methods for Network Meta-Analysis

Meta-analysis, the methodology for analyzing the results from multiple independent studies, has grown tremendously in popularity over the last four decades. Although most meta-analyses involve a single effect size (summary result, such as a treatment difference) from each study, there are often multiple treatments of interest across the network of studies in the analysis. Multi-treatment (or network) meta-analysis can be used for simultaneously analyzing the results from all the treatments. However, the methodology is considerably more complicated than for the analysis of a single effect size, and there have not been adequate explanations of the approach for agricultural investigations. We review the methods and models for conducting a network meta-analysis based on frequentist statistical principles, and demonstrate the procedures using a published multi-treatment plant pathology data set. A major advantage of network meta-analysis is that correlations of estimated treatment effects are automatically taken into account when an appropriate model is used. Moreover, treatment comparisons may be possible in a network meta-analysis that are not possible in a single study because all treatments of interest may not be included in any given study. We review several models that consider the study effect as either fixed or random, and show how to interpret model-fitting output. We further show how to model the effect of moderator variables (study-level characteristics) on treatment effects, and present one approach to test for the consistency of treatment effects across the network. Online supplemental files give explanations on fitting the network meta-analytical models using SAS.

Using Generalized Linear Mixed Models to Evaluate Inconsistency within a Network Meta-Analysis

BACKGROUND

Network meta-analysis compares multiple treatments by incorporating direct and indirect evidence into a general statistical framework. One issue with the validity of network meta-analysis is inconsistency between direct and indirect evidence within a loop formed by three treatments. Recently, the inconsistency issue has been explored further and a complex design-by-treatment interaction model proposed.

OBJECTIVE

The aim of this article was to show how to evaluate the design-by-treatment interaction model using the generalized linear mixed model.

METHODS

We proposed an arm-based approach to evaluating the design-by-treatment inconsistency, which is flexible in modeling different types of outcome variables. We used the smoking cessation data to compare results from our arm-based approach with those from the standard contrast-based approach.

RESULTS

Because the contrast-based approach requires transformation of data, our example showed that such a transformation may yield biases in the treatment effect and inconsistency evaluation, when event rates were low in some treatments. We also compared contrast-based and arm-based models in the evaluation of design inconsistency when different heterogeneity variances were estimated, and the arm-based model yielded more accurate results.

CONCLUSIONS

Because some statistical software commands can detect the collinearity among variables and automatically remove the redundant ones, we can use this advantage to help with placing the inconsistency parameters. This could be very useful for a network meta-analysis involving many designs and treatments.