What about anterior endocrowns?

A Commentary On: Fehrenbach J, de Soares J L S, do Nascimento Foly J C S, Miotti L L, Münchow E A Mechanical performance of endocrown restorations in anterior teeth: A systematic review and network meta-analysis. Dent Mater 2025; https://doi.org/10.1016/j.dental.2024.10.012 .

Objective And Method: A systematic review and network meta-analysis (NMA) of anterior endocrown mechanical performance compared to conventional crowns on endodontically treated teeth (ETT). An online search of major databases was conducted and relevant studies were selected. Data was extracted and meta-analyses performed comparing mechanical outcomes of anterior endocrowns to other crown systems. Endocrowns on the anterior dentition can be considered if mechanical fracture is a concern, however more research needs to be done before definitive case selection.

Data Sources: Two independent reviewers searched six electronic databases (PubMed, Scopus, Web of Science, Embase, SciELO and LILACS) using compounded terms like ETT and anterior and endocrown along with their variates. Manual searches were also done from the reference list of chosen studies. Following the search titles were put through Rayyan, an AI-powered systematic reviewer to remove duplicates. The reviewers then screened 25 random titles and abstracts independently and repeated this with a third reviewer.

Study Selection: Inclusion criteria were studies examining anterior ETT and endocrown compared with a different restoration. Exclusion criteria were non-experimental studies, theses, languages other than English Spanish or Portuguese and studies not evaluating mechanical performance. From this 24 studies were read in full and 11 were selected for the review. Bias risk assessment was done by two reviewers using the RoBDEMAT tool for in vitro studies and ROBFEAD tool for Finite Element Analysis (FEA) studies. The studies are scored as low, med, or high risk according to each tool's domains.

Data Extraction And Synthesis: Two reviewers extracted the data onto Microsoft Excel including authors name and year of publication, tooth type, endocrown depth, distance between the CEJ and finishing line,surface treatment, luting agent, thermocycling method, etc. Data synthesis and analysis was performed by MetaInsight V4 4.0.0 and two independent NMA's were made. The first NMA used load-to-fracture data and a mean difference outcome while the second used irreparable fracture data and a risk ratio outcome. Both used a 95% credible interval(Crl). Comparisons were evaluated using Surface Under the Cumulative ranking Curve (SUCRA), where a value closer to 100% indicates greater resistance to fracture.

Results: Eight studies used in vitro experiments, most of them investigating load-to-fracture and failure mode of fractured restorations and one study the pull-out bond strength. The remaining three studies used FEA models and examined von mises criterion by quantifying a material's fracture under stress. The first NMA considered load-to-fracture data and examined five studies. The probabilistic analysis showed teeth restored with a Glass Fibre Post (GFP) and composite crown performed best (SUCRA = 95.23%), with higher load-to-fracture values (MD 165.0, 95% CRL 28.3, 301.0) than the Composite endocrown group (MD 78.1 95% Crl 6.21, 150.0). Lowest ranking was the GFP plus ceramic crown group, with ceramic endocrowns and ceramic crowns without post in between. The second NMA analysed irreparable fracture data from five studies. The composite endocrown group showed a lower catastrophic fracture risk (RR 0.413, 95% Crl 0.152, 0.859) compared to the GFP ceramic crown group (RR 1.36, 95% Crl 1.09, 1.80). Probabilistic analysis shows composite endocrowns with the lowest risk of irreparable fracture (SUCRA = 96.84%), followed by ceramic crown with no core, ceramic endocrowns and GFP crown groups with the highest risk.

Conclusion: Endocrowns may be a viable choice for endodontically treated anterior teeth because they perform similarly to other restorative systems under mechanical load in lab and model scenarios. GRADE rating is low due to high risk of bias.