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Accelerated Fatigue Model for Predicting Composite Restoration Failure

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An empirical method is proposed to predict the clinical performance of resin composite dental restorations by using laboratory data derived from simple specimens subjected to chemical degradation and accelerated cyclic… Click to show full abstract

An empirical method is proposed to predict the clinical performance of resin composite dental restorations by using laboratory data derived from simple specimens subjected to chemical degradation and accelerated cyclic fatigue. Three resin composites were used to fill dentin disks (2-mm inner diameter, 5-mm outer diameter, and 2 mm thick) made from bovine incisor roots. The specimens (n = 30 per group) were aged with different durations of a low-pH challenge (0, 24, and 48 h under pH 4.5) before being subjected to diametral compression with either a monotonically increasing load (fast fracture) or a cyclic load with a continuously increasing amplitude (accelerated fatigue). The data from 1 material were used to establish the relationship between laboratory time (number of cycles) and clinical time to failure (years) via the respective survival probability curves. The temporal relationship was then used to predict the clinical rates of failure for restorations made of the other 2 materials, and the predictions were compared with the clinical data to assess their accuracy. Although there were significant differences in the fast fracture strength among the groups of materials or durations of chemical challenge, fatigue testing was much better at separating the groups. Linear relationships were found between the laboratory and clinical times to failure for the first material (R2 = 0.90, 0.90, and 0.62 for the 0-, 24-, and 48-h low-pH groups, respectively). The clinical life of restorations made of the other 2 materials was best predicted with data from the 48-h low-pH groups. In conclusion, an accelerated fatigue model was successfully calibrated and applied to predict the clinical failure of resin composite restorations, and the predictions based on data obtained from chemically aged specimens provided the best agreement with clinical data.

Keywords: accelerated fatigue; failure; predict clinical; model predicting; fatigue model

Journal Title: Journal of Dental Research
Year Published: 2022

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