Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University; Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.
Minnesota Dental Research Center for Biomaterials and Biomechanics, School of Dentistry, University of Minnesota, Minneapolis, MN, USA.
J Dent Res. 2022 Dec;101(13):1606-1612. doi: 10.1177/00220345221126928. Epub 2022 Oct 5.
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 ( = 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 ( = 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.
提出了一种经验方法，通过使用源自经受化学降解和加速循环疲劳的简单样本的实验室数据来预测树脂复合牙科修复体的临床性能。使用三种树脂复合材料填充来自牛门牙根的牙本质圆盘（内径 2mm，外径 5mm，厚 2mm）。将样本（每组 30 个）在 pH4.5 下经历不同时间的低 pH 挑战（0、24 和 48 小时）老化后，用单调增加的载荷（快速断裂）或连续增加幅度的循环载荷（加速疲劳）进行直径压缩。通过各自的生存概率曲线，使用一种材料的数据来建立实验室时间（循环次数）与临床失效时间（年）之间的关系。然后，使用该时间关系来预测由其他两种材料制成的修复体的临床失效率，并将预测结果与临床数据进行比较以评估其准确性。尽管在材料组或化学挑战持续时间的快速断裂强度之间存在显著差异，但疲劳测试在分离组方面要好得多。对于第一种材料，在实验室和临床失效时间之间发现了线性关系（0-、24-和 48-h 低 pH 组的 = 0.90、0.90 和 0.62）。对于其他两种材料制成的修复体，临床寿命可以通过来自 48-h 低 pH 组的数据来最佳预测。总之，成功校准了加速疲劳模型并将其应用于预测树脂复合材料修复体的临床失效，并且基于化学老化样本获得的数据的预测与临床数据最吻合。
