Department of Dentomaxillofacial Radiology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China.
Department of Periodontology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China.
Aust Endod J. 2023 Aug;49(2):302-310. doi: 10.1111/aej.12667. Epub 2022 Jul 21.
This study aimed to develop a predictive model to screen for undetected vertical root fractures (VRFs) in root canal treated teeth. We included 95 root canal treated teeth with suspected VRFs; 77 for training and 18 for validation. Following clinical and cone-beam CT parameters were recorded: sex, tooth type, coronal restoration, time interval from completion of endodontic treatment to definitive diagnosis (TI), type of bone loss (BL), apical extent of root filling (AR) and the ratio of root filling diameter to the actual diameter in the coronal (1/3TA) and middle (2/3TA) root thirds. A predictive model p = 1/(1 - e ) was generated, where x = -7.433 + 1.977BL + 1.479 (2/3TA) + 1.102 AR; the sensitivity and specificity were 0.852 and 0.875 for training and 0.917 and 0.833 for validation. VRF teeth were more likely to have vertical bone loss and overfilled root canals. This model had a high diagnostic efficacy for VRFs.
本研究旨在开发一种预测模型，以筛查根管治疗牙中未检测到的垂直根折（VRF）。我们纳入了 95 颗疑似有 VRF 的根管治疗牙；其中 77 颗用于训练，18 颗用于验证。记录了以下临床和锥形束 CT 参数：性别、牙位类型、冠部修复、从根管治疗完成到明确诊断的时间间隔（TI）、骨丢失类型（BL）、根尖根充的范围（AR）和根充直径与根尖（1/3TA）和中 1/3（2/3TA）的实际直径之比。生成了一个预测模型 p = 1 / (1 - e)，其中 x = -7.433 + 1.977BL + 1.479 (2/3TA) + 1.102AR；在训练和验证中，该模型的灵敏度和特异性分别为 0.852 和 0.875，以及 0.917 和 0.833。有 VRF 的牙齿更有可能有垂直的骨丢失和过度填充的根管。该模型对 VRF 具有较高的诊断效能。
