Univ. Lille, CHU Lille, Department of oral and maxillofacial surgery, URL 2694 - METRICS, Lille, F-59000, France.
Univ. Lille, CHU Lille, INSERM, Department of oral and maxillofacial surgery, U1008 - Advanced Drug Delivery Systems, Lille, F-59000, France.
BMC Oral Health. 2024 Sep 4;24(1):1039. doi: 10.1186/s12903-024-04827-0.
Oligodontia is a rare dental developmental pathology that requires prolonged, complex and multidisciplinary treatment. Although bone augmentation is frequently required during a complete implant treatment of oligodontia. Therefore, we evaluated the ability to predict pre-implant surgery complexity based on age, number of missing teeth, and number of implants required to achieve implant-supported prosthetic rehabilitation.
This retrospectively registered study included all patients who underwent surgical treatment for oligodontia in our Oral and Maxillofacial Surgery Department between January 2012 and May 2023. Demographic data, number and location of missing teeth, pre- and per-implant surgical procedures, and the number of planned implants were recorded. A quantitative variable called "complexity score of pre-implant surgery" was created. This 10-point score was calculated by adding one point for each preimplant surgical procedure registered. A simple linear regression was calculated to explain the number of targeted implants based on number of missing teeth. A multiple linear regression model was used to explain the complexity score of pre-implant surgery and age, number of missing teeth and number of targeted implants.
119 oligodontia patients were included in the study. The median number of tooth agenesis was 10. A total of 825 implants were placed, 14 (1.7%) of which failed. A significant regression equation was used (F(1,118) = 1098,338; p < 0.0001) to explain the number of targeted implants based on number of missing teeth, with a R of 0.903. A significant regression equation was found (F(3,116) = 107,229; p < 0.0001) to explain the complexity score of pre-implant surgery and age, number of missing teeth and number of targeted implants, with a R of 0.735.
These results based on patient data indicate that age, number of missing teeth and number of targeted implants could reliably explain the complexity of pre-implant surgery.
少牙症是一种罕见的牙齿发育病理，需要长期、复杂和多学科的治疗。尽管在完全植入治疗少牙症时经常需要进行骨增强。因此，我们评估了基于年龄、缺失牙数量和实现种植体支持修复所需的种植体数量来预测植入前手术复杂性的能力。
本回顾性注册研究包括 2012 年 1 月至 2023 年 5 月期间在我院口腔颌面外科接受手术治疗的所有少牙症患者。记录人口统计学数据、缺失牙的数量和位置、植入前和植入后手术程序以及计划植入物的数量。创建了一个称为“植入前手术复杂性评分”的定量变量。通过为每个登记的植入前手术程序加一分来计算这个 10 分制评分。使用简单线性回归解释基于缺失牙数量的目标植入物数量。使用多元线性回归模型解释植入前手术的复杂性评分以及年龄、缺失牙数量和目标植入物数量。
本研究纳入 119 例少牙症患者。牙缺失中位数为 10。共放置 825 个种植体，其中 14 个（1.7%）失败。使用了一个显著的回归方程（F(1,118) = 1098,338;p < 0.0001）来解释基于缺失牙数量的目标植入物数量，R 为 0.903。发现了一个显著的回归方程（F(3,116) = 107,229;p < 0.0001）来解释植入前手术和年龄、缺失牙数量和目标植入物数量的复杂性评分，R 为 0.735。
这些基于患者数据的结果表明，年龄、缺失牙数量和目标植入物数量可以可靠地解释植入前手术的复杂性。
