SAC Assessment Tool in Implant Dentistry: Evaluation of the Agreement Level Between Users.

Abstract

The SAC Assessment Tool is a clinical decision support system based on the foundations of the SAC Classification System in Implant Dentistry developed by the International Team for Implantology in 2009. It objectively classifies a patient's rehabilitation with dental implants as straightforward, advanced, or complex, from both a surgical and restorative perspective. The aim of this research was to test the agreement between observers with different qualification levels and clinical experience when using this clinical decision support system as a method that mitigates risk. A total of 30 patients were randomly selected from clinical records, and diagnostic casts, intraoral and extraoral images, and panoramic radiographs were obtained. All data were analyzed with and without the SAC Assessment Tool by a dentist with advanced training and clinical experience in implant dentistry (control dentist) and compared with three colleagues (dentists 1, 2, and 3) with fewer qualifications and less clinical experience. All data were analyzed using statistical agreement tests (Fless kappa), interclass correlation, and agreement rate. The level of significance (α) was set at .05. All patients included in this research presented 104 edentulous areas, which were subjected to surgical evaluation for possible placement of dental implants. Concerning the degree of risk evaluation for dental implant treatment, the results of this study found that the agreement rate of the control dentist without SAC and control dentist with SAC was excellent (81.7%); the agreement rate of the control dentist and dentists 1, 2, and 3 with the use of SAC was satisfactory (67.3% to 76.0%); the variable that presented a lower agreement rate (34.6%) was the comparison between dentists 1, 2, and 3 without use of the SAC Assessment Tool. The SAC classification seems to be a useful tool to assist dentists with less experience in implant dentistry with defining the complexity of the treatment and hence with patient selection. It helps in the collection and homogenization of important clinical data to assess the risk of implant-based rehabilitations, thus contributing to an increase in the agreement rate.