In-vitro dental implant primary stability : staircase predicting continuous method

Introduction: Primary Stability (PS) is critical for immediate loading protocols of dental implants. Fuchs et. al developed a Continuous Micromotion (CM) to measure PS invitro. The number of cycles (n) applied significantly influences the duration of tests. Longer time often translates to higher costs for laboratories and companies. This study presents a faster method, the Staircase Method (SM), and evaluates its reliability in predicting the PS as tested in the CM method. This would contribute to significant reduction of costs and therefore increase the availability and usage of such tests. Material and Methods: In this study, the SM was primarily developed to allow testing of implant design in Sawbone bone surrogate for a reduced n (e.g., 3’000) and at increasing load level (LL), using a stepwise approach, until reaching implant failure (IF) (Figure 1). Compared to SM, CM has a higher number of cycles (e.g., 50’000) but applied continuously at one chosen level (e.g., 100 N). Both methods evaluate two components: Migration (Mig) and Micromotion (MM) of the implant with lateral and axial movements monitored by two sensors of compressivebending loading, during 50’000 and 3’000 cycles (n), respectively in CM and SM. MM and Mig correspond to representative relative motion between implant and surrounding material, respectively due to elastic deformation and plastic deformation of the surrogate bone. To predict CM using the faster SM values, a statistical prediction model was developed. To check the reliability of this model, out-of-sample test was performed with 4 different Nobel Biocare implant system families with different platforms and lengths (22 in total from NobelParallel, NobelReplace, Nobel Speedy, N1), tested in Sawbone of 20 pounds per cubic foot (PCF) density, and a sample size of 4. Out of the 22 implants tested in SM, 4 were tested with CM in 20 PCF (Type III bone) at 100N, and additional 4 at 140 N. Model robustness was assessed using a 2-sample T-test to compare ranking PS performances of different implant groups using the R Sofware. Results: The CM prediction model could predict the probability of an implant to fail at a certain LL with overall 87.5% accuracy. When assessing the model robustness, similarly high accuracy in terms of CM ranking prediction was achieved: 100% for groups tested at 100N, 75% for groups tested at 140 N, for confidence level (CL) of 80% (Figure 2). Similarly to what arises within traditional CM testing, lower values of accuracy occur when the ranking of implants with similar performance is predicted. Thus, as presented in Figure 2, two implants tested at 140 N showed different predicted ranking (2* and 3*) compared to real data (3** and 2**). However, the prediction also showed that the endpoints of the PS curves were statistically not different (at a CL of 80%) and the same was found when comparing tested PS, proving similar performance. Discussion: This study provides evidence that SM is a reliable and robust in-vitro test that may be used as a method to predict PS faster compared with the CM method. However, how to accurately differentiate the predicted ranking of implants with similar performance remains a challenge. In additon, the accuracy ratios of the prediction might decrease when increasing the CL, or when increasing the number of compared implant groups. Future studies should further investigate the use of SM to compare implant systems.