OBJECTIVES: To assess the predictability of implant stability assessment either clinically or by resonance frequency analysis (RFA). MATERIAL AND METHODS: This prospective case series study evaluated 4114 consecutive SLA Straumann implants in two private clinics. Primary stability was classified in four categories, depending on the degree of implant rotation when tightening the healing cap: A (no rotation at all), B (light rotation with a feeling of resistance), C (rotation without resistance) and D (rotation and lateral oscillation). In one clinic (n=542 implants), RFA method was also used the day of the surgery (Osstell 1) and at restoration placement (Osstell 2). Survival rates were stratified according to the clinical classification categories using life table analysis. The association between Osstell 1 and 2 and the clinical classification was tested with ANOVA. RESULTS: 3899 implants were classified as stable (A) and 213 as unstable (B-D). Their survival rates were 99.1% and 97.2%, respectively. The unstable implants were further classified in B (158), C (51) and D (4), with survivals of 98.1%, 94.1% and 100%, respectively, being these differences statistically significant (P<0.009). Using Osstell, implants were stratified in two groups according to a predefined threshold of implant stability quotient (>or=60). At the Osstell 1 measurement there was no significant association between primary stability and implant survival (P<0.753). In Osstell 2, however, the association was significant (P<0.001). CONCLUSIONS: Only secondary stability RFA values were able to significantly predict implant outcomes, but not primary stability values. There was a good correlation between RFA and the proposed clinical classification of primary stability.
OBJECTIVES: To assess the predictability of implant stability assessment either clinically or by resonance frequency analysis (RFA). MATERIAL AND METHODS: This prospective case series study evaluated 4114 consecutive SLA Straumann implants in two private clinics. Primary stability was classified in four categories, depending on the degree of implant rotation when tightening the healing cap: A (no rotation at all), B (light rotation with a feeling of resistance), C (rotation without resistance) and D (rotation and lateral oscillation). In one clinic (n=542 implants), RFA method was also used the day of the surgery (Osstell 1) and at restoration placement (Osstell 2). Survival rates were stratified according to the clinical classification categories using life table analysis. The association between Osstell 1 and 2 and the clinical classification was tested with ANOVA. RESULTS: 3899 implants were classified as stable (A) and 213 as unstable (B-D). Their survival rates were 99.1% and 97.2%, respectively. The unstable implants were further classified in B (158), C (51) and D (4), with survivals of 98.1%, 94.1% and 100%, respectively, being these differences statistically significant (P<0.009). Using Osstell, implants were stratified in two groups according to a predefined threshold of implant stability quotient (>or=60). At the Osstell 1 measurement there was no significant association between primary stability and implant survival (P<0.753). In Osstell 2, however, the association was significant (P<0.001). CONCLUSIONS: Only secondary stability RFA values were able to significantly predict implant outcomes, but not primary stability values. There was a good correlation between RFA and the proposed clinical classification of primary stability.
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