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PLoS One. 2017 Oct 30;12(10):e0187010. doi: 10.1371/journal.pone.0187010. eCollection 2017.

Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability quotient values.

Author information

1
Department of Oral Implantology and Prosthetic Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), Universiteit van Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, Nord-Holland, the Netherlands.
2
Department of Prosthodontic Dentistry, Johann Wolfgang Goethe University, Frankfurt, Hessen, Germany.
3
Best & Easy Dental Clinic, Hangzhou, Zhejiang Province, P.R. China.
4
The Affiliated Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, P.R. China.
5
The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang Province, P.R. China.

Abstract

OBJECTIVES:

This study identified potential general influencing factors for a mathematical prediction of implant stability quotient (ISQ) values in clinical practice.

METHODS:

We collected the ISQ values of 557 implants from 2 different brands (SICace and Osstem) placed by 2 surgeons in 336 patients. Surgeon 1 placed 329 SICace implants, and surgeon 2 placed 113 SICace implants and 115 Osstem implants. ISQ measurements were taken at T1 (immediately after implant placement) and T2 (before dental restoration). A multivariate linear regression model was used to analyze the influence of the following 11 candidate factors for stability prediction: sex, age, maxillary/mandibular location, bone type, immediate/delayed implantation, bone grafting, insertion torque, I-stage or II-stage healing pattern, implant diameter, implant length and T1-T2 time interval.

RESULTS:

The need for bone grafting as a predictor significantly influenced ISQ values in all three groups at T1 (weight coefficients ranging from -4 to -5). In contrast, implant diameter consistently influenced the ISQ values in all three groups at T2 (weight coefficients ranging from 3.4 to 4.2). Other factors, such as sex, age, I/II-stage implantation and bone type, did not significantly influence ISQ values at T2, and implant length did not significantly influence ISQ values at T1 or T2.

CONCLUSIONS:

These findings provide a rational basis for mathematical models to quantitatively predict the ISQ values of implants in clinical practice.

PMID:
29084260
PMCID:
PMC5662232
DOI:
10.1371/journal.pone.0187010
[Indexed for MEDLINE]
Free PMC Article

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