Identification predictors of risk of dental implant rejection in the early postoperative period
- Authors: Lakman I.A.1, Dolgalev A.A.2,3, Stomatov D.V.4, Zolotaev K.E.2, Semerikov D.Y.5, Avanisyan V.M.2, Atapin P.M.6, Usmanova I.N.7, Gurenko S.A.6
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Affiliations:
- Ufa University of Science and Technology
- Stavropol State Medical University
- LLC Implant Additive Technologies
- Penza State University
- “Valentina” Dental Clinic LLC
- CIBERDOCTOR LLC
- Bashkir State Medical University
- Issue: Vol 29, No 3 (2023)
- Pages: 163-174
- Section: Original Research Articles
- URL: https://journals.rcsi.science/0869-2106/article/view/131851
- DOI: https://doi.org/10.17816/medjrf321458
- ID: 131851
Cite item
Abstract
BACKGROUND: The modern model of healthcare requires a paradigm shift in the thinking of healthcare managers, doctors, and patients. A personalized approach, identification of possible causes of diseases, and prevention of pathologies are the components of successful and quality healthcare delivery in our country. Dentistry is a field in which preventive, prophylactic, and personalized medicine is an integral part of patient care. One of the important tasks of modern digital dentistry is to find indicators that allow for predicting dental implant complications. The solution to this problem could be the creation of a medical decision support system that allows predicting outcomes before implant surgery.
AIMS: To reliably identify predictors of early (up to 6 months) risk of dental implant rejection by applying hierarchical Bayesian survival analysis models.
METHODS: Data collected retrospectively for patients who underwent dental implant placement between 2013 and 2022 were considered information bases. Data were generated from multicenter surveys conducted in dental implant centers in Stavropol, Moscow, and Penza. The total number of observed cases was 1472. A group of defined factors was considered candidate risk predictors, and the Bayesian hierarchical Cox model (Gsslasso Cox) was used to identify risk predictors.
RESULTS: After retrospective analysis of the collected data and screening out incomplete and poor-quality information, the database included a total of 39 variables (factors) for 1472 observations (implants). The multivariate analysis yielded the following predictors of risk of early dental implant rejection: male sex (hazard ratio [HR] 2.388, 95% confidence interval [CI] 1.345; 4.240, p=0.003), age at implantation (years; HR1.034, 95% CI 1.008–1.041, p=0.011), oral hygiene (Silnes–Low index; HR 2.439, 95% CI 1.205–4.701, p=0.051), osteoporosis (HR 5.512, 95% CI 3.684–8.248, p <0.001), bone width (mm; HR 0.823, 95% CI 0.716–0.944, p=0.006), anesthetic type (local; HR 0.469, 95% CI 0.234–0.944, p=0.034), localized periodontitis (HR 2.024, 95% CI 1.452–2.821, p=0.039), and low-festooned, thick gingiva (HR=0.485; 95% CI: 0.358–-0.658; p=0.0104).
CONCLUSIONS: This study shows that predictors of risk of dental implant rejection can be identified separately in the early postoperative period (up to 6 months) by using hierarchical Bayesian survival analysis models, and risk predictors different from those in the longer term are identified in this period.
Full Text
##article.viewOnOriginalSite##About the authors
Irina A. Lakman
Ufa University of Science and Technology
Author for correspondence.
Email: Lackmania@mail.ru
ORCID iD: 0000-0001-9876-9202
SPIN-code: 4521-9097
Cand. Sci. (Tech.), associate professor
Russian Federation, UfaAlexander A. Dolgalev
Stavropol State Medical University; LLC Implant Additive Technologies
Email: dolgalev@dolgalev.pro
ORCID iD: 0000-0002-6352-6750
SPIN-code: 5941-5771
MD, Dr. Sci. (Med.), professor
Russian Federation, Stavropol; StavropolDmitry V. Stomatov
Penza State University
Email: grekstom@mail.ru
ORCID iD: 0000-0002-3271-971X
MD, Cand. Sci. (Med.), associate professor
Russian Federation, PenzaKirill E. Zolotaev
Stavropol State Medical University
Email: kzolotaev@yandex.ru
ORCID iD: 0000-0003-2347-5378
graduate student
Russian Federation, StavropolDmitry Yu. Semerikov
“Valentina” Dental Clinic LLC
Email: sim2457@gmail.com
ORCID iD: 0000-0001-8843-4580
Russian Federation, Nyagan
Vazgen M. Avanisyan
Stavropol State Medical University
Email: avanvaz@yandex.ru
ORCID iD: 0000-0002-0316-5957
SPIN-code: 1207-9234
MD
Russian Federation, StavropolPavel M. Atapin
CIBERDOCTOR LLC
Email: pav1004@mail.ru
Russian Federation, Stavropol
Irina N. Usmanova
Bashkir State Medical University
Email: irinausma@mail.ru
ORCID iD: 0000-0002-1781-0291
SPIN-code: 1978-9470
MD, Dr. Sci. (Med.), professor
Russian Federation, UfaSergey A. Gurenko
CIBERDOCTOR LLC
Email: gsstav28@gmail.com
Russian Federation, Stavropol
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