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Risk factors of chronic periodontitis on healing response: a multilevel modelling analysis

BACKGROUND: Chronic periodontitis is a multifactorial polygenetic disease with an increasing number of associated factors that have been identified over recent decades. Longitudinal epidemiologic studies have demonstrated that the risk factors were related to the progression of the disease. A tradit...

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Autores principales: Song, J., Zhao, H., Pan, C., Li, C., Liu, J., Pan, Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5603071/
https://www.ncbi.nlm.nih.gov/pubmed/28915872
http://dx.doi.org/10.1186/s12911-017-0533-2
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author Song, J.
Zhao, H.
Pan, C.
Li, C.
Liu, J.
Pan, Y.
author_facet Song, J.
Zhao, H.
Pan, C.
Li, C.
Liu, J.
Pan, Y.
author_sort Song, J.
collection PubMed
description BACKGROUND: Chronic periodontitis is a multifactorial polygenetic disease with an increasing number of associated factors that have been identified over recent decades. Longitudinal epidemiologic studies have demonstrated that the risk factors were related to the progression of the disease. A traditional multivariate regression model was used to find risk factors associated with chronic periodontitis. However, the approach requirement of standard statistical procedures demands individual independence. Multilevel modelling (MLM) data analysis has widely been used in recent years, regarding thorough hierarchical structuring of the data, decomposing the error terms into different levels, and providing a new analytic method and framework for solving this problem. The purpose of our study is to investigate the relationship of clinical periodontal index and the risk factors in chronic periodontitis through MLM analysis and to identify high-risk individuals in the clinical setting. METHODS: Fifty-four patients with moderate to severe periodontitis were included. They were treated by means of non-surgical periodontal therapy, and then made follow-up visits regularly at 3, 6, and 12 months after therapy. Each patient answered a questionnaire survey and underwent measurement of clinical periodontal parameters. RESULTS: Compared with baseline, probing depth (PD) and clinical attachment loss (CAL) improved significantly after non-surgical periodontal therapy with regular follow-up visits at 3, 6, and 12 months after therapy. The null model and variance component models with no independent variables included were initially obtained to investigate the variance of the PD and CAL reductions across all three levels, and they showed a statistically significant difference (P < 0.001), thus establishing that MLM data analysis was necessary. Site-level had effects on PD and CAL reduction; those variables could explain 77–78% of PD reduction and 70–80% of CAL reduction at 3, 6, and 12 months. Other levels only explain 20–30% of PD and CAL reductions. Site-level had the greatest effect on PD and CAL reduction. CONCLUSIONS: Non-surgical periodontal therapy with regular follow-up visits had a remarkable curative effect. All three levels had a substantial influence on the reduction of PD and CAL. Site-level had the largest effect on PD and CAL reductions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-017-0533-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-56030712017-09-20 Risk factors of chronic periodontitis on healing response: a multilevel modelling analysis Song, J. Zhao, H. Pan, C. Li, C. Liu, J. Pan, Y. BMC Med Inform Decis Mak Research Article BACKGROUND: Chronic periodontitis is a multifactorial polygenetic disease with an increasing number of associated factors that have been identified over recent decades. Longitudinal epidemiologic studies have demonstrated that the risk factors were related to the progression of the disease. A traditional multivariate regression model was used to find risk factors associated with chronic periodontitis. However, the approach requirement of standard statistical procedures demands individual independence. Multilevel modelling (MLM) data analysis has widely been used in recent years, regarding thorough hierarchical structuring of the data, decomposing the error terms into different levels, and providing a new analytic method and framework for solving this problem. The purpose of our study is to investigate the relationship of clinical periodontal index and the risk factors in chronic periodontitis through MLM analysis and to identify high-risk individuals in the clinical setting. METHODS: Fifty-four patients with moderate to severe periodontitis were included. They were treated by means of non-surgical periodontal therapy, and then made follow-up visits regularly at 3, 6, and 12 months after therapy. Each patient answered a questionnaire survey and underwent measurement of clinical periodontal parameters. RESULTS: Compared with baseline, probing depth (PD) and clinical attachment loss (CAL) improved significantly after non-surgical periodontal therapy with regular follow-up visits at 3, 6, and 12 months after therapy. The null model and variance component models with no independent variables included were initially obtained to investigate the variance of the PD and CAL reductions across all three levels, and they showed a statistically significant difference (P < 0.001), thus establishing that MLM data analysis was necessary. Site-level had effects on PD and CAL reduction; those variables could explain 77–78% of PD reduction and 70–80% of CAL reduction at 3, 6, and 12 months. Other levels only explain 20–30% of PD and CAL reductions. Site-level had the greatest effect on PD and CAL reduction. CONCLUSIONS: Non-surgical periodontal therapy with regular follow-up visits had a remarkable curative effect. All three levels had a substantial influence on the reduction of PD and CAL. Site-level had the largest effect on PD and CAL reductions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-017-0533-2) contains supplementary material, which is available to authorized users. BioMed Central 2017-09-15 /pmc/articles/PMC5603071/ /pubmed/28915872 http://dx.doi.org/10.1186/s12911-017-0533-2 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Song, J.
Zhao, H.
Pan, C.
Li, C.
Liu, J.
Pan, Y.
Risk factors of chronic periodontitis on healing response: a multilevel modelling analysis
title Risk factors of chronic periodontitis on healing response: a multilevel modelling analysis
title_full Risk factors of chronic periodontitis on healing response: a multilevel modelling analysis
title_fullStr Risk factors of chronic periodontitis on healing response: a multilevel modelling analysis
title_full_unstemmed Risk factors of chronic periodontitis on healing response: a multilevel modelling analysis
title_short Risk factors of chronic periodontitis on healing response: a multilevel modelling analysis
title_sort risk factors of chronic periodontitis on healing response: a multilevel modelling analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5603071/
https://www.ncbi.nlm.nih.gov/pubmed/28915872
http://dx.doi.org/10.1186/s12911-017-0533-2
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