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A rapid, non-invasive tool for periodontitis screening in a medical care setting

BACKGROUND: Since periodontitis is bi-directionally associated with several systemic diseases, such as diabetes mellitus and cardiovascular diseases, it is important for medical professionals in a non-dental setting to be able examine their patients for symptoms of periodontitis, and urge them to vi...

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Autores principales: Verhulst, Martijn J. L., Teeuw, Wijnand J., Bizzarro, Sergio, Muris, Joris, Su, Naichuan, Nicu, Elena A., Nazmi, Kamran, Bikker, Floris J., Loos, Bruno G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6533660/
https://www.ncbi.nlm.nih.gov/pubmed/31122214
http://dx.doi.org/10.1186/s12903-019-0784-7
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author Verhulst, Martijn J. L.
Teeuw, Wijnand J.
Bizzarro, Sergio
Muris, Joris
Su, Naichuan
Nicu, Elena A.
Nazmi, Kamran
Bikker, Floris J.
Loos, Bruno G.
author_facet Verhulst, Martijn J. L.
Teeuw, Wijnand J.
Bizzarro, Sergio
Muris, Joris
Su, Naichuan
Nicu, Elena A.
Nazmi, Kamran
Bikker, Floris J.
Loos, Bruno G.
author_sort Verhulst, Martijn J. L.
collection PubMed
description BACKGROUND: Since periodontitis is bi-directionally associated with several systemic diseases, such as diabetes mellitus and cardiovascular diseases, it is important for medical professionals in a non-dental setting to be able examine their patients for symptoms of periodontitis, and urge them to visit a dentist if necessary. However, they often lack the time, knowledge and resources to do so. We aim to develop and assess “quick and easy” screening tools for periodontitis, based on self-reported oral health (SROH), demographics and/or salivary biomarkers, intended for use by medical professionals in a non-dental setting. METHODS: Consecutive, new patients from our outpatient clinic were recruited. A SROH questionnaire (8 questions) was conducted, followed by a 30 s oral rinse sampling protocol. A complete clinical periodontal examination provided the golden standard periodontitis classification: no/mild, moderate or severe periodontitis. Total periodontitis was defined as having either moderate or severe. Albumin and matrix metalloproteinase-8 concentrations, and chitinase and protease activities were measured in the oral rinses. Binary logistic regression analyses with backward elimination were used to create prediction models for both total and severe periodontitis. Model 1 included SROH, demographics and biomarkers. The biomarkers were omitted in the analysis for model 2, while model 3 only included the SROH questionnaire. The area under the receiver operating characteristic curves (AUROCC) provided the accuracy of each model. The regression equations were used to create scoring algorithms, composed of the remaining predictors, each with its own weight. RESULTS: Of the 156 patients participating in this study, 67% were classified with total periodontitis and 33% had severe periodontitis. The models for total periodontitis achieved an AUROCC of 0.91 for model 1, 0.88 for model 2 and 0.81 for model 3. For severe periodontitis, this was 0.89 for model 1, 0.82 for model 2 and 0.78 for model 3. The algorithm for total periodontitis (model 2), which we consider valid for the Dutch population, was applied to create a freely accessible, web-based screening tool. CONCLUSIONS: The prediction models for total and severe periodontitis proved to be feasible and accurate, resulting in easily applicable screening tools, intended for a non-dental setting. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12903-019-0784-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-65336602019-05-29 A rapid, non-invasive tool for periodontitis screening in a medical care setting Verhulst, Martijn J. L. Teeuw, Wijnand J. Bizzarro, Sergio Muris, Joris Su, Naichuan Nicu, Elena A. Nazmi, Kamran Bikker, Floris J. Loos, Bruno G. BMC Oral Health Research Article BACKGROUND: Since periodontitis is bi-directionally associated with several systemic diseases, such as diabetes mellitus and cardiovascular diseases, it is important for medical professionals in a non-dental setting to be able examine their patients for symptoms of periodontitis, and urge them to visit a dentist if necessary. However, they often lack the time, knowledge and resources to do so. We aim to develop and assess “quick and easy” screening tools for periodontitis, based on self-reported oral health (SROH), demographics and/or salivary biomarkers, intended for use by medical professionals in a non-dental setting. METHODS: Consecutive, new patients from our outpatient clinic were recruited. A SROH questionnaire (8 questions) was conducted, followed by a 30 s oral rinse sampling protocol. A complete clinical periodontal examination provided the golden standard periodontitis classification: no/mild, moderate or severe periodontitis. Total periodontitis was defined as having either moderate or severe. Albumin and matrix metalloproteinase-8 concentrations, and chitinase and protease activities were measured in the oral rinses. Binary logistic regression analyses with backward elimination were used to create prediction models for both total and severe periodontitis. Model 1 included SROH, demographics and biomarkers. The biomarkers were omitted in the analysis for model 2, while model 3 only included the SROH questionnaire. The area under the receiver operating characteristic curves (AUROCC) provided the accuracy of each model. The regression equations were used to create scoring algorithms, composed of the remaining predictors, each with its own weight. RESULTS: Of the 156 patients participating in this study, 67% were classified with total periodontitis and 33% had severe periodontitis. The models for total periodontitis achieved an AUROCC of 0.91 for model 1, 0.88 for model 2 and 0.81 for model 3. For severe periodontitis, this was 0.89 for model 1, 0.82 for model 2 and 0.78 for model 3. The algorithm for total periodontitis (model 2), which we consider valid for the Dutch population, was applied to create a freely accessible, web-based screening tool. CONCLUSIONS: The prediction models for total and severe periodontitis proved to be feasible and accurate, resulting in easily applicable screening tools, intended for a non-dental setting. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12903-019-0784-7) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-23 /pmc/articles/PMC6533660/ /pubmed/31122214 http://dx.doi.org/10.1186/s12903-019-0784-7 Text en © The Author(s). 2019 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
Verhulst, Martijn J. L.
Teeuw, Wijnand J.
Bizzarro, Sergio
Muris, Joris
Su, Naichuan
Nicu, Elena A.
Nazmi, Kamran
Bikker, Floris J.
Loos, Bruno G.
A rapid, non-invasive tool for periodontitis screening in a medical care setting
title A rapid, non-invasive tool for periodontitis screening in a medical care setting
title_full A rapid, non-invasive tool for periodontitis screening in a medical care setting
title_fullStr A rapid, non-invasive tool for periodontitis screening in a medical care setting
title_full_unstemmed A rapid, non-invasive tool for periodontitis screening in a medical care setting
title_short A rapid, non-invasive tool for periodontitis screening in a medical care setting
title_sort rapid, non-invasive tool for periodontitis screening in a medical care setting
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6533660/
https://www.ncbi.nlm.nih.gov/pubmed/31122214
http://dx.doi.org/10.1186/s12903-019-0784-7
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