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Type: Journal article
Title: Prediction models for the incidence and progression of periodontitis: a systematic review
Author: Du, M.
Bo, T.
Kapellas, K.
Peres, M.
Citation: Journal of Clinical Periodontology, 2018; 45(12):1408-1420
Publisher: Wiley
Issue Date: 2018
ISSN: 0303-6979
Statement of
Mi Du, Tao Bo, Kostas Kapellas, Marco A Peres
Abstract: AIMS:To comprehensively review, identify and critically assess the performance of models predicting the incidence and progression of periodontitis. METHODS:Electronic searches of the MEDLINE via PubMed, EMBASE, DOSS, Web of Science, Scopus and ProQuest databases, and hand searching of reference lists and citations were conducted. No date or language restrictions were used. The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist was followed when extracting data and appraising the selected studies. RESULTS:Of the 2,560 records, five studies with 12 prediction models and three risk assessment studies were included. The prediction models showed great heterogeneity precluding meta-analysis. Eight criteria were identified for periodontitis incidence and progression. Four models from one study examined the incidence, while others assessed progression. Age, smoking and diabetes status were common predictors used in modeling. Only two studies reported external validation. Predictive performance of the models (discrimination and calibration) was unable to be fully assessed or compared quantitatively. Nevertheless, most models had 'good' ability to discriminate between people at risk for periodontitis. CONCLUSIONS:Existing predictive modelling approaches were identified. However, no studies followed the recommended methodology, and almost all models were characterized by a generally poor level of reporting. This article is protected by copyright. All rights reserved.
Keywords: periodontitis; prediction; risk factors; systematic review
Rights: © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
RMID: 0030101967
DOI: 10.1111/jcpe.13037
Grant ID:
Appears in Collections:Dentistry publications

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