Prediction models for the incidence and progression of periodontitis: A systematic review

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 modelling. 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.

Predictors for tooth loss in periodontitis patients: Systematic review and meta‐analysis

“…Predicting periodontitis onset, progression and the tooth loss resulting from periodontitis is relevant for dentists during treatment planning, but also risk communication with patients. Prediction models for periodontitis incidence and progression have been systematically evaluated recently (Du, Bo, Kapellas, & Peres, ). No such evaluation for predictors of tooth loss is available, and while a range of tooth loss prediction systems have been established, most have not at all or only limitedly been externally validated (Schwendicke et al., ).…”

Individual predisposition and the intricate interplay between systemic biomarkers and periodontal risk in a general population

“…37 A compilation of risk predictors as applied in models for periodontitis incidence was published. 38 Such individual characteristics of disease predisposition are a matter of life-course epidemiology seeking to understand the development of chronic diseases. Complex interactions of genetic and environmental factors destine during the whole life span susceptibility and resilience toward systemic diseases, periodontitis, and tooth loss.…”

Proteome Analysis of Molecular Events in Oral Pathogenesis and Virus: A Review with a Particular Focus on Periodontitis

“…Worldwide research on periodontal and systemic diseases has resulted in a number of reports on the effects of general condition on periodontal tissues [ 12 , 13 , 14 ]. Both diabetes mellitus and periodontal disease are representative lifestyle habit illnesses and diet and smoking are associated lifestyle habit factors, with diabetes mellitus along with smoking said to be two big risk factors for periodontal disease [ 15 , 16 , 17 , 18 ]. Similarly, there are rare systemic disorders that are associated with severe periodontal disease, such as Papillon–Lefèvre syndrome [ 19 ].…”