Risk assessment of dental caries by using Classification and Regression Trees

Objectives able to predict an individualʹs risks of dental caries would offer a potentially huge natural step forward toward better oral heath. As things stand, preventive treatment against caries is mostly carried out without risk assessment because there is no proven way to analyse an individualʹs risk factors. The purpose of this study was to try to identify those patients with high and low risk of caries by using Classification and Regression Trees (CART). s s historical cohort study, data from 442 patients in a general practice who met the inclusion criteria were analysed. CART was applied to the data to seek a model for predicting caries by using the following parameters according to each patient: age, number of carious teeth, numbers of cariogenic bacteria, the secretion rate and buffer capacity of saliva, and compliance with a prevention programme. The risks of caries were presented by odds ratios. Multiple logistic regression analysis was performed to confirm the results obtained by CART. s dentified high and low risk patients for primary caries with relative odds ratios of 0.41 (95%CI: 0.22–0.77, p = 0.0055) and 2.88 (95%CI: 1.49–5.59, p = 0.0018) according the numbers of cariogenic bacteria. High and low risk patients for secondary caries were also identified with the odds ratios of 0.07 (95%CI: 0.01–0.55, p = 0.00109) and 7.00 (95%CI: 3.50–13.98, p < 0.0001) according the numbers of bacteria and existing caries. sions enic bacteria play a leading role in the incidence of caries. CART proved effective in identifying an individual patientʹs risk of caries.