TY - JOUR

T1 - How combining different caries lesions characteristics may be helpful in short-term caries progression prediction

T2 - model development on occlusal surfaces of primary teeth

AU - Floriano, Isabela

AU - Souza Rocha, Elizabeth

AU - Matos, Ronilza

AU - Mattos-Silveira, Juliana

AU - Ekstrand, Kim Rud

AU - Mendes, Fausto Medeiros

AU - Braga, Mariana Minatel

N1 - Publisher Copyright: © 2021, The Author(s).

PY - 2021

Y1 - 2021

N2 - Background: Few studies have addressed the clinical parameters' predictive power related to caries lesion associated with their progression. This study assessed the predictive validity and proposed simplified models to predict short-term caries progression using clinical parameters related to caries lesion activity status. Methods: The occlusal surfaces of primary molars, presenting no frank cavitation, were examined according to the following clinical predictors: colour, luster, cavitation, texture, and clinical depth. After one year, children were re-evaluated using the International Caries Detection and Assessment System to assess caries lesion progression. Progression was set as the outcome to be predicted. Univariate multilevel Poisson models were fitted to test each of the independent variables (clinical features) as predictors of short-term caries progression. The multimodel inference was made based on the Akaike Information Criteria and C statistic. Afterwards, plausible interactions among some of the variables were tested in the models to evaluate the benefit of combining these variables when assessing caries lesions. Results: 205 children (750 surfaces) presented no frank cavitations at the baseline. After one year, 147 children were reassessed (70%). Finally, 128 children (733 surfaces) presented complete baseline data and had included primary teeth to be reassessed. Approximately 9% of the reassessed surfaces showed caries progression. Among the univariate models created with each one of these variables, the model containing the surface integrity as a predictor had the lowest AIC (364.5). Univariate predictive models tended to present better goodness-of-fit (AICs < 388) and discrimination (C:0.959–0.966) than those combining parameters (AIC:365–393, C:0.958–0.961). When only non-cavitated surfaces were considered, roughness compounded the model that better predicted the lesions' progression (AIC = 217.7, C:0.91). Conclusions: Univariate model fitted considering the presence of cavitation show the best predictive goodness-of-fit and discrimination. For non-cavitated lesions, the simplest way to predict those lesions that tend to progress is by assessing enamel roughness. In general, the evaluation of other conjoint parameters seems unnecessary for all non-frankly cavitated lesions.

AB - Background: Few studies have addressed the clinical parameters' predictive power related to caries lesion associated with their progression. This study assessed the predictive validity and proposed simplified models to predict short-term caries progression using clinical parameters related to caries lesion activity status. Methods: The occlusal surfaces of primary molars, presenting no frank cavitation, were examined according to the following clinical predictors: colour, luster, cavitation, texture, and clinical depth. After one year, children were re-evaluated using the International Caries Detection and Assessment System to assess caries lesion progression. Progression was set as the outcome to be predicted. Univariate multilevel Poisson models were fitted to test each of the independent variables (clinical features) as predictors of short-term caries progression. The multimodel inference was made based on the Akaike Information Criteria and C statistic. Afterwards, plausible interactions among some of the variables were tested in the models to evaluate the benefit of combining these variables when assessing caries lesions. Results: 205 children (750 surfaces) presented no frank cavitations at the baseline. After one year, 147 children were reassessed (70%). Finally, 128 children (733 surfaces) presented complete baseline data and had included primary teeth to be reassessed. Approximately 9% of the reassessed surfaces showed caries progression. Among the univariate models created with each one of these variables, the model containing the surface integrity as a predictor had the lowest AIC (364.5). Univariate predictive models tended to present better goodness-of-fit (AICs < 388) and discrimination (C:0.959–0.966) than those combining parameters (AIC:365–393, C:0.958–0.961). When only non-cavitated surfaces were considered, roughness compounded the model that better predicted the lesions' progression (AIC = 217.7, C:0.91). Conclusions: Univariate model fitted considering the presence of cavitation show the best predictive goodness-of-fit and discrimination. For non-cavitated lesions, the simplest way to predict those lesions that tend to progress is by assessing enamel roughness. In general, the evaluation of other conjoint parameters seems unnecessary for all non-frankly cavitated lesions.

KW - Caries activity

KW - Dental caries

KW - Primary teeth

KW - Validation studies

KW - Visual inspection

U2 - 10.1186/s12903-021-01568-2

DO - 10.1186/s12903-021-01568-2

M3 - Journal article

C2 - 33980210

AN - SCOPUS:85105837830

VL - 21

JO - BMC Oral Health

JF - BMC Oral Health

SN - 1472-6831

M1 - 255

ER -