Caries Risk Prediction Models in a Medical Health Care Setting

Abstract

Despite development of new technologies for caries control, tooth decay in primary teeth remains a major global health problem. Caries risk assessment (CRA) models for toddlers and preschoolers are rare. Among them, almost all models use dental factors (e.g., past caries experience) to predict future caries risk, with limited clinical/community applicability owing to relatively uncommon dental visits compared to frequent medical visits during the first year of life. The objective of this study was to construct and evaluate risk prediction models using information easily accessible to medical practitioners to forecast caries at 2 and 3 y of age. Data were obtained from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) mother-offspring cohort. Caries was diagnosed using modified International Caries Detection and Assessment System criteria. Risk prediction models were constructed using multivariable logistic regression coupled with receiver operating characteristic analyses. Imputation was performed using multiple imputation by chained equations to assess effect of missing data. Caries rates at ages 2 y ( n = 535) and 3 y ( n = 721) were 17.8% and 42.9%, respectively. Risk prediction models predicting overall caries risk at 2 and 3 y demonstrated area under the curve (AUC) (95% confidence interval) of 0.81 (0.75–0.87) and 0.79 (0.74–0.84), respectively, while those predicting moderate to extensive lesions showed 0.91 (0.85–0.97) and 0.79 (0.73–0.85), respectively. Postimputation results showed reduced AUC of 0.75 (0.74–0.81) and 0.71 (0.67–0.75) at years 2 and 3, respectively, for overall caries risk, while AUC was 0.84 (0.76–0.92) and 0.75 (0.70–0.80), respectively, for moderate to extensive caries. Addition of anterior caries significantly increased AUC in all year 3 models with or without imputation (all P < 0.05). Significant predictors/protectors were identified, including ethnicity, prenatal tobacco smoke exposure, history of allergies before 12 mo, history of chronic maternal illness, maternal brushing frequency, childbearing age, and so on. Integrating oral-general health care using medical CRA models may be promising in screening caries-susceptible infants/toddlers, especially when medical professionals are trained to “lift the lip” to identify anterior caries lesions.

Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks

“…Therefore, dental caries seems to be a remarkably divergent trait constantly changing in time and with its occurrence increasing with age. The prevalence of caries experience in our study was 18.3% and was comparable to children of the corresponding age of other ethnicities, i.e., Western Europe, Eastern-Southern Asia and Sub-Saharan Africa [ 3 , 29 , 56 ]. Interestingly, it appeared to be much lower when compared to other studies concerning Polish children with active caries of distinct regions, i.e., 40.8% in the Podlasie region [ 57 ], 53.8% in Lower Silesian, Malopolskie and Lubelskie voivodships [ 4 ] or from 35% to 56.6% in the general 2–3-year-olds population [ 3 , 13 , 57 ].…”

“…However, it is challenging to assess the previous caries experience in infants and toddlers. Most of the previous studies and distinct caries prediction models apply to adults, school children or older preschoolers, while models for toddlers are scarce [ 29 ]. Another obstacle in assessing well-performing caries prediction model are discrepancies among the studies, i.e., imprecise definition of caries phenotype and caries lesions or inconsistency in the terms used by the researchers [ 9 , 23 , 30 , 31 ].…”

“…Age appears one of the key factors in caries analysis, as the disease experience is differentially defined in reference to the age of patients. Some authors emphasize that each age group should be characterized by other sets of temporal variables [ 50 ], with the most important risk factors as follows: in 2-year-olds—allergies and infections before first tooth eruption and intake of drugs during the first 12 months of life, in 3-year-olds—mother’s age at the time of pregnancy and smoking during pregnancy [ 29 ], consumption of sweetened food during first 12 months of life and nocturnal drinking of sweet drinks above 12th month [ 3 ], in 4-year-olds and older children—frequency of tooth brushing, fluoride treatment and mother’s education [ 51 ].…”

“…Still, depending on the population and its habits, prediction models using the same risk factors might be characterized by distinct final prediction parameters, as well as comparable prediction features of different models might be determined by distinct input variables [ 8 ]. To mention a few, Kalhan et al reached a high AUC value of 0.81–0.91 of caries prediction in 2-year-olds and 0.79 in 3-year-olds [ 29 ], while Fontana et al [ 60 ] assessed AUC of 0.73 in 4-year-old children. Tamaki et al [ 61 ] came across 33 studies on caries prediction, however, the majority of them used logistic regression analyses and only some authors used additional, advanced machine learning algorithms to compare the results, namely artificial neural network, decision analysis or classification and regression trees [ 27 , 50 , 61 , 62 , 63 ].…”

Improving Caries Risk Prediction Modeling: A Call for Action

“…CRPMs must evaluate their predictive accuracy (Kalhan et al 2020). An ideal CRPM will correctly discriminate those who will develop a new carious lesion from those who will not 100% of the time.…”