Caries Risk Prediction Models in a Medical Health Care Setting

Predicting Dental Caries in Young Children in Primary Health Care Settings

Young children need increased access to dental prevention and care. Targeting high caries risk children first helps meet this need. The objective of this study was to develop a parent-completed, easy-to-score, short, accurate caries risk tool for screening in primary health care settings to identify children at increased risk for cavities. A longitudinal, prospective, multisite, cohort study enrolled (primarily through primary health care settings) and followed 985 (out of 1,326) 1-y-old children and their primary caregivers (PCGs) until age 4. The PCG completed a 52-item self-administered questionnaire, and children were examined using the International Caries Detection and Assessment Criteria (ICDAS) at 12 ± 3 mo (baseline), 30 ± 3 mo (80% retention), and 48 ± 3 mo of age (74% retention). Cavitated caries lesion (dmfs = decayed, missing, and filled surfaces; d = ICDAS ≥3) experience at 4 y of age was assessed and tested for associations with questionnaire items using generalized estimating equation models applied to logistic regression. Multivariable analysis used backward model selection, with a limit of 10 items. At age 4, 24% of children had cavitated-level caries experience; 49% were female; 14% were Hispanic, 41% were White, 33% were Black, 2% were other, and 10% were multiracial; 58% enrolled in Medicaid; and 95% lived in urban communities. The age 4 multivariable prediction model, using age 1 responses (area under the receiver operating characteristic curve = 0.73), included the following significant (P < 0.001) variables (odds ratios): child participating in public assistance programs such as Medicaid (1.74), being non-White (1.80-1.96), born premature (1.48), not born by caesarean section (1.28), snacking on sugary snacks (3 or more/d, 2.22; 1-2/d or weekly, 1.55), PCG cleaning the pacifier with juice/soda/honey or sweet drink (2.17), PCG daily sharing/tasting food with child using same spoon/fork/glass (1.32), PCG brushing their teeth less than daily (2.72), PCG's gums bleeding daily when brushing or PCG having no teeth (1.83-2.00), and PCG having cavities/fillings/extractions in past 2 y (1.55). A 10-item caries risk tool at age 1 shows good agreement with cavitated-level caries experience by age 4.

Caries risk assessment-related knowledge, attitude, and behaviors among Chinese dentists: a cross-sectional survey

OBJECTIVES

To investigate caries risk assessment (CRA)-related knowledge, attitudes, and practices among dentists in China, to describe their subjective ratings of the significance of specific caries risk factors and to identify factors associated with the level of knowledge, attitudes, and use of CRA in routine clinical practice.

MATERIALS AND METHODS

A cross-sectional anonymous online questionnaire survey was performed. The questionnaire was distributed via WeChat (Tencent, Shenzhen, China) to practicing dentists between November 25 and December 25, 2021. For participant recruitment, we employed purposive and snowball sampling techniques. Data were collected using a specialized web-based survey tool ( www.wjx.cn ) and analyzed with descriptive statistics and regression analyses.

RESULTS

A total of 826 valid questionnaires were collected. Only 292 (35.4%) respondents used CRA in routine practice, among whom a majority (243, 83.2%) did not use a specific CRA tool. The routine use of CRA was associated with the type of practicing office, attendance of caries-related lectures, the habit of reading caries-related literature, geographic location, and the total knowledge score. The mean total knowledge score was 3.13 (score range: 0 to 6). Knowledge levels were related to several sociodemographic characteristics, including geographic location, the type of practicing office, attendance of caries-related lectures and the habit of reading caries-related literature. The risk factor deemed most important was "current oral hygiene."

CONCLUSIONS

Caries risk assessment has not widely entered clinical practice in China. The level of CRA-related knowledge among dentists was generally suboptimal.

CLINICAL RELEVANCE

Strengthening CRA-related education may allow practitioners to develop a better understanding of caries risk assessment and hence promote its implementation.

Prediction model for early childhood caries risk based on behavioral determinants using a machine learning algorithm

BACKGROUND

An easily accessible caries risk prediction model (CRPM) based on nonbiological predictors is lacking. Developing a CRPM for community screening is essential for children's dental health promotion by a public health approach.

OBJECTIVE

This study aimed to develop and validate a caries risk prediction model (CRPM) for children using a machine learning algorithm based on dental care behavioral factors and other nonbiological factors using a 3-month multicenter cohort.

METHODS

Children aged 12 months to 60 months were recruited at three primary care settings and three kindergartens in Chengdu, China. Dental examination was conducted for all enrolled children by calibrated pediatric dentists at baseline and three months later. All parents of the enrolled children were asked to complete a questionnaire with dental-related information. Machine learning algorithms, including random forest, logistic regression, and adaptive boosting, were used to develop a prediction model. Sensitivity, specificity, accuracy, precision, negative predictive value and F-score were reported to estimate the internal validation of the models.

RESULTS

A total of 481 out of 745 children without a history of caries experience at baseline remained for analysis. In the total sample population, 236 (49.1%) children were female, and the mean age was 31.2 months. During the follow-up exams, 66 (13.6%) children had new-onset caries. The child's age, height, weight, family caries status, brush teeth two minutes per time, fluoride toothpaste usage, brushing twice per day, parental monitoring brushing teeth, mother delivery method, brushing child's teeth every day, child number counts, and night feeding frequency in the last month were measured and included in a prediction model. Of the prediction models, the highest area under the curve of RF was 0.91 (95% CI: 0.87- 0.94), followed by 0.86 (95% CI: 0.81-0.91) of LR and 0.81 (95% CI: 0.76-0.86) of AdaBoost.

CONCLUSION

In this CRPM, new onset of dental caries in three months among children aged < 60 months could be predicted by answering twelve nonbiological questions. A good model performance was shown within the internal validation. Dental home care could be improved by referring the CRPM result before new caries onset.

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

Background: Several genes and single nucleotide polymorphisms (SNPs) have been associated with early childhood caries. However, they are highly age- and population-dependent and the majority of existing caries prediction models are based on environmental and behavioral factors only and are scarce in infants. Methods: We examined 6 novel and previously analyzed 22 SNPs in the cohort of 95 Polish children (48 caries, 47 caries-free) aged 2–3 years. All polymorphisms were genotyped from DNA extracted from oral epithelium samples. We used Fisher’s exact test, receiver operator characteristic (ROC) curve and uni-/multi-variable logistic regression to test the association of SNPs with the disease, followed by the neural network (NN) analysis. Results: The logistic regression (LogReg) model showed 90% sensitivity and 96% specificity, overall accuracy of 93% (p < 0.0001), and the area under the curve (AUC) was 0.970 (95% CI: 0.912–0.994; p < 0.0001). We found 90.9–98.4% and 73.6–87.2% prediction accuracy in the test and validation predictions, respectively. The strongest predictors were: AMELX_rs17878486 and TUFT1_rs2337360 (in both LogReg and NN), MMP16_rs1042937 (in NN) and ENAM_rs12640848 (in LogReg). Conclusions: Neural network prediction model might be a substantial tool for screening/early preventive treatment of patients at high risk of caries development in the early childhood. The knowledge of potential risk status could allow early targeted training in oral hygiene and modifications of eating habits.

Improving Caries Risk Prediction Modeling: A Call for Action

Dentistry has entered an era of personalized/precision care in which targeting care to groups, individuals, or even tooth surfaces based on their caries risk has become a reality to address the skewed distribution of the disease. The best approach to determine a patient's prognosis relies on the development of caries risk prediction models (CRPMs). A desirable model should be derived and validated to appropriately discriminate between patients who will develop disease from those who will not, and it should provide an accurate estimation of the patient's absolute risk (i.e., calibration). However, evidence suggests there is a need to improve the methodological standards and increase consistency in the way CRPMs are developed and evaluated. In fact, although numerous caries risk assessment tools are available, most are not routinely used in practice or used to influence treatment decisions, and choice is not commonly based on high-quality evidence. Research will propose models that will become more complex, incorporating new factors with high prognostic value (e.g., human genetic markers, microbial biomarkers). Big data and predictive analytic methods will be part of the new approaches for the identification of promising predictors with the ability to monitor patients' risk in real time. Eventually, the implementation of validated, accurate CRPMs will have to follow a user-centered design respecting the patient-clinician dynamic, with no disruption to the clinical workflow, and needs to operate at low cost. The resulting predictive risk estimate needs to be presented to the patient in an understandable way so that it triggers behavior change and effectively informs health care decision making, to ultimately improve caries outcomes. However, research on these later aspects is largely missing and increasingly needed in dentistry.