Development and validation of diagnostic and prognostic prediction tools for dental caries in young children through...

Dental caries is the most common oral disease worldwide, affecting up to 90% of children globally. It can lead to pain, infection and impaired quality of life. Early prevention is a key strategy for reducing the prevalence of dental caries in young children. Valid and reliable diagnostic or prognostic tools that enable accurate individualised prediction of current or future dental caries are essential for facilitating personalised caries prevention and early intervention. However, no efficacious tools currently exist in early childhood-the optimal period for disease prevention. We aim to develop and validate diagnostic and prognostic prediction tools for dental caries in young children, using a combination of environmental, physical, behavioural and biological early life data.

Data sources include two prospective studies, with a total sample size of approximately 600 children. These cohorts have collected detailed demographic, antenatal, perinatal and postnatal data from medical records and parent-completed questionnaires and biological samples including a dental plaque swab. Candidate predictor variables will include sociodemographic characteristics, health history, behavioural and microbiological characteristics. The outcome variable will be the presence, incidence or severity of dental caries diagnosed using the International Caries Detection and Assessment System. Statistical and machine learning approaches will be used for selection of predictor variables and model development. Internal validation will be conducted using resampling methods (i.e., bootstrapping) and nested cross-validation. Model performance will be evaluated using standard performance metrics such as accuracy, discrimination and calibration. Where feasible, external validation will be performed in an independent cohort. Model development and reporting will be guided by the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines.

This study has ethical and governance approval from The Royal Children's Hospital Melbourne Human Research Ethics Committee (HREC/111803/RCHM-2024). Results of this study will be published in peer-reviewed journals and presented at scientific conferences.

Infant2Child: ACTRN12622000205730-pre-results; MisBair: NCT01906853-post results.