Improving Caries Risk Prediction Modeling: A Call for Action

Abstract

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.