External validation and comparison of the predictive performance of 10 different tooth-level prognostic systems

Aim Tooth-level prognostic systems can be used for treatment planning and risk assessment. This retrospective longitudinal study aimed to evaluate the prognostic performance of 10 different tooth-level risk assessment systems in terms of their ability to predict periodontal-related tooth loss (TLP). Materials and methods Data were retrieved retrospectively from patients who received surgical and non-surgical periodontal treatment. Data on medical history and smoking status at baseline and the last maintenance visit were collected. Ten tooth-level prognostic systems were compared using both univariate and multivariate Cox proportional hazard regression models to analyse the prognostic capability of each system for predicting TLP risk. Results One-hundred and forty-eight patients with 3787 teeth, followed-up for a mean period of 26.5 +/- 7.4 years, were evaluated according to 10 different tooth-level prognostic systems, making up a total of 37,870 individual measurements. All compared prognostic systems were able to stratify the risk of TLP at baseline when different classes of association were compared. After controlling for maintenance, age, and gender, all systems exhibited excellent predictive capacity for TLP with no system scoring a Harrell's C-index less than 0.925. Conclusions All tooth-level prognostic systems displayed excellent predictive capability for TLP. Overall, the Miller and McEntire system may have shown the best discrimination and model fit, followed by the Nunn et al. system.

Automated summary

This retrospective study evaluated the prognostic performance of 10 different tooth-level risk assessment systems in predicting periodontal-related tooth loss (TLP). Results showed that all systems were able to effectively stratify the risk of TLP at baseline, with excellent predictive capacity even after controlling for maintenance, age, and gender. The Miller and McEntire system may have exhibited the best discrimination and model fit among the compared systems.