Methodological Quality of Prognostic Models for Periodontitis: A Systematic Review and Critical Appraisal

Aim: To critically appraise the methodological quality and historical evolution of prognostic models for periodontitis. Materials and Methods: A systematic search was conducted in three databases. Eligible studies were classified as conceptual tools, category-based tools and data-driven models. Category-based and data-driven models were evaluated with PROBAST. Risk of bias (ROB) was compared across four domains: Participants, Predictors, Outcome and Analysis. Results: Twenty-seven studies were included. Prognostic methods evolved from qualitative risk categories and expert-based classifications towards quantitative, individualised probability estimates generated through multivariable statistical and machine-learning models, alongside progressive improvements in outcome definition, prediction time and risk expression formats. Conceptual tools were not evaluable under PROBAST due to the absence of empirical data. Category-based tools showed limitations in predictor handling and validation rather than bias per se. Data-driven models demonstrated progressive refinement: earlier studies lacked consistent validation, whereas recent models more frequently report internal validation, calibration metrics and external validation. However, all studies except one were classified as high ROB. Domain-level assessment showed strengths in the Participants domain but persistent limitations in the Analysis domain. Methodological improvements after TRIPOD were modest. Conclusion: Prognostic models reflect distinct methodological profiles across design type. Conceptual and category-based frameworks remain clinically relevant historically, while data-driven models represent the contemporary standard but retain important methodological gaps. Trial Registration: PROSPERO number: CRD42024525505.