BACKGROUND: The American Academy of Periodontology has recently stated that, "[risk assessment will become] increasingly important in periodontal treatment planning and should be part of every comprehensive dental and periodontal evaluation." (J Periodontol 2006;77:1608). Unaided risk assessment and prognostication show significant variability because chronic periodontitis is a multifactorial disease. This report summarizes the clinical validation of an algorithm for chronic periodontitis risk assessment and prognostication. The algorithm is a Web-based analytic tool that integrates some 20 risk predictors and calculates scores indicating levels of risk for chronic periodontitis for the dentition (Level I) and, if an elevated risk is found, prognosticates disease progression tooth by tooth (Level II). METHODS: An independent clinical validation sample was generated in an open, prospective clinical trial and analyzed in a predetermined validation plan. RESULTS: The analyses identified two threshold scores above which significant progression of periodontitis was found. Based on these scores, sufficiently high explanatory values with significant and increasing parameter estimates for increasing risk were established in Level I, justifying detailed analysis tooth by tooth in Level II. Subsequent prognostication of chronic periodontitis in Level II was found to be accompanied by clinically relevant measures of quality in relation to rates of disease progression. Three score intervals representing increasing levels of periodontitis progression were identified corresponding to increasing levels of significant annual marginal bone loss. CONCLUSIONS: The predictors included in the algorithm reflect a relevant selection for periodontitis risk assessment. Risk assessment and prognostication with the algorithm provides the clinician with a validated, reliable, consistent, and objective tool supporting treatment planning.
BACKGROUND: The American Academy of Periodontology has recently stated that, "[risk assessment will become] increasingly important in periodontal treatment planning and should be part of every comprehensive dental and periodontal evaluation." (J Periodontol 2006;77:1608). Unaided risk assessment and prognostication show significant variability because chronic periodontitis is a multifactorial disease. This report summarizes the clinical validation of an algorithm for chronic periodontitis risk assessment and prognostication. The algorithm is a Web-based analytic tool that integrates some 20 risk predictors and calculates scores indicating levels of risk for chronic periodontitis for the dentition (Level I) and, if an elevated risk is found, prognosticates disease progression tooth by tooth (Level II). METHODS: An independent clinical validation sample was generated in an open, prospective clinical trial and analyzed in a predetermined validation plan. RESULTS: The analyses identified two threshold scores above which significant progression of periodontitis was found. Based on these scores, sufficiently high explanatory values with significant and increasing parameter estimates for increasing risk were established in Level I, justifying detailed analysis tooth by tooth in Level II. Subsequent prognostication of chronic periodontitis in Level II was found to be accompanied by clinically relevant measures of quality in relation to rates of disease progression. Three score intervals representing increasing levels of periodontitis progression were identified corresponding to increasing levels of significant annual marginal bone loss. CONCLUSIONS: The predictors included in the algorithm reflect a relevant selection for periodontitis risk assessment. Risk assessment and prognostication with the algorithm provides the clinician with a validated, reliable, consistent, and objective tool supporting treatment planning.
Authors: Pedro Martinez-Canut; Jaime Alcaraz; Jaime Alcaraz; Pablo Alvarez-Novoa; Carmen Alvarez-Novoa; Ana Marcos; Blas Noguerol; Fernando Noguerol; Ion Zabalegui Journal: J Clin Periodontol Date: 2017-11-28 Impact factor: 8.728
Authors: Joanna Mullins; Alfa Yansane; Elsbeth Kalenderian; Muhammad F Walji; Shwetha V Kumar; Suhasini Bangar; Ana Neumann; Todd R Johnson; Gregory W Olson; Krishna Kumar Kookal; Emily Sedlock; Aram Kim; Elizabeth Mertz; Ryan Brandon; Kristen Simmons; Joel M White Journal: BMC Oral Health Date: 2021-05-29 Impact factor: 2.757