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Comparison of Two Periodontal Risk Profile Assessment Tools

Abstract

Objective: The aim of this study is to compare two partially validated periodontal risk profile assessment tools designed to assess a patient's risk of developing periodontitis: the Periodontal Risk Assessment [PRA] and the Periodontal Risk Calculator [PRC]. The study will investigate the agreement or concordance of these two periodontal risk models when applied to the same study population.

Methods: A group of 100 subjects was assembled by random chart selection from patients seeking care at the UCSF Periodontology Clinic. Each subject's dental/medical history, clinical data, and radiographic data were reviewed, and the risk profile was generated according to both the PRA and the PRC models. The level of agreement between the risk profile as assessed by the PRA and the PRC models on the same study population was analyzed.

Results: Of the 100 subjects assessed, 14 low-risk, 49 medium-risk, and 37 high-risk cases were categorized by the PRA model, whereas the PRC model placed the same subjects in 13 low-risk, 16 medium-risk, and 71 high-risk groups. Statistical analysis demonstrated that there was only a weak level of agreement between the two models in identifying medium- and high-risk groups, while there was a very good level of agreement in identifying low-risk group. The PRC model identified more subjects as at high-risk of developing periodontitis in the future, while the PRA model identified a higher number of subjects as medium-risk group for future periodontal health breakdown.

Conclusions: The results suggest that risk scores generated for individual patients by different periodontal risk assessment models are highly variable. When used in periodontal clinical-decision making, choice of periodontal risk model could affect the risk assessment and may result in the misapplication of treatment for some patients. Long-term study on the validity and accuracy of current periodontal risk assessment models are needed to better achieve the goal of early identification of at-risk populations and formulation of proactive targeted interventions.

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