Comparison of two different periodontal risk assessment methods with regard to their agreement: Periodontal risk assessment versus periodontal risk calculator

Petsos, Hari; Arendt, Susanne; Eickholz, Peter; Nickles, Katrin; Dannewitz, Bettina

doi:10.1111/jcpe.13327

Cited by 7 publications

(9 citation statements)

References 43 publications

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“…PRA calculation was based on six parameters as previously described: (a) percentage of BOP, (b) number of residual PPD ≥ 5 mm, (c) tooth loss due to periodontitis, (d) BL/age considering the worst site affected, (e) systemic (diabetes) or genetic factors, and (f) environmental factor (smoking status) [ 16 , 17 , 18 ].…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, they could help to optimize clinical decision making and improve oral health [ 14 ]. In periodontology, scoring tools, such as the Periodontal Risk Calculator (PRC) [ 15 ], Perio Risk [ 14 ], the Periodontal Risk Assessment (PRA) [ 16 ], and their modifications, have been developed and evaluated for a long time [ 13 , 17 ]. The reliability of each of these tools has been demonstrated for predicting tooth loss [ 14 , 17 , 18 , 19 , 20 , 21 ] and/or periodontitis recurrence/progression [ 20 , 22 ] during the long-term periodontal follow-up, and they are mainly used to adapt periodontal maintenance frequencies at the end of active periodontal therapy [ 13 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…These scores could be useful for identifying and managing modifiable risk factors both after as well as prior to implant placement and prosthesis treatment, to adapt maintenance frequency, and to communicate the risk to the patient [ 10 , 11 ]. However, the relative heterogeneity of the parameters included to calculate risk scores raises the question of parameter selection criteria and calculation methods as previously discussed for periodontal diseases [ 17 ]. In some studies, only highly associated risk factors in the tested population are included in score calculation [ 11 , 12 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Two Risk Assessment Scores in Predicting Peri-Implantitis Occurrence during Implant Maintenance in Patients Treated for Periodontal Diseases: A Long-Term Retrospective Study

Sarbacher

Papalou

Vagia

et al. 2022

JCM

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Background: There is a need for reliable risk assessment tools to better predict peri-implantitis occurrence. This study compared the long-term prognosis value of two models of risk assessment scoring in predicting peri-implantitis. Methods: Seventy-three patients with treated periodontitis representing 232 implants and attending long-term implant maintenance were evaluated. The Periodontal Risk Assessment (PRA) score, which combines only periodontal risk factors/indicators, and the Implant Risk Assessment (IRA) score, which combines both periodontal and implant risk factors/indicators, were calculated during implant maintenance. Peri-implantitis was defined by the presence of probing depth ≥6 mm with bleeding on probing/suppuration and bone level ≥3 mm. Analyses were performed at the patient level. Results: The mean implant follow-up was 6.5 years. Peri-implantitis incidence was 17.8%, and high-risk PRA and IRA percentages were 36.9% and 27.3%, respectively. High-risk PRA and IRA were significantly associated with peri-implantitis incidence, with hazard ratio (HR) = 4.8 and 3.65, respectively. Risk factors/indicators considered separately showed reduced associations with peri-implantitis. Conclusions: The PRA score combining periodontal parameters and IRA score combining both periodontal and implant parameters have comparable value in predicting peri-implantitis. These scores could allow practicians to intercept the risk of peri-implantitis and to manage follow-up modalities in patients with treated periodontitis.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Two Risk Assessment Scores in Predicting Peri-Implantitis Occurrence during Implant Maintenance in Patients Treated for Periodontal Diseases: A Long-Term Retrospective Study

Sarbacher

Papalou

Vagia

et al. 2022

JCM

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“…Multiple studies showed low levels of inter‐model categorical agreement when comparing class hierarchy from different PRATs. 33–35 The frequency occurrence of each group category can be appreciated in Figure 1.…”

Section: Discussionmentioning

confidence: 99%

Long term comparison of the prognostic performance of PerioRisk, periodontal risk assessment, periodontal risk calculator, and staging and grading systems

Saleh

Dukka

Troiano

et al. 2021

Journal of Periodontology

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Background Clinicians predominantly use personal judgment for risk assessment. Periodontal risk assessment tools (PRATs) provide an effective and logical system to stratify patients based on their individual treatment needs. This retrospective longitudinal study aimed to validate the association of different risk categories of four PRATs (Staging and grading; Periodontal Risk Assessment (PRA); Periodontal Risk Calculator; and PerioRisk) with periodontal related tooth loss (TLP), and to compare their prognostic performance. Methods Data on medical history, smoking status, and clinical periodontal parameters were retrieved from patients who received surgical and non‐surgical periodontal treatment. A comparison of the rate of TLP and non‐periodontal related tooth loss (TLO) within the risk tool classes were performed by means of Kruskal‐Wallis test followed by post‐hoc comparison with the Bonferroni test. Both univariate and multivariate Cox Proportional hazard regression models were built to analyze the prognostic significance for each single risk assessment tool class on TLP. Results A total of 167 patients with 4321 teeth followed up for a mean period of 26 years were assigned to four PRATs. PerioRisk class 5 had a hazard ratio of 18.43, Stage 4 had a hazard ratio of 7.99, and PRA class 3 had a hazard ratio of 6.13 compared with class/stage I. With respect to prognostic performance, PerioRisk tool demonstrated the best discrimination and model fit followed by PRA. Conclusion All PRATs displayed very good predictive capability of TLP. PerioRisk showed the best discrimination and model fit, followed by PRA.

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“…In contrast to this, the prognostic tool described in this study attempts to assess the risk for tooth loss in periodontally compromised patients at the beginning of treatment in order to support treatment planning. Furthermore, the two periodontal risk assessment methods (PRA and PRC) do not seem to have a high level of agreement in terms of patient’s individual risk for disease progression [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Development of a prognostic tool: based on risk factors for tooth loss after active periodontal therapy

et al. 2021

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Objectives The aim of this study was to develop a prognostic tool to estimate long-term tooth retention in periodontitis patients at the beginning of active periodontal therapy (APT). Material and methods Tooth-related factors (type, location, bone loss (BL), infrabony defects, furcation involvement (FI), abutment status), and patient-related factors (age, gender, smoking, diabetes, plaque control record) were investigated in patients who had completed APT 10 years before. Descriptive analysis was performed, and a generalized linear-mixed model-tree was used to identify predictors for the main outcome variable tooth loss. To evaluate goodness-of-fit, the area under the curve (AUC) was calculated using cross-validation. A bootstrap approach was used to robustly identify risk factors while avoiding overfitting. Results Only a small percentage of teeth was lost during 10 years of supportive periodontal therapy (SPT; 0.15/year/patient). The risk factors abutment function, diabetes, and the risk indicator BL, FI, and age (≤ 61 vs. > 61) were identified to predict tooth loss. The prediction model reached an AUC of 0.77. Conclusion This quantitative prognostic model supports data-driven decision-making while establishing a treatment plan in periodontitis patients. In light of this, the presented prognostic tool may be of supporting value. Clinical relevance In daily clinical practice, a quantitative prognostic tool may support dentists with data-based decision-making. However, it should be stressed that treatment planning is strongly associated with the patient’s wishes and adherence. The tool described here may support establishment of an individual treatment plan for periodontally compromised patients.

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Comparison of two different periodontal risk assessment methods with regard to their agreement: Periodontal risk assessment versus periodontal risk calculator

Abstract: This is an open access article under the terms of the Creative Commons Attribution NonCommercial License which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 7 publications

References 43 publications

Comparison of Two Risk Assessment Scores in Predicting Peri-Implantitis Occurrence during Implant Maintenance in Patients Treated for Periodontal Diseases: A Long-Term Retrospective Study

Comparison of Two Risk Assessment Scores in Predicting Peri-Implantitis Occurrence during Implant Maintenance in Patients Treated for Periodontal Diseases: A Long-Term Retrospective Study

Long term comparison of the prognostic performance of PerioRisk, periodontal risk assessment, periodontal risk calculator, and staging and grading systems

Development of a prognostic tool: based on risk factors for tooth loss after active periodontal therapy

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