Periodontal risk score: Initiation and model validation for 6,762 teeth

Saleh, Muhammad H A; Tattan, Mustafa; Troiano, Giuseppe; Dukka, Himabindu; Ravidà, Andrea; Levine, Robert A.; Wang, Hom‐Lay; Miller, Preston D.

doi:10.1002/jper.22-0273

Cited by 9 publications

(9 citation statements)

References 28 publications

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“…In terms of risk or prognosis determination, tooth-level prognostic systems provide better information. Saleh et al reported recently that the periodontal risk score (PRS), which includes parameters such as age, smoking, diabetes, tooth type, mobility, probing depth, and furcation involvement, was able to predict long-term tooth loss ( 55 ).…”

Section: Discussionmentioning

confidence: 99%

Association between periodontal health status and quality of life: a cross-sectional study

Al-Bitar,

Garcia,

Han

et al. 2024

Front. Oral. Health

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IntroductionAttachment loss due to periodontal diseases is associated with functional limitations as well as physical pain and psychological discomfort, which may lead to a reduced quality of life. The purpose of this study is to determine if the oral health status, specifically the periodontal status, influences oral health–related quality of life.Materials and methodsSurvey data were collected in a US dental school clinical setting in a cross-sectional study. Quality of life related to oral health was assessed with the Oral Health Impact Profile-49 (OHIP-49). In addition, DMFT index, periodontal status, and health literacy scores (dental and medical health literacy) were recorded, and the data of n = 97 subjects were statistically analyzed.ResultsThe DMFT index of the study population was 14.98 ± 6.21 (D: 4.72 ± 4.77; M: 3.19 ± 3.46; F: 7.12 ± 4.62). Of the subjects, 44% were identified as periodontitis cases. These periodontitis cases demonstrated significantly higher OHIP-49 scores (66.93 ± 30.72) than subjects without signs of periodontal diseases (NP) (32.40 ± 19.27, p < 0.05). There was also a significant difference between NP patients and patients with gingivitis (66.24 ± 46.12, p < 0.05). It was found that there was a statistically significant difference between Stage 3 (severe) periodontitis and periodontal health (p = 0.003). Pearson correlations were completed, and positive relationships were found with OHIP-49 and DMFT (0.206, p < 0.05), and periodontal risk self-assessment (0.237, p < 0.05). Age [odds ratio (OR) 4.46], smoking (OR 2.67), and the presence of mobile teeth (OR 2.96) are associated with periodontitis.ConclusionsPeriodontal diseases may negatively impact the oral health–related quality of life. Patients suffering from periodontitis also showed more missing teeth, which might influence function. Age and smoking are associated with a higher prevalence of periodontitis. A good general health literacy was no guarantee for having an adequate oral literacy.

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Section: Discussionmentioning

confidence: 99%

Association between periodontal health status and quality of life: a cross-sectional study

Al-Bitar,

Garcia,

Han

et al. 2024

Front. Oral. Health

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“…to display excellent predictive capability for nonmolar anterior as well as posterior tooth loss due to periodontitis. [ 11 ] The PRS was retrospectively able to predict tooth loss with a very high accuracy even in a population treated by dental students and periodontics residents. [ 11 ]…”

Section: Discussionmentioning

confidence: 99%

“…[ 11 ] The PRS was retrospectively able to predict tooth loss with a very high accuracy even in a population treated by dental students and periodontics residents. [ 11 ]…”

Section: Discussionmentioning

confidence: 99%

Ten-year prognostic outcomes of molar survival using the Miller-Mcentire Periodontal Prognostic Index – A longitudinal prospective study

Gopalakrishnan,

Miller,

Levine

et al. 2024

Journal of Indian Society of Periodontology

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Background: A 10-year survival analysis was performed to assess the predictive validity of the periodontal risk score (formerly known as Miller-McEntire Periodontal Prognostic Index [MMPPI]) to predict long-term survival of periodontally diseased molars in a longitudinally assessed cohort. Materials and Methods: The MMPPI scores were computed at baseline by summarizing scores allocated for individual factors. The cohort was treated, followed up, and placed under regular periodontal maintenance. Molar survival data collected up to 10 years of follow-up were analyzed. Cox proportional regression was performed, and hazards ratio (HR) were computed for each prognostic factor and the MMPPI score. To estimate the predictive value of MMPPI, a receiver operating curve (ROC) curve analysis was performed. Results: From 1032 molars, 155 molars were extracted over the 10-year follow-up duration. Cox proportional hazard analysis showed significant hazard ratios for tooth loss for the component variables significant HR was noted for age: 4.92 (3.34:7.27), smoking: 1.74 (1.38–2.22), diabetes: 1.66 (1.49–1.86), molar type: 1.39 (1.15–1.67), probing depth: 2.00 (1.63–2.46), furcation: 2.64 (2.30–3.03), mobility: 3.45 (2.98–4.01), and total MMPPI score: 1.98 (1.85–2.12). ROC curve analysis showed an area under the curve value of 0.94 for the MMPPI index as a predictor of molar loss at 8 years, and the Youden index was maximized at the optimal cutoff point score of 7. Conclusions: All component scores of MMPPI showed significant hazard ratios at 10 years. These findings support the previous results from the 5-year analysis of this university-based cohort and warrant validation in independent cohorts.

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“…This introduces the risk of incorporation bias-where the model performance is artificially inflated by including the predictors as part of the outcome definition (Moons et al 2019). This would be less of a concern in studies where the cause of extraction was determined at the time of extraction (Saleh et al 2022). Narrowing the outcome to TLP also necessitates a competing risk framework: not accounting for competing risks may lead to substantial miscalibration and overestimation of risk (Berry et al 2010).…”

Section: Main Findings and Quality Of Evidencementioning

confidence: 99%

Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis

Chow,

Tay,

Nascimento

2024

J Dent Res

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This study reviews and appraises the methodological and reporting quality of prediction models for tooth loss in periodontitis patients, including the use of regression and machine learning models. Studies involving prediction modeling for tooth loss in periodontitis patients were screened. A search was performed in MEDLINE via PubMed, Embase, and CENTRAL up to 12 February 2022, with citation chasing. Studies exploring model development or external validation studies for models assessing tooth loss in periodontitis patients for clinical use at any time point, with all prediction horizons in English, were considered. Studies were excluded if models were not developed for use in periodontitis patients, were not developed or validated on any data set, predicted outcomes other than tooth loss, or were prognostic factor studies. The CHARMS checklist was used for data extraction, TRIPOD to assess reporting quality, and PROBAST to assess the risk of bias. In total, 4,661 records were screened, and 45 studies were included. Only 26 studies reported any kind of performance measure. The median C-statistic reported was 0.671 (range, 0.57–0.97). All studies were at a high risk of bias due to inappropriate handling of missing data (96%), inappropriate evaluation of model performance (92%), and lack of accounting for model overfitting in evaluating model performance (68%). Many models predicting tooth loss in periodontitis are available, but studies evaluating these models are at a high risk of bias. Model performance measures are likely to be overly optimistic and might not be replicated in clinical use. While this review is unable to recommend any model for clinical practice, it has collated the existing models and their model performance at external validation and their associated sample sizes, which would be helpful to identify promising models for future external validation studies.

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Periodontal risk score: Initiation and model validation for 6,762 teeth

Cited by 9 publications

References 28 publications

Association between periodontal health status and quality of life: a cross-sectional study

Association between periodontal health status and quality of life: a cross-sectional study

Ten-year prognostic outcomes of molar survival using the Miller-Mcentire Periodontal Prognostic Index – A longitudinal prospective study

Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis

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