Development of a nomogram for the prediction of periodontal tooth loss using the staging and grading system: A long‐term cohort study

A nomogram-based predictive model for tooth survival in Chinese patients with periodontitis: An 11-year retrospective cohort study

AIM

To develop a nomogram-based predictive model of tooth survival by comprehensively analysing clinical and radiographic risk factors of tooth loss (TL).

MATERIALS AND METHODS

In this study, 3447 teeth of 131 subjects who underwent non-surgical periodontal treatment were examined retrospectively within a mean follow-up period of 11.6 years. The association of risk factors including clinical and radiographic parameters with TL was assessed using univariate and multivariate Cox regression analyses. A nomogram-based predictive model was developed, and its validation and discriminatory ability were analysed.

RESULTS

In all, 313 teeth were lost in 94 patients in this study (overall tooth loss [OTL] 9.08%; 0.21 teeth/patient/year). Male, heavy smoking, molar teeth, probing depth (PD), attachment loss (AL), tooth mobility and radiographic bone loss were significantly associated with TL (p < .05). A gradient effect of tooth mobility on TL increased from degree I to III versus none (p < .0001). The area under the curve (AUC) of the model was 0.865. Calibration curve and decision curve analysis demonstrated good performance and high net benefit, respectively.

CONCLUSIONS

Adopting a specific nomogram could facilitate the prediction of tooth survival and the development of tailored treatment plans in Chinese patients with advanced periodontitis.

Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis

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.

ACES: A new framework for the application of the 2018 periodontal status classification scheme to epidemiological survey data

AIM

To propose a framework for consistently applying the 2018 periodontal status classification scheme to epidemiological surveys (Application of the 2018 periodontal status Classification to Epidemiological Survey data, ACES).

PROPOSED FRAMEWORK

We specified data requirements and workflows for either completed or planned epidemiological surveys, utilizing commonly collected measures of periodontal status (clinical attachment levels [CAL], probing depths, bleeding on probing), as well as additional necessary variables for the implementation of the 2018 periodontal status classification (tooth loss due to periodontitis and complexity factors). Following detailed instructions and flowcharts, survey participants are classified as having periodontal health, gingivitis or periodontitis. Rates of edentulism must also be reported. In cases of periodontitis, instructions on how to compute the stage and extent are provided. Assessment of grade can be derived from CAL measurements (or from radiographic alveolar bone loss data) in relation to root length and the participant's age.

CONCLUSIONS

ACES is a framework to be used in epidemiological studies of periodontal status that (i) have been completed, and in which stage and grade according to the 2018 classification are inferred retroactively, or (ii) are being planned. Consistent use of the proposed comprehensive approach will facilitate the comparability of periodontitis prevalence estimates across studies.

A nomogram prediction of implant apical non-coverage on bone-added transcrestal sinus floor elevation: A retrospective cohort study

OBJECTIVES

To identify the risk indicators and develop and validate a nomogram prediction model of implant apical non-coverage by comprehensively analyzing clinical and radiographic factors in bone-added transcrestal sinus floor elevation (TSFE).

MATERIAL AND METHODS

A total of 260 implants in 195 patients receiving bone-added TSFE were included in the study. The population was divided into a development (180 implants) and a validation (80 implants) cohort. According to 6 months post-surgery radiographic images, implants were categorized as "apical non-coverage" or "apical covered." The association of risk factors including clinical and radiographic parameters with implant apical non-coverage was assessed using regression analyses. A nomogram prediction model was developed, and its validation and discriminatory ability were analyzed.

RESULTS

The nomogram predicting bone-added TSFE's simultaneously placed implant's apex non-coverage after 6 months. This study revealed that sinus angle, endo-sinus bone gain, implant protrusion length, graft contact walls, and distal angle were predictors of implant apical non-coverage. The generated nomogram showed a strong predictive capability (area under the curve [AUC] = 0.845), confirmed by internal validation using 10-fold cross-validation (Median AUC of 0.870) and temporal validation (AUC = 0.854). The calibration curve and decision curve analysis demonstrated good performance and high net benefit of the nomogram, respectively.

CONCLUSIONS

The clinical implementation of the present nomogram is suitable for predicting the apex non-coverage of implants placed simultaneously with bone-added TSFE after 6 months.

Development and international validation of logistic regression and machine-learning models for the prediction of 10-year molar loss

AIM

To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients.

MATERIALS AND METHODS

Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine-learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K-nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment.

RESULTS

The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis-related molar loss. In addition, the ensembled model showed the best calibration performance.

CONCLUSIONS

Through a multi-centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice.

Evaluation of periodontitis-related tooth loss according to the new 2018 classification of periodontitis

The new 2018 classification of periodontal diseases is reported to be related to tooth loss due to periodontal disease (TLPD) during supportive periodontal therapy (SPT). However, few reports have evaluated this relationship for Asians or have analyzed the association of the new classification and TLPD by distinguishing between active periodontal therapy (APT) and SPT. In this study, we retrospectively applied the new classification to 607 Japanese periodontitis patients and examined the relationship between the new classification and annual TLPD rates per patient during the respective periods. TLPD rates were higher in patients in stage IV and/or grade C during both APT and SPT. TLPD during SPT was not associated with the presence or absence of TLPD during APT. Multivariate analysis revealed that stage IV and grade C as independent variables were significantly associated with the number of instances of TLPD not only during the total treatment period, but also during APT or SPT. Our results suggest that the new classification has a significantly strong association with TLPD during both APT and SPT, and that patients diagnosed with stage IV and/or grade C periodontitis had a higher risk of TLPD during both periods.

Who to Be Treated: Nomogram Using Self-Reported Periodontal Screening Instrument among English-Speaking Adults in Multi-Ethnic Singapore

Periodontal disease is a major public health problem. This study aimed to develop a nomogram using a self-reported periodontitis screening instrument in predicting severe periodontitis (SP), defined by the World Workshop on Classification of Periodontal and Peri-Implant Diseases and Conditions, and evaluate its utility in clinical setting. An Akaike information criterion selected multivariable model was developed to predict SP using a self-reported questionnaire, with a nomogram developed based on its regression coefficients. Discriminatory capability was evaluated by Receiver-operating characteristic curve. Ability to predict SP of individual patients was evaluated with bootstrapping. Decision curve analysis (DCA) was performed to evaluate its potential clinical utility by evaluating clinical net benefit at different thresholds. 58.1% of 155 participants were classified with SP. Older males without tertiary education, with ‘loose teeth’, ‘bone loss’ and ‘mouth rinse use’ had higher SP risk. The nomogram showed excellent discriminatory capability with Area under Curve of 0.83 (95% CI = (0.76, 0.89)), good calibration (intercept = 0.026) and slight overestimation of high risk and underestimation of low risk (slope = 0.834). DCA showed consistent clinical net benefit across the range of thresholds relative to assumption of ‘no patient’ or ‘all patient’ with SP. Our nomogram using a self-reported periodontitis instrument is useful in SP screening in English-speaking Singaporean adults.

Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis

Prevalence of tooth loss has increased due to population aging. Tooth loss negatively affects the overall physical and social well-being of older adults. Understanding the role of socio-demographic and other predictors associated with tooth loss that are measured in non-clinical settings can be useful in community-level prevention. We used high-dimensional epidemiological data to investigate important factors in predicting tooth loss among older adults over a 6-year period of follow-up. Data was from participants of 2010 and 2016 waves of the Japan Gerontological Evaluation Study (JAGES). A total of 19,407 community-dwelling functionally independent older adults aged 65 and older were included in the analysis. Tooth loss was measured as moving from a higher number of teeth category at the baseline to a lower number of teeth category at the follow-up. Out of 119 potential predictors, age, sex, number of teeth, denture use, chewing difficulty, household income, employment, education, smoking, fruit and vegetable consumption, community participation, time since last health check-up, having a hobby, and feeling worthless were selected using Boruta algorithm. Within the 6-year follow-up, 3013 individuals (15.5%) reported incidence of tooth loss. People who experienced tooth loss were older (72.9 ± 5.2 vs 71.8 ± 4.7), and predominantly men (18.3% vs 13.1%). Extreme gradient boosting (XGBoost) machine learning prediction model had a mean accuracy of 90.5% (±0.9%). A visual analysis of machine learning predictions revealed that the prediction of tooth loss was mainly driven by demographic (older age), baseline oral health (having 10-19 teeth, wearing dentures), and socioeconomic (lower household income, manual occupations) variables. Predictors related to wide a range of determinants contribute towards tooth loss among older adults. In addition to oral health related and demographic factors, socioeconomic factors were important in predicting future tooth loss. Understanding the behaviour of these predictors can thus be useful in developing prevention strategies for tooth loss among older adults.

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

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.

The 2018 classification of periodontal diseases. Its predictive value for tooth loss

BACKGROUND

Predictive models and assessment tools for disease susceptibility and progression are necessary to enhance personalized medicine. The aim of this study is to assess the predictive accuracy of using the 2018 classification to predict likelihood of tooth loss.

MATERIAL AND METHODS

134 patients were screened 10 years after periodontal therapy. Data were extracted from 82 patients' records and periodontal diagnoses were assigned according to the 1999 and 2018 classifications at baseline, whereas patient- and tooth-related parameters were documented at baseline and at reexamination. Statistical analysis included descriptive statistics, hurdle regression with a zero and count model as well as logistic regression.

RESULTS

Significantly more teeth were lost during SPT in patients with Stage IV or Grade C (p<0.05). Patients' adherence seems to have an impact on the predictability of the 2018 classification (p<0.001). In comparison, neither classification system alone (1999 vs. 2018) showed a high predictive value for tooth loss (Area under the curve (AUC) = 59.2% vs. 58.2%).

CONCLUSION

Class III and IV/grade C of the 2018 classification of periodontal diseases show similar predictive accuracy for tooth loss as severe cases in the former classification. Patients adherence seems to influence the prognostic value of the classification. This article is protected by copyright. All rights reserved.

Predictors of tooth loss during long-term periodontal maintenance: An updated systematic review

AIM

To evaluate the risk factors / predictors of tooth loss in patients with periodontitis who underwent periodontal therapy and long-term periodontal maintenance (PM).

MATERIAL AND METHODS

PubMed, CENTRAL, EMBASE, Web of Science, LILACS and Scholar were searched up to and including September 2020. Studies limited to periodontitis patients who underwent active periodontal therapy (APT) and followed a regular PM programme with 5 years follow-up minimum were eligible for inclusion in this review. Studies were included if they reported data on tooth loss during PM. Random effects meta-analyses of number of tooth loss per patient per year were conducted.

RESULTS

Thirty-six papers regarding thirty-three studies were included in this review, with three prospective 30 retrospective trials. Subgroup meta-analysis showed no differences between prospective and retrospective studies, with an average of 0.1 tooth loss per year per patient (p < 0.001). Maxillary and molar teeth were more susceptible to be extracted during long-term PM. Baseline characteristics (smoking, diabetes mellitus, cardiovascular disease, being male and teeth with furcation lesions) showed no significance as predictor of tooth loss through meta-regression. The percentage of tooth loss due to periodontal reasons ranged from 0.45% to 14.4%. The individual outcomes in each study evidenced different patient-related factors (age and smoking) and tooth-related factors (i.e. tooth type and location) were associated with tooth loss during PM.

CONCLUSION

The majority of patients undergoing long-term PM have not lost teeth. On average, long-term PM effectively causes the loss of 1 tooth per patient every 10 years. Additional prospective trials may confirm these results.