A nomogram prediction for mandibular molar survival in Chinese patients with periodontitis: A 10-year retrospective 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.

Influence of mobility on the long-term risk of tooth extraction/loss in periodontitis patients. A systematic review and meta-analysis

The aim of this systematic review (SR) was to assess whether tooth mobility (TM) increases the risk of tooth extraction/loss. The protocol was registered in PROSPERO database (CRD42023485425). The focused PECO questions were as follows: (1) "In patients with periodontitis, undergoing periodontal treatment, are teeth affected by mobility at higher risk of being extracted/lost compared to non-mobile teeth, with a minimum follow-up of 10 years?" and (2) "In these patients, does varying degrees of tooth mobility increase the risk of tooth extraction/loss, with a minimum follow-up of 10 years?". Results were reported according to PRISMA statement. Electronic and manual searches were conducted to identify longitudinal studies. The different assessments of tooth mobility were pooled into three groups: TM0: Undetectable tooth mobility, TM1: Horizontal/Mesio-distal mobility ≤1 mm, TM2: Horizontal/Mesio-distal mobility >1 mm or vertical tooth mobility. Tooth loss was the primary outcome. Various meta-analyses were conducted, including subgroup analyses considering different follow-up lengths and the timing of TM assessment, along with sensitivity analyses. A trial sequential analysis was also performed. Eleven studies were included (1883 patients). The mean follow-up range was 10-25 years. The weighted total of included teeth, based on the sample size, was 18 918, with a total of 1604 (8.47%) extracted/lost teeth. The overall rate of tooth extraction/loss increased with increasing mobility: TM0 was associated with a 5.85% rate (866/14822), TM1 with the 11.8% (384/3255), TM2 with the 40.3% (339/841). Mobile teeth (TM1/TM2) were at an increased risk for tooth extraction/loss, compared to TM0 (HR: 2.85; [95% CI 1.88-4.32]; p < .00001). TM1 had a higher risk than TM0 (HR: 1.96; [95% CI 1.09-3.53]; p < .00001). TM2 had a higher risk than TM1 (HR: 2.85; [95% CI 2.19-3.70]; p < .00001) and TM0 (HR: 7.12; [95% CI 3.27-15.51]; p < .00001). The results of the tests for subgroup differences were not significant. Sensitivity meta-analyses yielded consistent results with other meta-analyses. Within the limits of the quality of the studies included in the meta-analyses, mobile teeth were at higher risk of being extracted/lost in the long-term and higher degrees of TM significantly influenced clinicians' decision to extract a tooth. However, most teeth can be retained in the long-term and thus TM should not be considered a reason for extraction or a risk factor for tooth loss, regardless of the degree of TM.

The survival of periodontally treated molars in long-term maintenance: A systematic review and meta-analysis

AIM

This systematic review and meta-analysis aimed to determine the survival of periodontally treated molars during maintenance care and identify the risk factors associated with molar loss among patients with periodontitis who received professional periodontal therapy and maintenance.

MATERIALS AND METHODS

Longitudinal studies with a minimum follow-up duration of 5 years published until 28 August 2023 were retrieved from the following databases: the Cochrane Library, Embase, MEDLINE and Web of Science. All included studies reported data on molar retention. Meta-analysis was performed using Review Manager 5.4. A modified version of the Newcastle-Ottawa Scale was used to evaluate the study quality. Statistical results of analyses of the overall survival rate and molar loss are presented as estimated standardized mean differences, whereas the results of the analyses of risk factors are presented as risk ratios with 95% confidence intervals (95% CIs).

RESULTS

From among the 1323 potentially eligible reports, 41 studies (5584 patients, 29,908 molars retained at the beginning of maintenance therapy, mean follow-up duration of 14.7 years) were included. The pooled survival rate of the molars during maintenance therapy was 82% (95% CI: 80%-84%). The average loss of molars was 0.05 per patient per year (95% CI: 0.04-0.06) among the patients receiving long-term periodontal maintenance (PM) therapy. Fifteen factors were examined in this meta-analysis. Six patient-related factors (older age, lack of compliance, smoking, bruxism, diabetes and lack of private insurance) and five tooth-related factors (maxillary location, high probing pocket depth, furcation involvement, higher mobility and lack of pulpal vitality) were identified as risk factors for molar loss during maintenance therapy.

CONCLUSIONS

The findings of the present study suggest that the long-term retention of periodontally compromised molars can be achieved. The average number of molars lost per decade was <1 among the patients receiving long-term PM therapy. Older age, noncompliance, smoking, bruxism, diabetes, lack of private insurance coverage, maxillary location, furcation involvement, higher mobility, increase in the probing pocket depth and loss of pulpal vitality are strong risk factors for the long-term prognosis of molars.

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.

Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment

Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk of bias in prognostic prediction models of tooth loss that use machine learning. To do this, literature was searched in two electronic databases (MEDLINE via PubMed; Google Scholar) for studies that reported the accuracy or area under the curve (AUC) of prediction models. AUC measures the entire two-dimensional area underneath the entire receiver operating characteristic (ROC) curves. AUC provides an aggregate measure of performance across all possible classification thresholds. Although both development and validation were included in this review, studies that did not assess the accuracy or validation of boosting models (AdaBoosting, Gradient-boosting decision tree, XGBoost, LightGBM, CatBoost) were excluded. Five studies met criteria for inclusion and revealed high accuracy; however, models displayed a high risk of bias. Importantly, patient-level assessments combined with socioeconomic predictors performed better than clinical predictors alone. While there are current limitations, machine-learning-assisted models for tooth loss may enhance prognostication accuracy in combination with clinical and patient metadata in the future.

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.