Variables affecting tooth survival and changes in probing depth: a long-term follow-up of periodontitis patients

Influence of residual pockets on periodontal tooth loss: A retrospective analysis

BACKGROUND

Individuals enrolled in supportive periodontal therapy (SPT) can still present with tooth loss due to periodontitis (TLP). There is limited evidence on the influence of residual pockets (RPc) and a defined "threshold" at which a patient's profile is set to be at high risk for TLP in the literature. Therefore, this study aimed to assess the influence of RPc on TLP and determine the prognostic performance of RPc compared to the staging and grading of periodontitis on TLP risk.

METHODS

Clinical data from 168 patients (3869 teeth) treated for periodontitis and receiving SPT for at least 10 years were evaluated in this retrospective study. TLP and the percentage of sites with RPc ≥ 5 mm or ≥6 mm per patient were collected. The prognostic performance of RPc was compared to the staging and grading of the disease on TLP using a multilevel Cox proportional hazard regression model.

RESULTS

Over a median follow-up of 25 years, 13.7% of teeth were lost, 4.6% of which were due to periodontitis. Most patients with TLP had ≥1 site with RPc ≥5 mm (90.8%) or ≥6 mm (77.6%). Multivariate multilevel Cox regression revealed that patients with >15% of sites with RPc ≥5 mm had a hazard ratio of 2.34, and grade C had a hazard ratio of 4.6 for TLP compared to RPc ≤4 mm/grade A. Grading exhibited the best discrimination and model fit.

CONCLUSION

Patients with RPc ≥5 mm at >15% of the sites are at risk for tooth loss. Grading and RPc ≥5 mm displayed very good predictive capability of TLP.

Modelling time-varying risk factors of tooth loss: Results from joint model compared with extended Cox regression model

AIM

To illustrate the use of joint models (JMs) for longitudinal and survival data in estimating risk factors of tooth loss as a function of time-varying endogenous periodontal biomarkers (probing pocket depth [PPD], alveolar bone loss [ABL] and mobility [MOB]).

MATERIALS AND METHODS

We used data from the Veterans Affairs Dental Longitudinal Study, a longitudinal cohort study of over 30 years of follow-up. We compared the results from the JM with those from the extended Cox regression model which assumes that the time-varying covariates are exogenous.

RESULTS

Our results showed that PPD is an important risk factor of tooth loss, but each model produced different estimates of the hazard. In the tooth-level analysis, based on the JM, the hazard of tooth loss increased by 4.57 (95% confidence interval [CI]: 2.13-8.50) times for a 1-mm increase in maximum PPD, whereas based on the extended Cox model, the hazard of tooth loss increased by 1.60 (95% CI: 1.37-1.87) times.

CONCLUSIONS

JMs can incorporate time-varying periodontal biomarkers to estimate the hazard of tooth loss. As JMs are not commonly used in oral health research, we provide a comprehensive set of R codes and an example dataset to implement the method.

Identifying predictors of tooth loss using a rule-based machine learning approach: A retrospective study at university-setting clinics

BACKGROUND

This study aimed to identify predictors associated with tooth loss in a large periodontitis patient cohort in the university setting using the machine learning approach.

METHODS

Information on periodontitis patients and 18 factors identified at the initial visit was extracted from electronic health records. A two-step machine learning pipeline was proposed to develop the tooth loss prediction model. The primary outcome is tooth loss count. The prediction model was built on significant factors (single or combination) selected by the RuleFit algorithm, and these factors were further adopted by the count regression model. Model performance was evaluated by root-mean-squared error (RMSE). Associations between predictors and tooth loss were also assessed by a classical statistical approach to validate the performance of the machine learning model.

RESULTS

In total, 7840 patients were included. The machine learning model predicting tooth loss count achieved RMSE of 2.71. Age, smoking, frequency of brushing, frequency of flossing, periodontal diagnosis, bleeding on probing percentage, number of missing teeth at baseline, and tooth mobility were associated with tooth loss in both machine learning and classical statistical models.

CONCLUSION

The two-step machine learning pipeline is feasible to predict tooth loss in periodontitis patients. Compared to classical statistical methods, this rule-based machine learning approach improves model explainability. However, the model's generalizability needs to be further validated by external datasets.

Preoperative evaluation of oral hygiene may predict the overall survival of patients with esophageal cancer

BACKGROUND

Previously, the association between tooth loss and prognosis after esophagectomy was reported; however, the presence of periodontal disease has not been assessed. This study investigated the association between the degree of oral hygiene, as evaluated by tooth loss and periodontal disease, and the prognosis of patients with esophageal cancer.

METHODS

A total of 163 esophageal cancer patients who underwent surgery with perioperative oral care and examination were enrolled. We assessed the periodontal pocket depth for the presence of periodontal disease and established a periodontal pocket index, defined as the sum of the periodontal pocket depth of the remaining tooth divided by the total count of the remaining teeth. Patients were divided into three groups: Group A (tooth loss < 13 and periodontal pocket index < 3.67); Group B (tooth loss < 13 and periodontal pocket index ≥ 3.67); and Group C (tooth loss ≥ 13). Overall survival and cancer-specific survival were analyzed, and a multivariate analysis was performed.

RESULTS

There was a significant difference in the 5-year overall survival rates between the groups (A:B:C = 74.8%:62.8%:50.5%; p = 0.0098), but not in the 5-year cancer-specific survival rates (A:B:C = 80.2%:64.2%:62.2%; p = 0.0849). In multivariate analysis, oral hygiene (tooth loss < 13 and periodontal pocket index ≥ 3.67 + tooth loss ≥ 13; p = 0.041) was a significant independent poor prognostic factor for overall survival.

CONCLUSIONS

Oral evaluation, focusing on tooth loss and periodontal disease, is meaningful in predicting the long-term prognosis of postoperative esophageal cancer patients.

Compromised Teeth Preserve or Extract: A Review of the Literature

Multiple systems and associated factors have been described in the literature to assess the prognosis of teeth with periodontal disease. Nowadays there is a tendency among clinicians to consider implants as the best solution after tooth extraction, in cases of teeth with a questionable prognosis. However, the value of the natural tooth must be considered, as the proprioception of the periodontal ligament is preserved, and it adapts to stress during functional loads. We first review the literature focusing on analyzing the factors that should guide decision-making to maintain or extract a tooth with a compromised periodontium. Then, we propose a schematic diagram of prognostic indicators to reflect the main factors to consider and the survival rate that each one represents when preserving or extracting a tooth.

Treatment of stage IV periodontitis: The EFP S3 level clinical practice guideline

BACKGROUND

The recently published clinical practice guideline (CPG) for the treatment of periodontitis in stages I-III provided evidence-based recommendations for the treatment of periodontitis patients, defined according to the 2018 classification. Stage IV periodontitis shares the severity and complexity characteristics of stage III periodontitis, but includes the anatomical and functional sequelae of tooth and periodontal attachment loss (tooth flaring and drifting, bite collapse, etc.), which require additional interventions following completion of active periodontal therapy.

AIM

To develop an S3 Level CPG for the treatment of stage IV periodontitis, focusing on the implementation of inter-disciplinary treatment approaches required to treat/rehabilitate patients following associated sequelae and tooth loss.

MATERIALS AND METHODS

This S3 Level CPG was developed by the European Federation of Periodontology (EFP), following methodological guidance from the Association of Scientific Medical Societies in Germany and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) process. A rigorous and transparent process included synthesis of relevant research in 13 specifically commissioned systematic reviews, evaluation of the quality and strength of evidence, the formulation of specific recommendations and a structured consensus process with leading experts and a broad base of stakeholders.

RESULTS

The S3 Level CPG for the treatment of stage IV periodontitis culminated in recommendations for different interventions, including orthodontic tooth movement, tooth splinting, occlusal adjustment, tooth- or implant-supported fixed or removable dental prostheses and supportive periodontal care. Prior to treatment planning, it is critically important to undertake a definitive and comprehensive diagnosis and case evaluation, obtain relevant patient information, and engage in frequent re-evaluations during and after treatment. The periodontal component of therapy should follow the CPG for the treatment of periodontitis in stages I-III.

CONCLUSIONS

The present S3 Level CPG informs clinical practice, health systems, policymakers and, indirectly, the public on the available and most effective modalities to treat patients with stage IV periodontitis and to maintain a healthy dentition over lifetime, according to the available evidence at the time of publication.

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.

Association, prediction, generalizability: Cross-center validity of predicting tooth loss in periodontitis patients

OBJECTIVES

To predict patients' tooth loss during supportive periodontal therapy across four German university centers.

METHODS

Tooth loss in 897 patients in four centers (Kiel (KI) n = 391; Greifswald (GW) n = 282; Heidelberg (HD) n = 175; Frankfurt/Main (F) n = 49) during supportive periodontal therapy (SPT) was assessed. Our outcome was annualized tooth loss per patient. Multivariable linear regression models were built on data of 75 % of patients from one center and used for predictions on the remaining 25 % of this center and 100 % of data from the other three centers. The prediction error was assessed as root-mean-squared-error (RMSE), i.e., the deviation of predicted from actually lost teeth per patient and year.

RESULTS

Annualized tooth loss/patient differed significantly between centers (between median 0.00 (interquartile interval: 0.00, 0.17) in GW and 0.09 (0.00, 0.19) in F, p = 0.001). Age, smoking status and number of teeth before SPT were significantly associated with tooth loss (p < 0.03). Prediction within centers showed RMSE of 0.14-0.30, and cross-center RMSE was 0.15-0.31. Predictions were more accurate in F and KI than in HD and GW, while the center on which the model was trained had a less consistent impact. No model showed useful predictive values.

CONCLUSION

While covariates were significantly associated with tooth loss in linear regression models, a clinically useful prediction was not possible with any of the models and generalizability was not given. Predictions were more accurate for certain centers.

CLINICAL RELEVANCE

Association should not be confused with predictive value: Despite significant associations of covariates with tooth loss, none of our models was useful for prediction. Usually, model accuracy was even lower when tested across centers, indicating low generalizability.

Systemic azithromycin as an adjunct to scaling and root planing in patients with stage III/IV periodontitis: 12-month results of a randomized controlled clinical trial

OBJECTIVES

To determine whether azithromycin (AZI) as an adjunct to scaling and root planing (SRP), when compared to placebo, decreases the number of sites demonstrating pocket depth (PD) ≥ 5 mm and bleeding on probing (BOP) 12 months post-treatment in stage III/IV periodontitis patients.

MATERIALS AND METHODS

In a double-blind randomized parallel-arm placebo-controlled trial, 40 stage III/IV periodontitis patients received steps 1 and 2 of periodontal treatment in two sessions within 7 days. Patients then received systemic antibiotic therapy (n = 20; AZI 500 mg/day, 3 days) or placebo (n = 20). Additional instrumentation of residual diseased sites (DS) - sites with PD ≥ 5 mm and BOP - was performed at the 3-, 6- and 9-month follow-ups. The primary outcome variable was the number of DS at the 12-month re-evaluation. Using a multivariate multilevel logistic regression model, the effects of gender, age, antibiotic therapy, presence of Porphyromonas gingivalis or Aggregatibacter actinomycetemcomitans, smoking, tooth being a molar and interdental location were evaluated.

RESULTS

The number of DS after 12 months was similar in the test (median (Me) = 4, interquartile range (IQR) = 0-6) and control (Me = 3, IQR = 1-6.5) groups. Both groups showed substantial but equivalent improvements in periodontal parameters, with no intergroup differences at initially shallow or deep sites. The logistic regression showed a lower odds ratio (OR) for the healing of DS on molars (OR = 0.29; p < 0.001) and in smokers (OR = 0.36; p = 0.048).

CONCLUSION

Stage III/IV periodontitis patients showed significant but comparable improvements in periodontal parameters and the number of residual DS at the 12-month revaluation regardless of treatment type. This may have been the result of the additional instrumentation received by patients at residual DS in both treatment groups.

CLINICAL RELEVANCE

Treatment with AZI + SRP provided no additional benefits after 12 months in terms of periodontal parameters or the number of persisting sites with PD ≥ 5 mm + BOP as compared to SRP plus placebo.

TRIAL REGISTRATION

EUDRA-CT: 2015-004306-42; https://www.clinicaltrialsregister.eu/ctr-search/trial/2015-004306-42/SI , registered 17. 12. 2015.

Influence of adjunctive azithromycin on microbiological and clinical outcomes in periodontitis patients: 6-month results of randomized controlled clinical trial

BACKGROUND

Our aim was to determine if azithromycin therapy, as an adjunct to scaling and root planing (SRP), decreases the number of pathobiontic subgingival plaque species and sites demonstrating pocket depth (PD) ≥ 5 mm and bleeding on probing (BOP) 6 months post-treatment.

METHODS

In a double-blind randomized parallel-arm placebo-controlled trial, 40 patients received nonsurgical periodontal treatment in two sessions within 7 days. Patients then received systemic antibiotic therapy (n = 20, azithromycin 500 mg/day for 3 days) or placebo (n = 20). Pooled microbiologic samples were taken before and 6 months after therapy and analysed by established culture methods. The primary outcome variable was the number of sites with PD ≥ 5 mm and BOP at the 6-month re-evaluation. Using multivariate multilevel logistic regression, the effects of gender, age, antibiotic therapy, presence of P. gingivalis or A. actinomycetemcomitans, smoking, tooth being a molar and interdental location were evaluated.

RESULTS

The number of sites with PD ≥ 5 mm and BOP after 6 months was similar in the test (Me = 4, IQR = 0-11) and control (Me = 5, IQR = 1-22) group. Adjunctive azithromycin treatment, compared to SRP alone, resulted in more frequent eradication of A. actinomycetemcomitans (p = 0.013) and C. rectus (p = 0.029), decreased proportion (p = 0.006) and total counts (p = 0.003) of P. gingivalis, and decreased proportion of C. rectus (p = 0.012). Both groups showed substantial but equivalent improvements in periodontal parameters, with no intergroups differences at initially shallow or deep sites. The logistic regression showed a lower odds ratio for healing of diseased sites on molars (OR = 0.51; p <  0,001).

CONCLUSION

Despite significant changes in numbers of A. actinomycetemcomitans, P. gingivalis and C. rectus, patients with periodontitis do not benefit from adjunctive systemic azithromycin in terms of number of persisting sites with PD ≥ 5 mm and BOP.

TRIAL REGISTRATION

EUDRA-CT: 2015-004306-42; https://www.clinicaltrialsregister.eu/ctr-search/trial/2015-004306-42/SI , registered 17. 12. 2015.

Probiotic strains of Lactobacillus brevis and Lactobacillus plantarum as adjunct to non-surgical periodontal therapy: 3-month results of a randomized controlled clinical trial

OBJECTIVES

To determine if periodontitis patients benefit from treatment with Lactobacillus brevis and Lactobacillus plantarum strains, applied into periodontal pockets as gel and thereafter taken as lozenges, as an adjunct to scaling and root planing (SRP).

MATERIALS AND METHODS

In a double-blind, randomized, placebo-controlled trial, 40 patients received scaling and root planing (SRP) in two sessions within 7 days. Patients then received either probiotic gel and lozenges (n = 20) or placebo (n = 20). The primary outcome variable was the number of diseased sites (DS: PD > 4 mm + BOP) at the 3-month re-evaluation. The effects of gender, age, probiotic therapy, presence of Porphyromonas gingivalis or Aggregatibacter actinomycetemcomitans, smoking, tooth being a molar and interdental location were evaluated using a multivariate multilevel logistic regression model.

RESULTS

The number of DS after 3 months was similar in the test (Me = 8, IQR = 5-11) and control (Me = 5, IQR = 1-10) groups. Both groups showed substantial but equivalent improvements in periodontal parameters. The logistic regression showed higher odds for the healing of gingival bleeding (OR = 2.12, p = 0.048) and lower odds for the healing of DS (OR = 0.51; p < 0.001) in the probiotic group.

CONCLUSIONS

Patients with periodontitis benefit from adjunctive use of probiotics containing L. brevis and L. plantarum in terms of reduction of gingival bleeding. However, adjunctive probiotics increase the number of persisting diseased sites with PD > 4 mm and BOP.

CLINICAL RELEVANCE

The adjunctive use of probiotics containing L. brevis and L. plantarum strains in treating chronic periodontitis results in a higher number of residual diseased sites when compared with SRP + placebo; its use is therefore unfounded.

Risk factors for recurrence of periodontal disease in patients in maintenance care in a private practice

AIM

To assess periodontal and dental conditions in individuals in maintenance care after periodontal therapy in private practice, and to identify risk factors for recurrence of disease and tooth loss.

MATERIALS AND METHODS

One hundred patients attending a routine recall visit were included. All had been treated for periodontal disease and were in maintenance since ≥ 2 years.

RESULTS

Examinations took place 18.0 (±8.71) years after the start of periodontal therapy. A total of 40.1 ± 22.5 recall visits were registered during this time. 91% of the participants had an initial diagnosis of chronic, 9% of aggressive periodontitis. The average participant was 46 years old and had 26 teeth. 283 of 2,549 initially present teeth were lost, half of them being molars. Periodontal and endo-periodontal complications accounted for only 16 lost teeth. The prevalence of all probing depth (PD) categories decreased significantly. The longer the time, the more frequent the recall visits, and the more was spent during the maintenance phase, the greater was the reduction. Multivariate analysis rendered BMI and smoking as factors influencing number of sites with PD ≥ 4 mm and bleeding on probing.

CONCLUSION

Tooth loss and periodontal tissue damage can be contained over prolonged periods if periodontal disease is treated and patients attend regular maintenance care.

Evaluating Modeling and Validation Strategies for Tooth Loss

Prediction models learn patterns from available data (training) and are then validated on new data (testing). Prediction modeling is increasingly common in dental research. We aimed to evaluate how different model development and validation steps affect the predictive performance of tooth loss prediction models of patients with periodontitis. Two independent cohorts (627 patients, 11,651 teeth) were followed over a mean ± SD 18.2 ± 5.6 y (Kiel cohort) and 6.6 ± 2.9 y (Greifswald cohort). Tooth loss and 10 patient- and tooth-level predictors were recorded. The impact of different model development and validation steps was evaluated: 1) model complexity (logistic regression, recursive partitioning, random forest, extreme gradient boosting), 2) sample size (full data set or 10%, 25%, or 75% of cases dropped at random), 3) prediction periods (maximum 10, 15, or 20 y or uncensored), and 4) validation schemes (internal or external by centers/time). Tooth loss was generally a rare event (880 teeth were lost). All models showed limited sensitivity but high specificity. Patients' age and tooth loss at baseline as well as probing pocket depths showed high variable importance. More complex models (random forest, extreme gradient boosting) had no consistent advantages over simpler ones (logistic regression, recursive partitioning). Internal validation (in sample) overestimated the predictive power (area under the curve up to 0.90), while external validation (out of sample) found lower areas under the curve (range 0.62 to 0.82). Reducing the sample size decreased the predictive power, particularly for more complex models. Censoring the prediction period had only limited impact. When the model was trained in one period and tested in another, model outcomes were similar to the base case, indicating temporal validation as a valid option. No model showed higher accuracy than the no-information rate. In conclusion, none of the developed models would be useful in a clinical setting, despite high accuracy. During modeling, rigorous development and external validation should be applied and reported accordingly.

Predictors for tooth loss in periodontitis patients: Systematic review and meta-analysis

AIM

A range of predictors for tooth loss in periodontitis patients have been reported. We performed a systematic review and meta-analysis to assess the consistency and magnitude of any association between a total of 12 predictors and tooth loss.

MATERIALS AND METHODS

Medline/Embase/Central were searched for longitudinal studies investigating the association between predictors and tooth loss in periodontitis patients. Random-effects meta-analysis was performed, and study quality assessed.

RESULTS

Twenty studies (15,422 patients, mean follow-up: 12 years) were included. The mean annual tooth loss/patient was 0.12 (min./max: 0.01/0.36). Older patients (n = 8 studies; OR: 1.05, 95% CI: 1.03-1.08/year), non-compliant ones (n = 11; 1.51, 1.06-2.16), diabetics (n = 7; 1.80, 1.26-2.57), those with IL-1-polymorphism (n = 3; 1.80; 1.29-2.52) and smokers (n = 15; 1.98, 1.58-2.48) had a significantly higher risk of tooth loss. Teeth with bone loss (n = 3; 1.04, 1.03-1.05/%), high probing pocket depth (n = 6; 3.19, 1.70-5.98), mobility (n = 4; 3.71, 1.65-8.38) and molars (n = 4; 4.22, 2.12-8.39), especially with furcation involvement (n = 5; 2.68, 1.75-4.08) also showed higher risks. Gender (n = 16; 0.95, 0.86-1.05) and endodontic affection (n = 3; 3.62, 0.99-13.2) were not significantly associated with tooth loss.

CONCLUSIONS

Older, non-compliant, smoking or diabetic patients, and teeth with bone loss, high probing pocket depth, mobility, or molars, especially with furcation involvement showed higher risks of tooth loss.

Risk factors for tooth loss in adult Japanese dental patients: 8020 Promotion Foundation Study

AIM

The assessment of the risk of tooth loss in patients visiting a dental clinic is important for managing their oral health. In the present study, we examined the risk factors for tooth loss among dental patients.

METHODS

Data from the 8020 Promotion Foundation Study on the Health Promotion Effects of Dental Care were used in the present study. The study involved 2743 patients who visited a dental clinic in Japan and completed a questionnaire and oral health examination at baseline and at the 2-year follow up. Tooth- and person-level risk factors for tooth loss during 2 years were subjected to a multilevel multivariate logistic regression analysis.

RESULTS

In both the person- and tooth-level analyses, age, smoking habit, reason for dental visit, economic status, number of remaining teeth, and periodontal status were significantly associated with tooth loss. In the tooth-level analysis, tooth type, tooth status, and periodontal status were significantly related to tooth loss. Persons who visited a dental clinic for periodic maintenance had a significantly lower risk of tooth loss than those who visited only to receive dental treatment.

CONCLUSIONS

Various tooth-level factors and modifiable factors, such as smoking cessation and periodic maintenance, are important for the suppression of tooth loss in dental patients.

Predictive factors for tooth loss during supportive periodontal therapy in patients with severe periodontitis: a Japanese multicenter study

Background

Supportive periodontal therapy (SPT) must take individual patient risk factors into account. We conducted a multicenter joint retrospective cohort study to investigate the value of modified periodontal risk assessment (MPRA) and therapy-resistant periodontitis (TRP) assessment as predictive factors for tooth loss due to periodontal disease in patients with severe periodontitis during SPT.

Methods

The subjects were 82 patients from 11 dental institutions who were diagnosed with severe periodontitis and continued SPT for at least 1 year (mean follow-up = 4.9 years) between 1981 and 2008. The outcome was tooth loss due to periodontal disease during SPT. The Cox proportional hazards model was used to analyze sex, age, diabetes status, smoking history, number of periodontal pockets measuring ≥6 mm, rate of bleeding on probing, bone loss/age ratio, number of teeth lost, MPRA, and TRP assessment as explanatory variables.

Results

Univariate analysis showed that loss of ≥8 teeth by the start of SPT [hazard ratio (HR) 2.86], MPRA score indicating moderate risk (HR 8.73) or high risk (HR 11.04), and TRP assessment as poor responsiveness to treatment (HR 2.79) were significantly associated with tooth loss (p < 0.05). In a model in which the explanatory variables of an association that was statistically significant were added simultaneously, the HR for poor responsiveness to treatment and ≥8 teeth lost was significant at 20.17 compared with patients whose TRP assessment indicated that they responded favorably to treatment and who had lost <8 teeth by the start of SPT.

Conclusion

MPRA and TRP assessment may be useful predictive factors for tooth loss due to periodontal disease during SPT in Japanese patients with severe periodontitis. Additionally, considering the number of teeth lost by the start of SPT in TRP assessment may improve its predictive accuracy.

Tooth loss is a complex measure of oral disease: Determinants and methodological considerations

OBJECTIVES

Counts of missing teeth or measures of incident tooth loss are gaining attention as a simple way to measure dental status in large population studies. We explore the meaning of these metrics and how missing teeth might influence other measures of dental status.

METHODS

An observational study was performed in 2 contrasting adult populations. In total, 62 522 adult participants were available with clinically assessed caries and periodontal indices from the Swedish arm of the Gene-Lifestyle Interactions and Dental Endpoints Study (GLIDE) and the Korea National Health and Nutrition Examination Survey (KNHANES) in the Republic of Korea. Longitudinal measures of tooth loss were available for 28 244 participants in GLIDE with median follow-up of 10.6 years.

RESULTS

In longitudinal analysis, hazard for tooth loss was associated with baseline dental status (previous tooth loss, periodontal status and caries status) and socio-demographic variables (age, smoking status and highest educational level). Analysis of cross-sectional data suggested that indices of caries exposure were not independent of periodontal status. The strength and direction of association varied between groups, even for measures specifically intended to avoid measuring tooth loss. Individuals with impaired periodontal health (community periodontal index [CPI] 3 or higher in any sextant) had higher standardized decayed and filled surfaces (DFS; number of DFS divided by total number of tooth surfaces) in GLIDE (incidence risk ratio [IRR] 1.05 [95% CI: 1.04, 1.07], but lower standardized DFS in KNHANES (IRR: 0.95 [0.92, 0.98]) than individuals with better periodontal health (CPI <3 in all sextants).

CONCLUSIONS

Incident tooth loss is a complex measure of dental disease, with multiple determinants. The relative importance of dental caries and periodontal disease as drivers of tooth loss differs between age groups. Measures of dental caries exposure are associated with periodontal status in the studied populations, and these associations can be population-specific. Consideration of the study-specific properties of these metrics may be required for valid inference in large population studies.

Tooth loss after periodontal treatment-Mining an insurance database

OBJECTIVES

The aim of this study was to evaluate tooth loss after periodontal treatment.

METHODS

The data was collected from the digital database of a major German national health insurance company. Periodontal treatment was the intervention in the treatment group. Kaplan-Meier survival analyses on the patient level with the primary outcome extraction were carried out over four years. A control group without treatment was matched and analysed. Differences were tested with the Log-Rank-test. Extraction incidences were calculated over a matched observation period six years before and four years after treatment for both treatment and control group.

RESULTS

A total of 415,718 periodontal treatments could be traced. Focussing on the outcome "extraction", the cumulative four-year survival rate was 63.8% after periodontal treatment. The matched control group without periodontal treatment showed a survival rate of 72.5%. These differences were significant (p < 0.0001). The extraction incidence over time was higher in a four-year period after periodontal treatment compared to a six-year period before periodontal treatment.

CONCLUSIONS

The outcome of periodontal treatment was acceptable. In about two thirds of the patients, extractions could be completely avoided within a four year period after treatment.

CLINICAL SIGNIFICANCE STATEMENT

This study within the German national health insurance system shows that extractions were not observed after periodontal treatment in the majority of cases. Although periodontitis is a chronic disease, patients suffering from periodontitis have a considerable chance to prevent further tooth loss.

Site-specific treatment outcome in smokers following 12 months of supportive periodontal therapy

Aim

To evaluate the effect of cigarette smoking on periodontal health at patient, tooth, and site levels following supportive therapy.

Materials and Methods

Eighty chronic periodontitis patients, 40 smokers and 40 non‐smokers, were recruited to a single‐arm clinical trial. Periodontal examinations were performed at baseline (T0), 3 months following active periodontal therapy (T1), and 12 months following supportive periodontal therapy (T2). Smoking status was validated measuring serum cotinine levels. Probing depth (PD) ≥ 5 mm with bleeding on probing (BoP) was defined as the primary outcome. Logistic regression analyses adjusted for clustered observations of patients, teeth, and sites and mixed effects models were employed to analyse the data.

Results

All clinical parameters improved from T0 to T2 (p < 0.001), whereas PD, bleeding index (BI), and plaque index (PI) increased from T1 to T2 in smokers and non‐smokers (p < 0.001). An overall negative effect of smoking was revealed at T2 (OR = 2.78, CI: 1.49, 5.18, p < 0.001), with the most pronounced effect at maxillary single‐rooted teeth (OR = 5.08, CI: 2.01, 12.78, p < 0.001). At the patient level, less variation in treatment outcome was detected within smokers (ICC = 0.137) compared with non‐smokers (ICC = 0.051).

Conclusion

Smoking has a negative effect on periodontal health following 12 months of supportive therapy, in particular at maxillary single‐rooted teeth.

Microbial profiles at baseline and not the use of antibiotics determine the clinical outcome of the treatment of chronic periodontitis

Antibiotics are often used in the treatment of chronic periodontitis, which is a major cause of tooth loss. However, evidence in favour of a microbial indication for the prescription of antibiotics is lacking, which may increase the risk of the possible indiscriminate use of antibiotics, and consequent, microbial resistance. Here, using an open-ended technique, we report the changes in the subgingival microbiome up to one year post-treatment of patients treated with basic periodontal therapy with or without antibiotics. Antibiotics resulted in a greater influence on the microbiome 3 months after therapy, but this difference disappeared at 6 months. Greater microbial diversity, specific taxa and certain microbial co-occurrences at baseline and not the use of antibiotics predicted better clinical treatment outcomes. Our results demonstrate the predictive value of specific subgingival bacterial profiles for the decision to prescribe antibiotics in the treatment of periodontitis, but they also indicate the need for alternative therapies based on ecological approaches.

Predictors of tooth loss due to periodontal disease in patients following long-term periodontal maintenance

AIM

To analyse patient-related factors (PRFs) and tooth-related factors (TRFs) associated with tooth loss due to periodontal disease (TLPD) in patients undergoing periodontal maintenance (PM).

MATERIAL AND METHODS

The sample consisted of 500 patients (mean follow-up of 20 years). The impact of PRFs on TLPD was analysed with Poisson regression and multivariate logistic regression. The simultaneous impact of PRFs and TRFs was analysed with multilevel logistic regression and Cox regression.

RESULTS

Tooth loss due to periodontal disease was 515 (mean 0.05 patient/year). The significant PRFs were severe periodontitis (p < 0.001), aggressive periodontitis (p < 0.001), smoking (p = 0.018), bruxism (p = 0.022) and baseline number of teeth (p = 0.001). These PRFs allowed characterizing patients losing more teeth. The whole TRFs analysed were significant, depending on the type of tooth and the category of each factor (e.g. mobility 0, 1, 2, and 3). The significant PRFs increased the risk of TLPD by 2 to 3 times while TRFs increased the risk to a higher extent. Mobility was the main TRF.

CONCLUSIONS

Severe periodontitis, aggressive periodontitis, smoking, bruxism and baseline number of teeth, as well as the whole TRFs analysed, were associated with TLPD.