Self-reported questionnaire for surveillance of periodontitis in Chinese patients from a prosthodontic clinic: a validation study

Development of Risk Prediction Models for Severe Periodontitis in a Thai Population: Statistical and Machine Learning Approaches

BACKGROUND

Severe periodontitis affects 26% of Thai adults and 11.2% of adults globally and is characterized by the loss of alveolar bone height. Full-mouth examination by periodontal probing is the gold standard for diagnosis but is time- and resource-intensive. A screening model to identify those at high risk of severe periodontitis would offer a targeted approach and aid in reducing the workload for dentists. While statistical modelling by a logistic regression is commonly applied, optimal performance depends on feature selections and engineering. Machine learning has been recently gaining favor given its potential discriminatory power and ability to deal with multiway interactions without the requirements of linear assumptions.

OBJECTIVE

We aim to compare the performance of screening models developed using statistical and machine learning approaches for the risk prediction of severe periodontitis.

METHODS

This study used data from the prospective Electricity Generating Authority of Thailand cohort. Dental examinations were performed for the 2008 and 2013 surveys. Oral examinations (ie, number of teeth and oral hygiene index and plaque scores), periodontal pocket depth, and gingival recession were performed by dentists. The outcome of interest was severe periodontitis diagnosed by the Centre for Disease Control-American Academy of Periodontology, defined as 2 or more interproximal sites with a clinical attachment level ≥6 mm (on different teeth) and 1 or more interproximal sites with a periodontal pocket depth ≥5 mm. Risk prediction models were developed using mixed-effects logistic regression (MELR), recurrent neural network, mixed-effects support vector machine, and mixed-effects decision tree models. A total of 21 features were considered as predictive features, including 4 demographic characteristics, 2 physical examinations, 4 underlying diseases, 1 medication, 2 risk behaviors, 2 oral features, and 6 laboratory features.

RESULTS

A total of 3883 observations from 2086 participants were split into development (n=3112, 80.1%) and validation (n=771, 19.9%) sets with prevalences of periodontitis of 34.4% (n=1070) and 34.1% (n=263), respectively. The final MELR model contained 6 features (gender, education, smoking, diabetes mellitus, number of teeth, and plaque score) with an area under the curve (AUC) of 0.983 (95% CI 0.977-0.989) and positive likelihood ratio (LR+) of 11.9 (95% CI 8.8-16.3). Machine learning yielded lower performance than the MELR model, with AUC (95% CI) and LR+ (95% CI) values of 0.712 (0.669-0.754) and 2.1 (1.8-2.6), respectively, for the recurrent neural network model; 0.698 (0.681-0.734) and 2.1 (1.7-2.6), respectively, for the mixed-effects support vector machine model; and 0.662 (0.621-0.702) and 2.4 (1.9-3.0), respectively, for the mixed-effects decision tree model.

CONCLUSIONS

The MELR model might be more useful than machine learning for large-scale screening to identify those at high risk of severe periodontitis for periodontal evaluation. External validation using data from other centers is required to evaluate the generalizability of the model.

Validity of Simple Self-Reported Periodontal Status Questions

OBJECTIVE

The aim of this research was to evaluate the validity of the questions developed by Thai periodontists on self-reported periodontal status and symptoms in identifying severe periodontitis amongst adults in Thailand.

METHODS

Registered Electricity Generating Authority of Thailand (EGAT) employees completed medical examinations and full-mouth periodontal examinations. They also were interviewed using a self-reported questionnaire that was developed by Thai periodontists. The questions pertained to their periodontal status and symptoms comprising swollen gums, bad breath, loose teeth, bleeding on brushing, painful gums, and pus or abscesses. The participants were categorised as having nonsevere and severe periodontitis according to the Centers for Disease Control and Prevention in partnership with the American Academy of Periodontology (CDC/AAP) case definitions. Self-reported periodontal status and symptom results were compared with actual periodontal status. The area under the receiver operating characteristic curve (AUROCC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated.

RESULTS

A total of 1393 EGAT employees participated in this study. The questions on self-reported periodontal status and all symptoms poorly identified patients with severe periodontitis, with an AUROCC of 0.52 to 0.60. The sensitivity was 5.0% to 40.2%; however, the specificity was good, at 73.2% to 99.1%. The validity of the self-reported questions was comparable amongst the sex, education, and income subgroups.

CONCLUSIONS

This study demonstrated that self-reported periodontal status and symptoms were inadequate in identifying patients with severe periodontal disease.

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.

Periodontal Disease and Tooth Loss Are Associated with Lung Cancer Risk

Background

The associations between periodontal disease, tooth loss, and lung cancer risk remain debatable. Therefore, the purpose of the present study is to evaluate whether periodontal disease and tooth loss are associated with lung cancer risk.

Methods

A literature search was performed for relevant studies using PubMed and Embase databases. Risk ratio (RR) with 95% confidence interval (CI) was applied as effect size to summarize the associations between periodontal disease, tooth loss, and lung cancer risk. A further dose-response analysis was also performed.

Results

A total of twelve studies comprising 263,238 participants were included. The results indicated that periodontal disease was positively associated with lung cancer risk (RR = 1.37, 95%CI = 1.16‐1.63). There was a positive association between tooth loss and lung cancer risk (RR = 1.69, 95%CI = 1.46‐1.96). Moreover, there was a significantly linear dose-response relationship between tooth loss and lung cancer risk, and every 5 increment in tooth loss was associated with 10% increased lung cancer risk. Similar results were obtained in subgroup analysis.

Conclusions

Periodontal disease and tooth loss are increased risk factors for lung cancer. Prevention and treatment of periodontal disease may be effective potential prevention strategies for lung cancer.

Self-reported and clinical periodontal conditions in a group of Eastern European postpartum women

Periodontitis is a highly prevalent condition leading to a continuous destruction of tooth-supporting tissues. It increases the risk for various systemic diseases and adverse pregnancy outcomes. Therefore, screening for periodontitis is important. Screening measures can range from self-reported symptoms to clinical full-mouth periodontal examination. The hypothesis of our study was that self-reported parameters and clinical definition perform equally well in identifying periodontitis patients. The aim of this study was to develop, validate its internal consistency, and evaluate a self-reported instrument against periodontal clinical evaluation for diagnosis of periodontitis in a group of postpartum women, as well as to describe their periodontal status and the risk factors associated with periodontal disease. A cross-sectional study on postpartum women was conducted in a tertiary university hospital, from April 2018 to March 2019. Sociodemographic and behavioral data, periodontal clinical parameters, and self-reported periodontal perception were collected. A 16-item questionnaire was developed to obtain information about perceived periodontal alterations and oral hygiene habits. The utility of the questionnaire was tested against a periodontal diagnosis based on a full-mouth periodontal examination. The questionnaire was applied in 215 postpartum women aged 29.16±5.54 years (mean age (y) ± standard deviation) having the following periodontal status: 16 individuals without periodontal disease (7.44%), 32 individuals with gingivitis (14.88%), 19 individuals with mild periodontitis (8.84%), 132 individuals with moderate periodontitis (61.39%), and 16 individuals with severe periodontitis (7.44%). A significant association was observed between oral hygiene score, smoking status, and periodontal conditions (p<0.05). A significant association between the self-reported items related to “gum swelling”, “halitosis”, “previous periodontal diagnosis” and “previous periodontal treatment” with clinical periodontitis have been identified (p<0.05). Using self-reported questionnaires for detection of periodontal disease was ineffective in our studied population, since self-reported parameters and clinical definition do not appear to perform equally in identifying periodontitis cases. Clinical periodontal examination remains the gold standard for screening. Periodontitis was frequent in our group and the severity was significantly associated with the oral hygiene score and smoking. These results underline the necessity for periodontal clinical examination during pregnancy.

Evaluation of a New Self-Reported Tool for Periodontitis Screening

INTRODUCTION

Periodontitis is still highly prevalent in industrial population whereas at the same time appropriate screening programs are missing.

AIM

To evaluate, a self-reported questionnaire about periodontal risk factors in combination with the Periodontal Screening Index (PSI) to identify an existing need for periodontal treatment combined with the early recognition of high-risk patients.

MATERIALS AND METHODS

Total 200 patients took part in the questionnaire based study and were examined using the PSI. Thereafter the participants were divided into two groups, subjects with periodontitis (Group 1; PSI 0-2) and subjects without periodontitis (Group 2; PSI 3-4). The answers were evaluated using a point system ranging from 0 to 8, based on known periodontal risk factors and their assumed degree of influence. Receiver-Operating Characteristic (ROC) curve analysis were applied to examine the overall discriminatory power, sensitivity, specificity and corresponding cut-off points of the self-reported periodontal disease scale.

RESULTS

There was a significant difference between Group 1 and 2 concerning the majority of the inquired items (12 of 16, p<0.05). The distribution of the individual total score exhibited a high statistical significance (p<0.001) of robustness in terms of differing definitions of periodontitis. The Area Under the Curve (AUC) was 0.912 with a sensitivity of 86% and a specificity of 76%.

CONCLUSION

The questionnaire produced a reliable assessment of the individual risk (total score) and the need for periodontal treatment as well as the differentiation between gingivitis and periodontitis.

CLINICAL RELEVANCE

Patient-based data (clinical variables and periodontal risk factors of periodontitis) were adequate to make a preliminary assessment of a possible need for periodontal treatment.

Development and validation of a self-reported periodontal disease measure among Jordanians

BACKGROUND

The development of self-reported measures of periodontal disease would be of great benefit to facilitate epidemiological studies of periodontal disease on a larger scale, and to allow for surveillance of the periodontal condition of populations over time.

OBJECTIVES

To develop a culturally adapted self-reported measure of periodontal disease, test its predictive and discriminative validity and establish a cut-off value for this measure to diagnose periodontal disease.

METHODS

A total of 288 Jordanian adults completed the questionnaire assessing self-reported periodontal health (18 questions) and underwent periodontal examination. Of the 18 questions, six were significantly associated with at least one clinical definition of periodontitis and were used to constitute the self-reported periodontal disease measure. Receiver-operating characteristics (ROC) curve analyses were used to examine the overall discriminatory power, sensitivity and specificity, and corresponding cut-off points of the self-reported periodontal disease measure.

RESULTS

ROC analysis showed that the self-reported periodontal disease measure had an excellent performance to discriminate between those with and without periodontal disease, regardless of the clinical definition used. A score of 2, on a scale of 0 to 6, had the highest sensitivity and specificity to detect periodontal disease when defined by all study criteria. Significant associations were observed between self-reported periodontal disease measures and all clinical definitions in the regression analysis (the odds ratio ranged from 8.31 to 18.96), according to the clinical definition to be predicted.

CONCLUSION

Self-reported periodontal disease measures have excellent predictive and discriminative validity when tested against clinical definitions, and severity and extent of periodontal disease.

Impact of periodontal disease on outcomes in diabetes

INTRODUCTION

The prevalence of periodontal disease (POD) among adults aged 30years and older in the United States is reported to be more than 47%, with higher prevalence seen among patients with diabetes mellitus (DM). POD has been associated with systemic inflammation, a known risk factor for cardiovascular and bone disease, both of which are more common in patients with DM. However, there is mixed evidence that treatment of POD reduces inflammation, improves DM control, and reduces DM complications. Our study objectives are to assess factors associated with POD in patients with DM and determine the impact of POD treatment on inflammation and bone turnover biomarkers associated with complications of DM.

METHODS

In this pilot study, we will first recruit 200 patients with DM to complete a 48-item investigator-administered questionnaire designed to assess socio-economic status, oral health status, adequacy of oral care, glycemic control and presence of DM complications. Responses will be verified by individual chart review. Then, using a crossover design, a subgroup of 24 subjects with responses suggestive of POD will be assigned to undergo POD treatment for three months followed by three months of routine dental care (group 1) or be followed for three months during routine dental care then receive POD treatment for three months (group 2). Outcome measures will be collected before and after POD treatment and include glycemic control and inflammatory and bone turnover biomarkers.

RESULTS

We hypothesize that the prevalence of POD among DM patients will be associated with inadequate glycemic control and greater DM complications.