A Two-Domain Self-Report Measure of Periodontal Disease Has Good Accuracy for Periodontitis Screening in Dental School Outpatients

Estimating Periodontitis Susceptibility Cases for Epidemiological Studies with Multiple Imputation

KNOWLEDGE TRANSFER STATEMENT

Our proposed estimate of periodontitis susceptibility cases addresses the issue of missing teeth, offering an innovative solution through a generative missing data imputation model. The implications of our findings extend to fostering more robust investigations into the relationships between periodontal health and systemic diseases, thereby offering valuable insights to clinicians for informed decision-making. Moreover, the study's capacity to shape clinical practices and interventions in public health will further fortify health policy strategies.

Data-driven prediction model for periodontal disease based on correlational feature analysis and clinical validation

Objectives

This study aimed to investigate the performance and reliability of data-driven models employing correlational feature analysis and clinical validation for predicting periodontal disease.

Methods

The 7th Korea National Health and Nutrition Examination Survey (n = 10,654) was used for correlation analysis to identify significant risk factors for periodontitis. Periodontal prediction models were developed with the selected factors and database, followed by internal validation with 5-fold cross-validation and 1000 bootstrap resampling. External validation was conducted with clinical data (n = 120) collected through self-reported questionnaires, clinical periodontal parameters, and radiographic image analysis. Predictive performance was assessed for logistics regression, support vector machine, random forest, XGBoost, and neural network algorithms using the area under the receiver operating characteristic curves (AUC) and other performance metrics.

Results

Correlation analysis identified 16 features from over 1000 potential risk factors for periodontitis. The best data-driven model (XGBoost) showed AUC values of 0.823 and 0.796 for internal and external validations, respectively. Modeling with clinical data revealed those same measures to be 0.836 and 0.649, respectively. In addition, the data-driven model could predict other clinical periodontal parameters including severe bone loss (AUC = 0.813), gingival bleeding (AUC = 0.694), and tooth loss (AUC = 0.734). A patient case study about prognostic predictions revealed that the probability of periodontitis can be reduced by 6.0 % (stop smoking) and 0.6 % (stop drinking) on average.

Conclusions

Data-driven models for predicting periodontitis and other periodontal parameters were developed from 16 risk factors, demonstrating enhanced prediction performance and reproducibility in internal-external validations.

Type 1 Diabetes Mellitus Patients' Self-perception of Periodontal Diseases

OBJECTIVES

 The study aimed to evaluate type 1 diabetes mellitus (T1DM) patients' self-perceived periodontal health status and to identify the association between periodontal disease (PD) and DM.

MATERIALS AND METHODS

 This cross-sectional study included 113 T1DM children between 3 and 18 years old from the Universiti Teknologi MARA and the University of Malaya. Periodontal health parameters, including plaque index, gingival index, probing pocket depth, simplified basic periodontal examination, and clinical attachment loss, were recorded. Self-perceived periodontal health status was assessed with questionnaires.

STATISTICAL ANALYSIS

 Statistical analysis was performed to evaluate the sensitivity of the questionnaire and the relationship between T1DM and periodontal parameters.

RESULTS

 The median age was 11.4 years. Half of them (50.4%) were females. A total of 83.5% rated their oral condition as good, whereas 27.5% reported a history of gingival bleeding. Clinical examination revealed that 48.7% had healthy gingiva, whereas 47.8% had gingivitis. The question "Do you have bleeding when brushing, flossing, or eating food?" showed good accuracy in the evaluation of PD (p < 0.001).

CONCLUSION

 The questionnaire has a high potential to be used by medical professionals in identifying T1DM patients at risk of PD to guide nondental health care providers in making appropriate referrals to dental services.

Putative Bidirectionality of Chronic Obstructive Pulmonary Disease and Periodontal Disease: A Review of the Literature

The prevalence of chronic obstructive pulmonary disease (COPD) is increasing worldwide and is currently the third leading cause of death globally. The long-term inhalation of toxic substances, mainly cigarette smoke, deteriorates pulmonary function over time, resulting in the development of COPD in adulthood. Periodontal disease is an inflammatory condition that affects most adults and is caused by the bacteria within dental plaque. These bacteria dissolve the gums around the teeth and the bone that supports them, ultimately leading to tooth loss. Periodontal disease and COPD share common risk factors, such as aging and smoking. Other similarities include local chronic inflammation and links with the onset and progression of systemic diseases such as ischemic heart disease and diabetes mellitus. Understanding whether interventions for periodontal disease improve the disease trajectory of COPD (and vice versa) is important, given our rapidly aging society. This review focuses on the putative relationship between COPD and periodontal disease while exploring current evidence and future research directions.

Optimizing a Diagnostic Model of Periodontitis by Using Targeted Proteomics

Periodontitis (PD), a widespread chronic infectious disease, compromises oral health and is associated with various systemic conditions and hematological alterations. Yet, to date, it is not clear whether serum protein profiling improves the assessment of PD. We collected general health data, performed dental examinations, and generated serum protein profiles using novel Proximity Extension Assay technology for 654 participants of the Bialystok PLUS study. To evaluate the incremental benefit of proteomics, we constructed two logistic regression models assessing the risk of having PD according to the CDC/AAP definition; the first one contained established PD predictors, and in addition, the second one was enhanced by extensive protein information. We then compared both models in terms of overall fit, discrimination, and calibration. For internal model validation, we performed bootstrap resampling (n = 2000). We identified 14 proteins, which improved the global fit and discrimination of a model of established PD risk factors, while maintaining reasonable calibration (area under the curve 0.82 vs 0.86; P < 0.001). Our results suggest that proteomic technologies offer an interesting advancement in the goal of finding easy-to-use and scalable diagnostic applications for PD that do not require direct examination of the periodontium.

Self-perception of periodontal health status among individuals with Fanconi anemia

INTRODUCTION

Fanconi anemia (FA) is a rare genetic disease characterized by congenital malformations and bone marrow failure. One of the most common oral diseases in individuals with FA is periodontitis and adequate self-perception of periodontal status could contribute to its prevention and early detection.

AIM

To compare oral health self-perception, measured by a questionnaire, with the clinical oral condition of patients with FA.

METHODS AND RESULTS

Fifty-six patients with FA, over 11 years of age, answered a questionnaire about dental history and self-reported oral health. Decayed, missing, and filled teeth (DMFT), Visible Plaque Index (VPI) and Gingival Bleeding Index (GBI) were measured. The median age of participants was 21 years (min 11, max 44), 31 (55%) were females and 25 (45%) males. Thirty-five (62.5%) participants rated their oral condition as satisfactory and 7 (12.5%) participants reported tooth mobility, 10 (17.9%) exposed roots and 21 (37.5%) gingival bleeding. Clinical examination detected average DMFT = 5.23, VPI = 31.36% and GBI = 33.77%. The gingival bleeding report was more frequent among individuals with higher GBI (p =  0.014). The DMFT was higher in those who had already undergone dental treatments (p =  0.031). There was an association between participants who presented dental caries and who rated their oral health as poor (p =  0.03). The question "Do your gums bleed easily?" had good accuracy in the evaluation of periodontal disease (p =  0.68).

CONCLUSION

Oral health self-perception of individuals with FA about gingival inflammation was associated with their gingival bleeding index.

Optimal Examination Sites for Periodontal Disease Evaluation: Applying the Item Response Theory Graded Response Model

Periodontal examination data have a complex structure. For epidemiological studies, mass screenings, and public health use, a simple index that represents the periodontal condition is necessary. Periodontal indices for partial examination of selected teeth have been developed. However, the selected teeth vary between indices, and a justification for the selection of examination teeth has not been presented. We applied a graded response model based on the item response theory to select optimal examination teeth and sites that represent periodontal conditions. Data were obtained from 254 patients who participated in a multicenter follow-up study. Baseline data were obtained from initial follow-up. Optimal examination sites were selected using item information calculated by graded response modeling. Twelve sites—maxillary 2nd premolar (palatal-medial), 1st premolar (palatal-distal), canine (palatal-medial), lateral incisor (palatal-central), central incisor (palatal-distal) and mandibular 1st premolar (lingual, medial)—were selected. Mean values for clinical attachment level, probing pocket depth, and bleeding on probing by full mouth examinations were used for objective variables. Measuring the clinical parameters of these sites can predict the results of full mouth examination. For calculating the periodontal index by partial oral examination, a justification for the selection of examination sites is essential. This study presents an evidence-based partial examination methodology and its modeling.

Validity of Self-Reported Periodontitis in Japanese Adults: The Japan Public Health Center-Based Prospective Study for the Next-Generation Oral Health Study

This study evaluated the validity of self-reported periodontitis measures among 2404 Japanese adults aged 40 to 75 years. A self-administered questionnaire survey and a clinical periodontal examination were conducted from 2013 through 2016. The self-reported periodontitis questions included 3 sociodemographic, 3 health, and 5 periodontal health-related items. Based on the clinical case definition of periodontitis, 26.5% of participants were found to be periodontally healthy, 2.7% had mild periodontitis, 55.2% moderate periodontitis, and 15.6% severe periodontitis. No single self-reported question demonstrated satisfactory validity in predicting the presence or absence of periodontitis. The predictive ability in mild and/or moderate periodontitis was poor even after combining multiple sociodemographic, health, and periodontal health-related questions. In severe periodontitis, the model including age, sex, education level, smoking status, diabetes history, body mass index, informed by a dentist, gingival bleeding, calculus deposit, and tooth mobility, presented moderate predictive performance (C-statistic: 0.676, sensitivity: 65.2%, and specificity: 61.1%). An age-stratified analysis on severe periodontitis showed that sensitivity was higher, and specificity was lower in older age group (60-75 years) than younger age group (40-59 years). Further refinement of questions in the self-report is required to increase the accuracy of the prediction of clinical periodontitis.

Examining Bias and Reporting in Oral Health Prediction Modeling Studies

Recent efforts to improve the reliability and efficiency of scientific research have caught the attention of researchers conducting prediction modeling studies (PMSs). Use of prediction models in oral health has become more common over the past decades for predicting the risk of diseases and treatment outcomes. Risk of bias and insufficient reporting present challenges to the reproducibility and implementation of these models. A recent tool for bias assessment and a reporting guideline—PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis)—have been proposed to guide researchers in the development and reporting of PMSs, but their application has been limited. Following the standards proposed in these tools and a systematic review approach, a literature search was carried out in PubMed to identify oral health PMSs published in dental, epidemiologic, and biostatistical journals. Risk of bias and transparency of reporting were assessed with PROBAST and TRIPOD. Among 2,881 papers identified, 34 studies containing 58 models were included. The most investigated outcomes were periodontal diseases (42%) and oral cancers (30%). Seventy-five percent of the studies were susceptible to at least 4 of 20 sources of bias, including measurement error in predictors ( n = 12) and/or outcome ( n = 7), omitting samples with missing data ( n = 10), selecting variables based on univariate analyses ( n = 9), overfitting ( n = 13), and lack of model performance assessment ( n = 24). Based on TRIPOD, at least 5 of 31 items were inadequately reported in 95% of the studies. These items included sampling approaches ( n = 15), participant eligibility criteria ( n = 6), and model-building procedures ( n = 16). There was a general lack of transparent reporting and identification of bias across the studies. Application of the recommendations proposed in PROBAST and TRIPOD can benefit future research and improve the reproducibility and applicability of prediction models in oral health.

A Self-Report to 2-Domain Questions May Accurately Screen for Periodontitis

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION

A two-domain self-report measure of periodontal disease has good accuracy for periodontitis screening in dental school outpatients. Chatzopoulos GS, Tsalikis L, Konstantinidis A, Kotsakis GA. J Periodontol 2016;87:1165-73.

SOURCE OF FUNDING

Information not available TYPE OF STUDY/DESIGN: Cohort study.