Accuracy of a 7-Item Patient-Reported Stand-Alone Tool for Periodontitis Screening

Screening, Referral, Behavioral Counseling, and Preventive Interventions for Oral Health in Adults: A Systematic Review for the US Preventive Services Task Force

Importance

Dental caries and periodontal disease are common adult oral health conditions and potentially amenable to primary care screening and prevention.

Objective

To systematically review the evidence on primary care screening and prevention of dental caries and periodontal disease in adults to inform the US Preventive Services Task Force.

Data Sources

MEDLINE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews (to October 3, 2022); surveillance through July 21, 2023.

Study Selection

Diagnostic accuracy studies of primary care screening instruments and oral examination; randomized and nonrandomized trials of screening and preventive interventions; cohort studies on primary care oral health screening and preventive intervention harms.

Data Extraction and Synthesis

One investigator abstracted data; a second checked accuracy. Two investigators independently rated study quality. Diagnostic accuracy data were pooled using a bivariate mixed-effects binary regression model.

Main Outcomes and Measures

Dental caries, periodontal disease, morbidity, quality of life, harms; and diagnostic test accuracy.

Results

Five randomized clinical trials, 5 nonrandomized trials, and 6 observational studies (total 3300 participants) were included. One poor-quality trial (n = 477) found no difference between oral health screening during pregnancy vs no screening in caries, periodontal disease, or birth outcomes. One study (n = 86) found oral health examination by 2 primary care clinicians associated with low sensitivity (0.42 and 0.56) and high specificity (0.84 and 0.87) for periodontal disease and with variable sensitivity (0.33 and 0.83) and high specificity (0.80 and 0.93) for dental caries. Four studies (n = 965) found screening questionnaires associated with a pooled sensitivity of 0.72 (95% CI, 0.57-0.83) and specificity of 0.74 (95% CI, 0.66-0.82) for periodontal disease. For preventive interventions no study evaluated primary care counseling or dental referral, and evidence from 2 poor-quality trials (n = 178) of sealants, and 1 fair-quality and 4 poor-quality trials (n = 971) of topical fluorides, was insufficient. Three fair-quality trials (n = 590) of persons with mean age 72 to 80 years found silver diamine fluoride solution associated with fewer new root caries lesions or fillings vs placebo (mean reduction, -0.33 to -1.3) and decreased likelihood of new root caries lesion (2 trials; adjusted odds ratio, 0.4 [95% CI, 0.3-0.7]). No trial evaluated primary care-administered preventive interventions.

Conclusions and Relevance

Screening questionnaires were associated with moderate diagnostic accuracy for periodontal disease. Research is needed to determine benefits and harms of oral health primary care screening and preventive interventions.

Suitability of machine learning models for prediction of clinically defined Stage III/IV periodontitis from questionnaires and demographic data in Danish cohorts

AIM

To evaluate if, and to what extent, machine learning models can capture clinically defined Stage III/IV periodontitis from self-report questionnaires and demographic data.

MATERIALS AND METHODS

Self-reported measures of periodontitis, demographic data and clinically established Stage III/IV periodontitis status were extracted from two Danish population-based cohorts (The Copenhagen Aging and Midlife Biobank [CAMB] and The Danish Health Examination Survey [DANHES]) and used to develop cross-validated machine learning models for the prediction of clinically established Stage III/IV periodontitis. Models were trained using 10-fold cross-validations repeated three times on the CAMB dataset (n = 1476), and the resulting models were validated in the DANHES dataset (n = 3585).

RESULTS

The prevalence of Stage III/IV periodontitis was 23.2% (n = 342) in the CAMB dataset and 9.3% (n = 335) in the DANHES dataset. For the prediction of clinically established Stage III/IV periodontitis in the CAMB cohort, models reached area under the receiver operating characteristics (AUROCs) of 0.67-0.69, sensitivities of 0.58-0.64 and specificities of 0.71-0.80. In the DANHES cohort, models derived from the CAMB cohort achieved AUROCs of 0.64-0.70, sensitivities of 0.44-0.63 and specificities of 0.75-0.84.

CONCLUSIONS

Applying cross-validated machine learning algorithms to demographic data and self-reported measures of periodontitis resulted in models with modest capabilities for the prediction of Stage III/IV periodontitis in two Danish cohorts.

Association between Periodontal Treatment and Healthcare Costs in Patients with Coronary Heart Disease: A Cohort Study Based on German Claims Data

There is empirical evidence of an association between periodontitis and coronary heart disease (CHD). However, it is uncertain whether periodontal treatment in CHD patients might lead to reduced healthcare costs. This study aims to assess the association between periodontal treatment and healthcare costs in newly diagnosed CHD patients. Data from 21,263 adults who were continuously insured between 2011 and 2016 and who were newly diagnosed with CHD in 2013 were selected from a German claims database. The study population was differentiated by the utilization of periodontal treatment. The average treatment effect (ATE) of periodontal treatment on healthcare costs (total, inpatient, outpatient, drugs) was investigated using weighted Poisson regression models conditional on covariates and is shown as a ratio (of geometric means). Periodontal treatment was documented for 4.7% of the persons in the study population. Newly diagnosed CHD patients showed an ATE of 0.98 for total healthcare cost (95% CI 0.90–1.06), 0.79 for inpatient costs (95% CI 0.61–1.04), and 0.95 for drug costs (95% CI 0.87–1.04). A statistically significant 7% increase in outpatient costs was shown (95% CI 1.01–1.13). Despite a lack of statistical significance in most cases, the study provides evidence of a meaningful decrease in inpatient costs after periodontal treatment. Further studies are needed.

External validation of a rapid, non-invasive tool for periodontitis screening in a medical care setting

OBJECTIVES

Medical professionals should advise their patients to visit a dentist if necessary. Due to the lack of time and knowledge, screening for periodontitis is often not done. To alleviate this problem, a screening model for total (own teeth/gum health, gum treatment, loose teeth, mouthwash use, and age)/severe periodontitis (gum treatment, loose teeth, tooth appearance, mouthwash use, age, and sex) in a medical care setting was developed in the Academic Center of Dentistry Amsterdam (ACTA) [1]. The purpose of the present study was to externally validate this tool in an outpatient medical setting.

MATERIALS AND METHODS

Patients were requited in an outpatient medical setting as the validation cohort. The self-reported oral health questionnaire was conducted, demographic data were collected, and periodontal examination was performed. Algorithm discrimination was expressed as the area under the receiver operating characteristic curve (AUROCC). Sensitivity, specificity, and positive and negative predictive values were calculated. Calibration plots were made.

RESULTS

For predicting total periodontitis, the AUROCC was 0.59 with a sensitivity of 49% and specificity of 68%. The PPV was 57% and the NPV scored 55%. For predicting severe periodontitis, the AUROCC was 0.73 with a sensitivity of 71% and specificity of 63%. The PPV was 39% and the NPV 87%.

CONCLUSIONS

The performance of the algorithm for severe periodontitis is found to be sufficient in the current medical study population. Further external validation of periodontitis algorithms in non-dental school populations is recommended.

CLINICAL RELEVANCE

Because general physicians are obligated to screen patients for periodontitis, it is our general goal that they can use a prediction model in medical settings without an oral examination.

An aMMP-8 Point-of-Care and Questionnaire Based Real-Time Diagnostic Toolkit for Medical Practitioners

The aim of this cross-sectional study is to propose an efficient strategy based on biomarkers adjunct with an interview/questionnaire covering risk factors for periodontitis for the identification of undiagnosed periodontitis by medical professionals. Active matrix metalloproteinase (aMMP)-8 levels in mouthrinse were analyzed by a point-of-care (PoC)/chairside lateral-flow immunotest, and salivary total MMP-8, total MMP-9 and calprotectin levels were analyzed by enzyme-linked immunosorbent assays (ELISAs) and active MMP-9 by gelatin zymography for 149 Greek patients. Patients underwent a full-mouth oral health examination for diagnosis according to the 2018 classification system of periodontal diseases. In addition, patient characteristics (risk factors: age, gender, education level, smoking and body mass index) were recorded. Receiver operating curve (ROC) analysis indicated better diagnostic precision to identify undiagnosed periodontitis for oral fluid biomarkers in adjunct with an interview/questionnaire compared with a plain questionnaire (i.e., risk factors): aMMP-8 AUC (95% confidence interval) = 0.834 (0.761-0.906), total MMP-8 = 0.800 (0.722-0.878), active MMP-9 = 0.787 (0.704-0.870), total MMP-9 = 0.773 (0.687-0.858) and calprotectin = 0.773 (0.687-0.858) vs. questionnaire = 0.764 (0.676-0.851). The findings of this study suggest that oral fluid biomarker analysis, such as a rapid aMMP-8 PoC immunotest, could be used as an adjunct to an interview/questionnaire to improve the precision of timely identification of asymptomatic, undiagnosed periodontitis patients by medical professionals. This strategy appears to be viable for referring patients to a dentist for diagnosis and treatment need assessment.