A rapid, non-invasive tool for periodontitis screening in a medical care setting

Abstract

Background Since periodontitis is bi-directionally associated with several systemic diseases, such as diabetes mellitus and cardiovascular diseases, it is important for medical professionals in a non-dental setting to be able examine their patients for symptoms of periodontitis, and urge them to visit a dentist if necessary. However, they often lack the time, knowledge and resources to do so. We aim to develop and assess “quick and easy” screening tools for periodontitis, based on self-reported oral health (SROH), demographics and/or salivary biomarkers, intended for use by medical professionals in a non-dental setting. Methods Consecutive, new patients from our outpatient clinic were recruited. A SROH questionnaire (8 questions) was conducted, followed by a 30 s oral rinse sampling protocol. A complete clinical periodontal examination provided the golden standard periodontitis classification: no/mild, moderate or severe periodontitis. Total periodontitis was defined as having either moderate or severe. Albumin and matrix metalloproteinase-8 concentrations, and chitinase and protease activities were measured in the oral rinses. Binary logistic regression analyses with backward elimination were used to create prediction models for both total and severe periodontitis. Model 1 included SROH, demographics and biomarkers. The biomarkers were omitted in the analysis for model 2, while model 3 only included the SROH questionnaire. The area under the receiver operating characteristic curves (AUROCC) provided the accuracy of each model. The regression equations were used to create scoring algorithms, composed of the remaining predictors, each with its own weight. Results Of the 156 patients participating in this study, 67% were classified with total periodontitis and 33% had severe periodontitis. The models for total periodontitis achieved an AUROCC of 0.91 for model 1, 0.88 for model 2 and 0.81 for model 3. For severe periodontitis, this was 0.89 for model 1, 0.82 for model 2 and 0.78 for model 3. The algorithm for total periodontitis (model 2), which we consider valid for the Dutch population, was applied to create a freely accessible, web-based screening tool. Conclusions The prediction models for total and severe periodontitis proved to be feasible and accurate, resulting in easily applicable screening tools, intended for a non-dental setting. Electronic supplementary material The online version of this article (10.1186/s12903-019-0784-7) contains supplementary material, which is available to authorized users.

Validation of self‐reported measures of periodontitis in a Spanish Population

“…Based on ROC statistics, in this study the predictive ability of a combination of questionnaire items and other sociodemographic and risk factors was comparable to other populations . Using the same classification, Eke and coworkers (2013) reported a ROC value of 0.81, while Heaton et al (2017) in a sample of African American women reported a ROC value of 0.79.…”

“…Carra et al (2018) developed a periodontal screening score (PESS; calculated with five of the self‐reported items plus age and smoking) potentially useful to screen for severe periodontitis in a French population, showing a sensitivity of 78.9% and a specificity of 74.8%, with a ROC value of 0.82. Last, a recent study from the Netherlands evaluated the added value of using salivary biomarkers from oral rinse samples in addition to the questionnaire and demographic determinants, concluding that these may be redundant in periodontitis screening, as long as the questionnaire alone performed very well (ROC value of 0.78 for severe periodontitis) …”

“…This questionnaire was validated and proven to be adequate to estimate the prevalence of periodontitis in national surveys in the United States of America . Other research groups have evaluated the performance of different questionnaires together with periodontitis symptoms and risk factors to ascertain periodontitis in different countries or populations at high risk such as postmenopausal females or African Americans …”

<p>Self-reported oral health and quality of life in patients with type 2 diabetes mellitus in primary care: a multi-center cross-sectional study</p>

“…*Estimated by applying screening tool. 28 a dentist, compared to 2.3±5.0 in those with a dentist (p=0.029). Patients without regular dentist consultation had a mean score of 3.7±7.0, compared to 2.1±4.6 in subjects who regularly visit a dentist (p=0.002) (Table S1).…”

“…tool. 28 This tool was able to accurately distinguish periodontitis patients, defined according to the golden standard, ie, full mouth clinical periodontal measurements. 29…”