Background: Psychosocial wellbeing is an important determinant for patients' oral health‐related quality of life (OHRQoL). Psychosocial impact (PI), together with the dimensions Oral Function, Orofacial Pain and Orofacial Appearance, has been proposed to cover the different areas of OHRQoL.Objective: The objective of the study was to collect further scientific support for the new four‐dimensional structure of OHRQoL. This study is one out of a series of four and focuses on the PI in patients with dental anxiety, oral cancer and periodontitis (PROSPERO registration number: CRD42017064033).Methods: Five databases (Pubmed (Medline), EMBASE, Cochrane, CINAHL and PsycINFO) were electronically searched on 8 June 2017 and updated on 14 January 2019, to identify the studies that measure OHRQoL using the Oral Health Impact Profile (OHIP) for oral health conditions. In this review, studies were included if the mean/median domain scores from OHIP‐14 or OHIP‐49 were available for patients with dental anxiety, oral cancer or periodontitis. The score of the handicap domain from the OHIP was used to assess patients` PI. The handicap domain includes 6 items for OHIP‐49 with a domain score ranging from 0 to 24 and 2 items for OHIP‐14 with a domain score ranging from 0 to 8. For comparison between the 2 versions of the OHIP, the domain score of OHIP‐49 was conversed into a 0 to 8 metric. The domain scores of the included studies were then pooled, separately for each of the included dental disorders.Results: A total of 2104 records were identified based on the search strategy. After screening of titles and abstracts, 1607 articles were reviewed in full text. Twenty‐three articles met the inclusion criteria for this review and were included in the study. The 23 articles contained 3884 patients, grouped in 30 patient populations and 42 patient samples. The pooled mean scores of PI for dental anxiety, oral cancer and periodontitis were 3.2, 1.9 and 0.8, respectively, on the 0 to 8 metric.Conclusion: This review provides standardised information about the OHRQoL impact for three dental disorders as a model for the PI dimension. Dental anxiety tends to show the strongest effect on the PI dimension, while periodontitis tends to show the weakest effect on the PI dimension. Future studies need to confirm whether the reported differences in PI scores between the three dental disorders are statistically significant.

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. 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. 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. 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.

To assess the predictive performance of caries risk assessment (CRA) models for prediction of caries increment for individuals based on a systematic review and meta-analyses. We included external validation studies assessing the predictive performance of CRA models for prediction of caries increment for individuals, using discrimination and calibration as the outcome parameters. PubMed, EMBASE, and CINAHL were searched electronically on 10th September 2020 to identify prediction modeling studies on external validation of CRA models. The risk of bias of the included studies was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). A total of 22 studies with seven different CRA models were included. As for full Cariogram, the pooled area under the receiver operating characteristic curve (AUC) was 0.78 (95 %CI: 0.68; 0.85) based on eight studies regardless of the risk of bias levels, and 0.82 (95 %CI: 0.58; 0.93) based on four studies with low risk of bias only. The pooled observed: expected ratio (O:E ratio) of full Cariogram was 0.91 (95 %CI: 0.72; 1.14) based on 12 studies regardless of the risk of bias levels, and 0.89 (95 %CI: 0.71; 1.12) based on five studies with low risk of bias only. As for reduced Cariogram, the pooled AUC was 0.72 (95 %CI: 0.67; 0.77) based on six studies regardless of the risk of bias levels, and 0.74 (95 %CI: 0.45; 0.91) based on two studies with low risk of bias only. The pooled O:E ratio of reduced Cariogram was 0.84 (95 %CI: 0.59; 1.18) based on six studies regardless of the risk of bias levels, and 1.05 (95 %CI: 0.43; 2.59) based on two studies with low risk of bias only. Based on an insufficient number of studies for the other CRA models, the pooled AUCs ranged from 0.50 to 0.88, while the pooled O:E ratio ranged from 0.38 to 1.00. The average predictive performance of both full and reduced Cariogram seems to be acceptable. However, the evidence from research does not allow a firm conclusion on the performance of the other included CRA models, due to the insufficient number of high-quality studies. Both full and reduced Cariogram were found to be reliable CRA models for prediction of caries increment in clinical practices for dental patients and communities for general populations. The reduced Cariogram showed better predictive performance and less burden in terms of time and resources to individuals than the full Cariogram. Therefore, the reduced Cariogram could be more recommended than the full Cariogram.

To investigate the association between self-reported sleep bruxism and insomnia and their potential risk factors (eg, depression and anxiety), and to construct a network model with all these factors. We recruited 2251 participants from the Netherlands Sleep Registry. All participants completed questionnaires on self-reported sleep bruxism, insomnia, depression, anxiety, smoking frequency, and alcohol and caffeine consumption. The associations between self-reported sleep bruxism and other variables were analyzed by univariate analysis, multivariate logistic regression, and network analysis. Although univariate analysis showed that there was a positive association between sleep bruxism and insomnia (P < 0.001), this association disappeared in the multivariate logistic regression model (P = 0.258). However, multivariate logistic regression did show an association between self-reported sleep bruxism and anxiety (OR = 1.087, 95% CI 1.041-1.134). The network model showed that there was no direct link between self-reported sleep bruxism and insomnia. However, there was an indirect link between self-reported sleep bruxism and insomnia via anxiety. Although self-reported sleep bruxism has no direct association with insomnia, anxiety is a bridging factor between these variables.

Aim: To investigate whether and which diseases co‐occur with periodontitis (PD) to assess the prevalence of comorbidities and multimorbidity and to identify patterns and profiles of comorbidity and multimorbidity and the influence of demographic and lifestyle factors to identify distinct groups of multimorbid patients.Materials and Methods: A database from the Academic Centre of Dentistry Amsterdam (ACTA) with 37,801 adult individuals containing information about demographic (age, sex, socio‐economic position [SEP]) and lifestyle factors (smoking, alcohol use and addictive substance use) and PD and systemic diseases was constructed. PD assessment was based on clinical information by the use of claim codes and systemic diseases data were derived from self‐reported medical history. For analyses, univariable and multivariable (adjusted for age, sex, SEP, smoking, alcohol use and addictive substance use) logistic regression analyses and cluster analysis were used.Results: Individuals with PD more often had one or multiple diseases. The adjusted odds ratio (OR) for PD patients having up to four systemic diseases ranged from 1.46 to 1.20. Co‐occurrence of PD with several systemic diseases and a higher prevalence of multimorbidity was found (adjusted OR comorbidity = 1.36; 95% confidence interval (CI): 1.30–1.43; multimorbidity = 1.18; 95% CI: 1.11–1.25). Four clusters existed: cluster 1 was defined as a periodontal and systemically healthy group and cluster 4 as burdened with PD but not containing any systemic diseases. Individuals in cluster 1 were of the lowest age (44.9 [SD: 15.5]) and had the lowest prevalence of the lifestyle factors of smoking (13.6%) and alcohol use (3.9%). Clusters 2 and 3 contained both PD and had several systemic diseases but were different from each other. Cluster 2 contained 34.5% of PD individuals and had mainly respiratory tract, immune system and digestive system diseases. Cluster 3 contained 45.9% of PD individuals and had mainly cardiometabolic diseases. Cluster 2 had the highest prevalence of females (63.1%) and the highest prevalence of smokers (23.8%) and addictive substance users (8.9%). Cluster 3 included individuals of the highest age (63.5 [SD: 11.9]), and had highest prevalence of alcohol users (17.7%) and lowest prevalence of addictive substance users (3.8%).Conclusions: This study shows that individuals with PD are more often burdened with comorbidity and multimorbidity. Presence of distinct clusters suggests overlap in pathophysiology between certain types of PD and specific systemic diseases. Therefore, PD can be considered as part of multimorbidity, as one of the systemic diseases co‐occurring in certain groups of individuals.

Naichuan Su, PhD(c)/Corine M. Visscher, PT, PhD/Arjen J. van Wijk, PhD/Frank Lobbezoo, DDS, PhD/Geert J.M.G van der Heijden, PhD: Aims: To identify potential predictors of types of treatment indicated for patients with temporomandibular disorders (TMD) and to develop, validate, and calibrate a prediction model for type of treatment. Methods: The derivation cohort at baseline was comprised of 356 adult patients with TMD. Patient and disease characteristics were recorded at baseline as potential predictors. Types of treatment indicated for TMD patients were the end points of the model, classified into no treatment, physical treatment only (including splint and/or physiotherapy), and combined physical and psychological treatment. Multinomial logistic regression analysis was used to develop the prediction model. The internal validation, calibration, discrimination, and external validation of the model were determined. For practical use, the prediction model was converted into score charts and line charts. The score of each included predictor was produced based on the shrunken regression coefficients. Results: Patient age, gender, anxiety, sleep bruxism, pain-related TMD, function-related TMD, stress, passive stretch of maximum mouth opening, and depression were significantly associated with the type of treatment indicated for TMD patients. The multinomial model showed reasonable calibration and good discrimination, with area under the curve values of 0.76 to 0.86. The internal validity of the model was good, with a shrinkage factor of 0.89. The external validity of the model was acceptable. Conclusion: Potential predictors in patient profiles for prediction of type of treatment indicated for TMD patients were identified. The internal validity, calibration, discrimination, and external validity of the model were acceptable.