Background: It is thought that infrabony defect morphology affects the outcome of periodontal regenerative surgery. However, this has not been systematically investigated.Aims: To investigate how well defect morphology is described in papers reporting regenerative therapy of periodontal infrabony defects and to investigate its effect on clinical and radiographic outcomes.Materials and Methods: A search was conducted in 3 electronic databases for publications reporting clinical and radiographic outcomes of periodontal intra‐bony defects after regenerative therapy, divided by defect morphology.Results: The initial search resulted in 4487 papers, reduced to 143 after first and second screening. Fifteen of these publications were suitable for a fixed‐effects meta‐analysis. Initial defect depth was found to influence radiographic bone gain 12 months post‐surgery, while narrower angles and increased number of walls influenced both radiographic bone gain and clinical attachment level (CAL) gain at 12 months. These associations seemed to occur irrespective of biomaterials used. Risk of bias ranged from low to high.Conclusion: Deeper defects with narrower angles and increased number of walls exhibit improved CAL and radiographic bone gain at 12 months post‐regenerative surgery. More data are needed about other aspects of defect morphology such as extension to buccal/lingual surfaces.

Aim: To assess the diagnostic utility of an oral rinse active matrix metalloproteinase‐8 (aMMP‐8) point‐of‐care test (POCT) for differentiating periodontal health, gingivitis, as well as different stages and grades of periodontitis.Materials & Methods: The aMMP‐8 index test was undertaken in 408 consecutive adults, followed by a full‐mouth periodontal examination. The reference standard was the 2017 World Workshop classification of periodontal diseases. Sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) were assessed.Results: 68.6% of the participants were diagnosed with periodontitis, including Stages I (15.9%), II (15.9%), III (29.7%) and IV (7.1%). A positive aMMP‐8 POCT was associated with periodontitis after adjusting for age, gender, tobacco smoking and systemic diseases, while it was unable to differentiate among the stages/grades of periodontitis and between gingivitis/periodontal health. This test showed a sensitivity of 33.2% and a specificity of 93.0% for detecting periodontitis (threshold level >10 ng/ml). The levels of aMMP‐8 adjusted by the number of teeth present (aMMP‐8/NTP) performed better for periodontitis (sensitivity: 67.1%; specificity: 68.8%). Notably, aMMP‐8/NTP were strongly predictive for Stage IV periodontitis (threshold level =0.4312 ng/ml) (sensitivity: 89.7%; specificity: 73.6%; and AUROC: 0.856). The test performance greatly improved in combination with age and smoking, with a sensitivity of 82.5%, a specificity of 84.4%, and an AUROC of 0.883.Conclusion: This aMMP‐8 POCT is able to detect periodontitis with better specificity than sensitivity across the spectrum of its severity. This test may be useful for periodontal screening in conjunction with subject characteristics and/or other sensitive screening tools. Further validation studies are needed.

Aim: To develop a multiclass non‐clinical screening tool for periodontal disease and assess its accuracy for differentiating periodontal health, gingivitis and different stages of periodontitis.Materials and Methods: A cross‐sectional diagnostic study on a convenience sample of 408 consecutive subjects was conducted by applying three non‐clinical index tests estimating different features of the periodontal health–disease spectrum: a self‐administered questionnaire, an oral rinse activated matrix metalloproteinase‐8 (aMMP‐8) point‐of‐care test (POCT) and determination of gingival bleeding on brushing (GBoB). Full‐mouth periodontal examination was the reference standard. The periodontal diagnosis was made on the basis of the 2017 classification of periodontal diseases and conditions. Logistic regression and random forest (RF) analyses were performed to predict various periodontal diagnoses, and the accuracy measures were assessed.Results: Four‐hundred and eight subjects were enrolled in this study, including those with periodontal health (16.2%), gingivitis (15.2%) and stage I (15.9%), stage II (15.9%), stage III (29.7%) and stage IV (7.1%) periodontitis. Nine predictors, namely ‘gum disease’ (Q1), ‘a rating of gum/teeth health’ (Q2), ‘tooth cleaning’ (Q3a), the symptom of ‘loose teeth’ (Q4), ‘use of floss’ (Q7), aMMP‐8 POCT, self‐reported GBoB, haemoglobin and age, resulted in high levels of accuracy in the RF classifier. High accuracy (area under the ROC curve > 0.94) was observed for the discrimination of three (health, gingivitis and periodontitis) and six classes (health, gingivitis, stages I, II, III and IV periodontitis). Confusion matrices showed that the misclassification of a periodontitis case as health or gingivitis was less than 1%–2%.Conclusions: Machine learning‐based classifiers, such as RF analyses, are promising tools for multiclass assessment of periodontal health and disease in a non‐clinical setting. Results need to be externally validated in appropriately sized independent samples (ClinicalTrials.gov NCT03928080).