Development of a Root Caries Prediction Model in a Population of Dental Attenders.

Root caries prevalence is increasing as populations age and retain more of their natural dentition. However, there is generally no accepted practice to identify individuals at risk of disease. There is a need for the development of a root caries prediction model to support clinicians to guide targeted prevention strategies. The aim of this study was to develop a prediction model for root caries in a population of regular dental attenders. Clinical and patient-reported predictors were collected at baseline by routine clinical examination and patient questionnaires. Clinical examinations were conducted at the 4-year timepoint by trained outcome assessors blind to baseline data to record root caries data at two thresholds - root caries present on any teeth (RC > 0) and root caries present on three or more teeth (RC ≥ 3). Multiple logistic regression analyses were performed with the number of participants with root caries at each outcome threshold utilized as the outcome and baseline predictors as the candidate predictors. An automatic backwards elimination process was conducted to select predictors for the final model at each threshold. The sensitivity, specificity, and c-statistic of each model's performance was assessed. A total of 1,432 patient participants were included within this prediction model, with 324 (22.6%) presenting with at least one root caries lesion, and 97 (6.8%) with lesions on three or more teeth. The final prediction model at the RC >0 threshold included increasing age, having ≥9 restored teeth at baseline, smoking, lack of knowledge of spitting toothpaste without rinsing following toothbrushing, decreasing dental anxiety, and worsening OHRQoL. The model sensitivity was 71.4%, specificity 69.5%, and c-statistic 0.79 (95% CI: 0.76, 0.81). The predictors included in the final prediction model at the RC ≥ 3 threshold included increasing age, smoking, and lack of knowledge of spitting toothpaste without rinsing following toothbrushing. The model sensitivity was 76.5%, specificity 73.6%, and c-statistic 0.81 (95% CI: 0.77, 0.86). To the authors' knowledge, this is the largest published root caries prediction model, with statistics indicating good model fit and providing confidence in its robustness. The performance of the risk model indicates that adults at risk of developing root caries can be accurately identified, with superior performance in the identification of adults at risk of multiple lesions.

How do different levels of smoking affect dental implants?

Data sources PubMed, EMBASE, Web of Science and Scopus.Study selection Randomised controlled clinical trials, cohort studies, case-control studies and case series reporting data on dental implant failures related to smoking published up to January 2019 were included, with no language restrictions.Data extraction and synthesis Data was extracted by one reviewer and quality assessment of the included observational studies was performed using the Newcastle-Ottawa scale. Smoker subgroups were categorised by number of cigarettes smoked daily, and statistical heterogeneities were assessed using Cochran's Q statistics and quantified using the I² test.Results A higher implant failure rate was found in patients who smoked <10 cigarettes/day than in non-smokers (p = 0.046), 95% CI (1-1.64). Likewise, patients who smoked >10 cigarettes/day showed higher failure rates than those smoking <10 cigarettes/day (p <0.001), 95% CI (1.31-2.17).Conclusions Increase in smoking dose appears to correspond directly with increase in implant failure rate. Smoking <10 cigarettes/day can be recommended as a safe level for dental implant failure.

Cone beam computed tomography in pre-surgical assessment of mandibular third molars.

DesignMulticentre, randomised controlled clinical trial.InterventionPatients referred for third molar removal received a digital panoramic radiograph(PR). Adults with one or more lower third molars in a close relationship with the mandibular canal were eligible for the study. Patients randomised to the cone beam computed tomography (CBCT) group received a high resolution CBCT scan in addition to the PR. All lower third molar extractions were performed under local anaesthesia without sedation and without antibiotic prophylaxis. Information on variables such as experience of the surgeon, duration of surgery and technique for third molar removal were recorded.Outcome measureThe primary outcome measure was the number of patient-reported altered sensations one week after surgery. Secondary outcomes included the number of patients with an objective IAN injury; permanent IAN injury (>6 months); occurrence of other postoperative complications (wound infection, alveolar osteitis); Oral Health Related Quality of Life-14, questionnaire responses; pain (VAS score); duration of surgery; number of emergency visits; and number of missed days of work or study.ResultsThree hundred and forty-one patients with 477 lower third molars were randomised from three centres. Two hundred and sixty-eight patients with 320 mandibular third molars were analysed according to the intention-to-treat principle for the primary and secondary outcomes. The overall incidence of patient-reported altered sensations one week after surgery was 6.3%. At one week there was no difference in subjective IAN injury between the CBCT and PR group. No significant differences were noted between the two groups for any of the secondary outcomes recorded.ConclusionsAlthough CBCT is a valuable diagnostic adjunct for identification of an increased risk for IAN injury, the use of CBCT does not translate into a reduction of IAN injury and other postoperative complications, after removal of the complete mandibular third molar. In these selected cases with a high risk for IAN injury, an alternative strategy, such as monitoring or a coronectomy, might be more appropriate.