Multidisciplinary Presurgical Education: Clinical Impact on Children With Orofacial Clefts Undergoing Maxillary Distraction via Rigid External Distraction.

OBJECTIVE: Treatment of severe maxillary hypoplasia is commonly addressed via distraction osteogenesis with a rigid external device (RED). While effective, this method can be socially stigmatizing in an already vulnerable patient population. To prepare children and their caregivers for life with a RED and decrease peri-operative anxiety, we instituted a multidisciplinary pre-surgical education session (MPES). This educational team involves our cleft care coordinator, child life specialist, orthodontist and plastic surgeon 2 weeks prior to surgery. We reviewed the impact of this intervention by examining clinical outcomes before and after its implementation. DESIGN: From February 2017 to February 2020, a retrospective chart review was performed to include patients with orofacial clefts and maxillary hypoplasia who underwent maxillary distraction osteogenesis with RED at our center before (28 patients) and after (29 patients) the implementation of MPES. RESULTS: MPES was associated with a significantly shorter length of stay compared to controls who did not receive MPES (3.6 vs 3.1 days, p < 0.03) and significantly decreased usage of inpatient narcotic pain medication compared to controls (16.8 morphine equivalents vs 31.8 morphine equivalents, p < 0.02). Our intervention also demonstrated a trend towards decrease in minor complications but did not achieve statistical significance p = 0.32). CONCLUSIONS: Multidisciplinary presurgical education is a beneficial adjunct in the care of patients with orofacial clefts and maxillary hypoplasia undergoing maxillary advancement with a RED.

Application of machine learning for diagnostic prediction of root caries.

OBJECTIVE: This study sought to utilise machine learning methods in artificial intelligence to select the most relevant variables in classifying the presence and absence of root caries and to evaluate the model performance. BACKGROUND: Dental caries is one of the most prevalent oral health problems. Artificial intelligence can be used to develop models for identification of root caries risk and to gain valuable insights, but it has not been applied in dentistry. Accurately identifying root caries may guide treatment decisions, leading to better oral health outcomes. METHODS: Data were obtained from the 2015-2016 National Health and Nutrition Examination Survey and were randomly divided into training and test sets. Several supervised machine learning methods were applied to construct a tool that was capable of classifying variables into the presence and absence of root caries. Accuracy, sensitivity, specificity and area under the receiver operating curve were computed. RESULTS: Of the machine learning algorithms developed, support vector machine demonstrated the best performance with an accuracy of 97.1%, precision of 95.1%, sensitivity of 99.6% and specificity of 94.3% for identifying root caries. The area under the curve was 0.997. Age was the feature most strongly associated with root caries. CONCLUSION: The machine learning algorithms developed in this study perform well and allow for clinical implementation and utilisation by dental and nondental professionals. Clinicians are encouraged to adopt the algorithms from this study for early intervention and treatment of root caries for the ageing population of the United States, and for attaining precision dental medicine.

Oral health as a gateway to overall health and well-being: Surveillance of the geriatric population in the United States.

AIMS: Little evidence exists to confirm that better oral health is associated with better overall health and well-being. The present study aimed to examine the impact of oral health on the overall health of the population greater than 65-year old in the entire United States. METHODS AND RESULTS: Data from National Health and Nutrition Examination Survey (NHANES) 2015-2016 were used. Variables included demographics and perceptions of oral health and overall health and well-being. Weighted prevalence estimates were calculated using mean, standard deviation, and percentage as appropriate. Chi-square tests and logistic regressions were performed to examine the association of oral health with physical health, mental health, general health, and systemic disease conditions. Analyses showed statistically significant relationships between oral health, physical, mental and general health, energy levels, work limitation, depression, and appetite. Out of the 10 systemic diseases being investigated, six of them were directly related to oral health outcome. CONCLUSION: This study provided strong empirical evidence that oral health is directly associated with different disease conditions and contributes largely to an individual's general health, particularly in the elderly. In the current landscape of patient-centered and value-based care, addressing the oral health needs of the elderly, who generally find themselves with limited access to care, should be a priority.