Caries Management By Risk Assessment in a Cleft and Craniofacial Center

A predictive nomogram: a cross-sectional study on a simple-to-use model for screening 12-year-old children for severe caries in middle schools

BACKGROUND

A nomogram is a tool that transforms complex regression equations into simple and visual graphs and enables clinicians and patients to conveniently compute output probabilities without needing medical knowledge and complex formulas. The aim of this study was to develop and validate a predictive nomogram to screen for severe caries among 12-year-old children based on risk factors in Sichuan Province, China.

METHODS

A cross-sectional study of 4573 12-year-olds was conducted up to May 2016 in middle schools from three districts and three counties in Sichuan Province, China. All the children underwent oral examinations and completed questionnaires to assess general information, oral impacts on daily performance, dietary habits, subjective health conditions, history of dental trauma, frequency of toothache, dental visits, and knowledge, attitudes, and behaviours toward oral hygiene. Univariate analysis and multivariate logistic regression analysis were used to determine which variables were significantly associated with severe caries (operationalized as DMFT ≥ 3). A nomogram was developed and validated by using the 'rms' package and two cross-validation methods.

RESULTS

Severe caries was found in 537 of the 4573 children (11.74%). Multivariate logistic regression analysis revealed that the following variables predicted a higher risk of severe caries: 'female' [odds ratio (OR) = 1.985, 95% confidence interval (95% CI): 1.63-2.411], 'urban' (OR = 2.389, 95% CI: 1.96-2.91), 'non-only child' (OR = 1.317, 95% CI: 1.07-1.625), 'very poor self-assessment of oral health status' (OR = 2.157, 95% CI: 1.34-3.467) and 'visited a dentist less than 6 months' (OR = 1.861, 95% CI: 1.38-2.505). Multivariate logistic regression analysis also indicated that the following variables predicted a lower risk of severe caries: 'middle level of urbanization' (OR = 0.395, 95% CI: 0.32-0.495) and 'high level of urbanization' (OR = 0.466, 95% CI: 0.37-0.596). Both the fivefold and leave-one-out cross-validation methods indicated that the nomogram model built by these 6 variables displayed good disease recognition ability.

CONCLUSIONS

The nomogram was a simple-to-use model to screen children for severe caries. This model was found to facilitate non-dental professionals in assessing risk values without oral examinations and making referrals to dental professionals.

Adherence to Orthodontic Treatment in Youth With Cleft Lip and/or Palate

OBJECTIVE

To identify rates of nonadherence to orthodontic treatment among youth with cleft lip and/or palate (CL/P) and to compare demographic and clinical characteristics of patients categorized as adherent or nonadherent.

DESIGN

Retrospective chart review.

SETTING

Orthodontic treatment program affiliated with a US pediatric hospital-based craniofacial team.

PARTICIPANTS

Medical charts of 54 patients with CL/P beginning phase I or phase II orthodontics between 2011 and 2014 (54% male; mean age: 11.7 ± 3.2 years) were reviewed.

MAIN OUTCOME MEASURE(S)

Data abstracted included demographic and clinical characteristics. Patients were classified as nonadherent based upon treatment termination due to nonadherence, treatment courses extending beyond 3 years, and information about missed appointments, poor oral hygiene, and broken appliances.

RESULTS

In all, 24% had treatment terminated due to nonadherence and were significantly more likely to have behavioral health diagnoses (P = .01) or visits (P = .02) and social work consults (P = .01) than patients without termination. Thirty-seven percent had treatment courses beyond 3 years; youth with longer courses were significantly more likely to have cleft lip and palate versus cleft palate only or cleft lip (P = .03). Patients who missed more than 4 appointments were less likely to have behavioral health diagnoses (P < .01) compared to those with fewer missed appointments. Poor hygiene and broken appliance notations were common. Youth with poor hygiene notations were significantly older (P < .01) at treatment initiation than those without notations.

CONCLUSIONS

Nonadherence was associated with diagnosis, age, and history of behavioral health or social work involvement. An understanding of adherence and relationships with clinical and demographic factors can inform clinical care and support intervention development to improve outcomes.