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Establishment and Validation of an Early Childhood Caries Prediction Model: a Multicentre Prospective Cohort Study
Insights
A new risk assessment model accurately predicts early childhood caries (dental decay) in children. This tool aids dentists in identifying at-risk youth for timely intervention.
Area of Science:
- Pediatric Dentistry
- Oral Health Epidemiology
- Risk Assessment Modeling
Background:
- Early childhood caries (ECC) poses a significant public health challenge.
- Accurate risk assessment is crucial for early intervention and prevention strategies.
Purpose of the Study:
- To develop and validate a predictive risk assessment model for early childhood caries.
- To assess the model's credibility and performance in identifying children at risk for caries.
Main Methods:
- A 1-year multicentre cohort study involving 4,381 children aged 3 years.
- Collected social, behavioral, and clinical data, including plaque acidogenicity and salivary pH.
- Utilized logistic regression and cross-validation to construct and validate the predictive model.
Main Results:
- The predictive model achieved an area under the curve (AUC) of 0.83 in the development set and 0.80 in the validation set.
- Demonstrated high sensitivity (0.69-0.72) and specificity (0.73-0.81) for predicting caries incidence.
- Internal validation confirmed the model's good discriminative ability.
Conclusions:
- The developed risk assessment models show good discriminability for predicting early childhood caries.
- The models are suitable for use by primary oral care clinicians and dental hospitals for risk stratification.
Objective:
To establish a risk assessment model for early childhood caries and verify its credibility.
Methods:
This 1-year multicentre cohort study comprised cohorts of 3-year-old children with and without caries recruited from kindergartens in Beijing, Shanghai, Sichuan, Hubei and Guangdong. Oral disease-related social and behavioural risk factors were collected from both children and their parents using questionnaires. Clinical examinations were performed according to World Health Organization standards, and the plaque's ability to produce acid, saliva pH value and saliva-buffering capacity were assessed. Logistic regression methods were employed to construct a risk-prediction model for caries incidence. Cross-validation methods were applied for the internal validation of the model, and the area under the curve (AUC) and calibration curves were used to evaluate the predictive performance of the constructed model.
Results:
A total of 4,381 children were included in the study and randomly allocated 1:1 to the development and validation sets. Using the new occurrence of decayed, missing and filled surfaces as the dependent variable, the predictive model demonstrated AUCs of 0.83 (95% confidence interval [CI] 0.81 to 0.84) in the development model and 0.80 (95% CI 0.78 to 0.82) in the validation model at 1 year. The training and validation sets exhibited high sensitivity (0.69 to 0.72) and specificity (0.73 to 0.81).
Conclusion:
The predictive models demonstrated good discriminability with both the training and validation sets and were suitable for primary oral care clinicians and stomatological hospitals.
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