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Development of a Prediction Model to Identify Children at Risk of Future Developmental Delay at Age 4 in a
Nienke H van Dokkum1,2, Sijmen A Reijneveld2, Martijn W Heymans3
1Department of Pediatrics, Division of Neonatology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713GZ Groningen, The Netherlands.
Insights
A new prediction model identifies infants at risk for developmental delay by age 4 using simple factors like milestones and maternal education. This tool aids early intervention for at-risk children.
Area of Science:
- Pediatrics
- Developmental Psychology
- Public Health
Background:
- Early identification of developmental delay is crucial for timely intervention.
- Existing prediction models often lack easily obtainable predictors or general population applicability.
- Predicting developmental delay risk in infancy can significantly improve long-term child outcomes.
Purpose of the Study:
- To develop and validate a prediction model for developmental delay at age 4 in a general infant population.
- To utilize easily obtainable predictors for practical application in community settings.
- To assess the model's performance in terms of calibration and discriminative ability.
Main Methods:
- Longitudinal cohort study of 1983 infants (full-term and preterm).
- Developmental assessment at age 4 using the Ages and Stages Questionnaire.
- Multiple logistic regression with backward selection and internal validation, including sensitivity, specificity, and AUC calculation.
Main Results:
- The final model incorporated sex, maternal education, maternal obesity, early milestones (smiling, 2-3 word sentences, standing), and weight-for-height z-score at age 1.
- The model demonstrated good fit and high discriminative performance with an Area Under the Curve (AUC) of 0.837.
- At a 10% probability cut-off, the model achieved 73% sensitivity and 80% specificity.
Conclusions:
- The developed prediction model is a promising tool for identifying infants at high risk of developmental delay.
- Its ease of use and strong performance make it suitable for community-based screening.
- Early identification can facilitate timely interventions, potentially improving developmental trajectories for at-risk children.
Abstract:
Our aim was to develop a prediction model for infants from the general population, with easily obtainable predictors, that accurately predicts risk of future developmental delay at age 4 and then assess its performance. Longitudinal cohort data were used (N = 1983), including full-term and preterm children. Development at age 4 was assessed using the Ages and Stages Questionnaire. Candidate predictors included perinatal and parental factors as well as growth and developmental milestones during the first two years. We applied multiple logistic regression with backwards selection and internal validation, and we assessed calibration and discriminative performance (i.e., area under the curve (AUC)). The model was evaluated in terms of sensitivity and specificity at several cut-off values. The final model included sex, maternal educational level, pre-existing maternal obesity, several milestones (smiling, speaking 2-3 word sentences, standing) and weight for height z score at age 1. The fit was good, and the discriminative performance was high (AUC: 0.837). Sensitivity and specificity were 73% and 80% at a cut-off probability of 10%. Our model is promising for use as a prediction tool in community-based settings. It could aid to identify infants in early life (age 2) with increased risk of future developmental problems at age 4 that may benefit from early interventions.
