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Published on: November 6, 2014
Prediction of Preterm Birth among Infants with Orofacial Cleft Defects
Katherine L Ludorf1, Renata H Benjamin1, Mark A Canfield2
1Department of Epidemiology, Human Genetics and Environmental Sciences, UTHealth School of Public Health, Houston, TX, USA.
Insights
Researchers developed models to predict preterm birth risk in infants with orofacial clefts. The models showed modest predictive capability, identifying high-risk pregnancies for improved infant outcomes.
Area of Science:
- Medical research
- Public health
- Pediatric medicine
Background:
- Orofacial clefts are common birth defects.
- Preterm birth poses significant risks to infant health and development.
- Predicting preterm birth in infants with orofacial clefts is crucial for targeted interventions.
Purpose of the Study:
- To develop and validate risk prediction models for preterm birth in infants diagnosed with orofacial clefts.
- To assess the predictive performance of these models using population-based data.
Main Methods:
- Utilized data from the Texas Birth Defects Registry (1999-2014) for 6774 infants with orofacial clefts.
- Employed logistic regression to identify maternal and infant predictors of preterm birth.
- Validated models internally using bootstrapping and calculated the area under the curve (AUC) for performance assessment.
Main Results:
- The overall predictive model for preterm birth in infants with orofacial clefts achieved an optimism-corrected AUC of 0.67.
- Separate models for cleft lip and cleft palate demonstrated similar predictive performance (AUCs 0.66 and 0.67, respectively).
- A subgroup analysis for delivery before 32 weeks showed improved predictive capability (AUC = 0.74).
Conclusions:
- The developed models represent an initial step in predicting preterm birth risk for infants with orofacial clefts.
- Identifying high-risk pregnancies can potentially lead to improved outcomes for affected infants.
- Further research may refine these models for clinical application and enhanced neonatal care.
Objective:
To develop risk prediction models for preterm birth among infants with orofacial clefts.
Design:
Data from the Texas Birth Defects Registry for infants with orofacial clefts born between 1999-2014 were used to develop preterm birth predictive models. Logistic regression was used to consider maternal and infant characteristics, and internal validation of the final model was performed using bootstrapping methods. The area under the curve (AUC) statistic was generated to assess model performance, and separate predictive models were built and validated for infants with cleft lip and cleft palate alone. Several secondary analyses were conducted among subgroups of interest.
Setting:
State-wide, population-based Registry data.
Patients/Participants:
6774 infants with orofacial clefts born in Texas between 1999-2014.
Main Outcome Measure(S):
Preterm birth among infants with orofacial clefts.
Results:
The final predictive model performed modestly, with an optimism-corrected AUC of 0.67 among all infants with orofacial clefts. The optimism-corrected models for cleft lip (with or without cleft palate) and cleft palate alone had similar predictive capability, with AUCs of 0.66 and 0.67, respectively. Secondary analyses had similar results, but the model among infants with delivery prior to 32 weeks demonstrated higher optimism-corrected predictive capability (AUC = 0.74).
Conclusions:
This study provides a first step towards predicting preterm birth risk among infants with orofacial clefts. Identifying pregnancies affected by orofacial clefts at the highest risk for preterm birth may lead to new avenues for improving outcomes among these infants.
