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Predicting neonatal respiratory distress syndrome in late preterm infants
Shaked Yarza1, Ran Matot1,2, Asaf Romano1,2
1The Helen Schneider Hospital for Women, Rabin Medical Center - Beilinson Hospital, 39 Jabotinsky St., Petach Tikva, 4941492, Israel.
Insights
Predictive models using maternal and prenatal factors can identify late preterm infants at risk for respiratory distress syndrome (RDS). These tools aid in antenatal counseling and delivery care planning for improved neonatal outcomes.
Area of Science:
- Neonatal Medicine
- Maternal-Fetal Medicine
- Predictive Analytics in Healthcare
Background:
- Late preterm births (34 0/7–36 6/7 weeks) account for 75% of preterm deliveries.
- Infants born late preterm face increased risks for respiratory distress syndrome (RDS), a primary cause of neonatal morbidity.
- Developing predictive models for RDS in this population is crucial for timely intervention.
Purpose of the Study:
- To develop and evaluate predictive models for neonatal RDS.
- To utilize maternal and prenatal clinical variables available before labor onset.
- To identify key predictors of RDS in late preterm neonates.
Main Methods:
- Retrospective cohort study (May 2016 - June 2023) at a tertiary medical center.
- Analysis of a healthcare database for singleton neonates born between 34 0/7 and 36 6/7 gestational weeks.
- Comparison of three predictive models: multivariable logistic regression, conditional inference trees (CIT), and extreme gradient boosting (XGBoost).
Main Results:
- Five significant predictors of RDS identified: gestational age, male sex, cesarean delivery, nulliparity, and antenatal corticosteroids (ACS).
- Logistic regression showed the highest discriminatory performance (AUC 0.75), followed by XGBoost (AUC 0.70) and CIT (AUC 0.68).
- Models demonstrated modest discrimination but strong negative predictive value for RDS.
Conclusions:
- Prenatal clinical variables can predict RDS risk in late preterm infants with modest accuracy.
- These predictive tools can assist in antenatal counseling and delivery site selection.
- Further research is needed to validate models and enhance predictive performance with additional variables.
Background:
Late preterm births (34 + 0-36 + 6 gestational weeks) comprise 75% of preterm deliveries and are at increased risk for respiratory distress syndrome (RDS), a leading cause of neonatal morbidity. We aimed to develop a predictive model for RDS in late preterm neonates based on maternal and prenatal clinical variables available before the onset of labor.
Methods:
A retrospective cohort study was conducted in a tertiary medical center between May 2016 and June 2023. The institutional healthcare database was searched for all women who gave birth to a singleton neonate at 34 + 0-36 + 6 gestational weeks. Three models were applied to predict the likelihood of development of neonatal RDS: multivariable logistic regression, conditional inference trees (CIT), and extreme gradient boosting (XGBoost).
Results:
Out of 37,205 deliveries during the study period, 2,444 singleton neonates were included, of whom 137 (5.6%) were diagnosed with RDS. Multivariable logistic regression analysis identified five significant predictors of RDS: gestational age (aOR 0.35, 95%CI: 0.32-0.38), male sex (aOR 1.68, 95%CI: 1.47-1.92), cesarean delivery (CD) (aOR 1.28, 95%CI: 1.20-1.37), nulliparity (aOR 0.47, 95%CI: 0.40-0.54) and antenatal corticosteroids (ACS) (aOR 0.54, 95%CI: 0.45-0.64 for administration before 34 weeks; aOR 0.60, 95%CI: 0.49-0.72 for administration after 34 weeks). These findings represent predictive associations rather than causal treatment effects. Among the predictive models, the logistic regression demonstrated the highest discriminatory performance with an area under the curve (AUC) of 0.75 (95% CI: 0.70-0.79), followed by XGBoost algorithm with an AUC of 0.70 (95% CI: 0.66-0.75), and CIT with an AUC of 0.68 (95% CI: 0.64-0.73).
Conclusions:
Models based on prenatal maternal and fetal characteristics demonstrated modest discrimination but strong negative predictive value for identifying RDS in late preterm infants. Such tools may assist with antenatal counseling and support decisions regarding the appropriate level-of-care setting for delivery. Further studies are needed to validate these findings and to explore whether incorporating additional clinical variables can enhance predictive performance.
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