Antenatal Prediction Model for Neonatal Intensive Care Unit Admission in Late Preterm Infants
Erkan Yergin, İbrahim Taşkum, Seyhun Sucu, Fatma Didem Yücel Yetişkin, Serhat Özkan, Necd Makansi, Cansu Barutçu, Selcan SınacıABSTRACT
Objective
To develop and internally validate an antenatal prediction model for neonatal intensive care unit (NICU) admission among late preterm infants using routinely available maternal and obstetric parameters.
Methods
This retrospective observational cohort study included deliveries between 34 0 / 7 and 36 6 / 7 weeks of gestation at a tertiary referral center. Maternal, obstetric, and perinatal data were extracted from electronic medical records. The primary outcome was NICU admission. Variable selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO), followed by penalized multivariable logistic regression. Model performance was assessed using Harrell's concordance index (C‐index), bootstrap internal validation (1000 resamples), calibration analysis, and decision curve analysis. A nomogram was constructed to estimate individualized NICU admission risk.
Results
A total of 2007 late preterm pregnancies were included, and neonatal NICU admission occurred in 656 pregnancies (32.7%). Independent predictors of NICU admission were lower gestational age at delivery, fetal growth restriction, twin pregnancy, cesarean delivery, and antenatal corticosteroid exposure. The model demonstrated good discrimination with an apparent C‐index of 0.752 and an optimism‐corrected C‐index of 0.747. Calibration analysis showed excellent agreement between predicted and observed risks. Decision curve analysis indicated a positive net benefit across a wide range of clinically relevant threshold probabilities. The resulting nomogram enabled individualized antenatal risk estimation.
Conclusion
An antenatal model incorporating routinely available clinical variables may help estimate the risk of NICU admission among late preterm infants; however, external validation is necessary to confirm its generalizability.