Identification of Neonatal Factors Predicting Pre-Discharge Mortality in Extremely Preterm or Extremely Low Birth Weight Infants: A Historical Cohort Study.

BACKGROUND/

OBJECTIVES

This study identified early neonatal factors predicting pre-discharge mortality among extremely preterm infants (EPIs) or extremely low birth weight infants (ELBWIs) in China, where data are scarce.

METHODS

We conducted a retrospective analysis of 211 (92 deaths) neonates born <28 weeks of gestation or with a birth weight <1000 g, admitted to University Affiliated Hospitals from 2013 to 2024 in Guangzhou, China. Data on 26 neonatal factors before the first 24 h of life and pre-discharge mortality were collected. LASSO-Cox regression was employed to screen predictive factors, followed by stepwise Cox regression to develop the final mortality prediction model. The model's performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic, calibration curves, and decision curve analysis.

RESULTS

The LASSO-Cox model identified 13 predictors that showed strong predictive accuracy (AUC: 0.806/0.864 in the training/validation sets), with sensitivity and specificity rates above 70%. Among them, six predictors remained significant in the final stepwise Cox model and generated similar predictive accuracy (AUC: 0.830; 95% CI: 0.775-0.885). Besides the well-established predictors (e.g., gestational age, 5 min Apgar scores, and multiplicity), this study highlights the predictive value of the maximum FiO2. It emphasizes the significance of the early use of additional doses of surfactant and umbilical vein catheterization (UVC) in reducing mortality.

CONCLUSIONS

We identified six significant predictors for pre-discharge mortality. The findings highlighted the modifiable factors (FiO2, surfactant, and UVC) as crucial neonatal factors for predicting mortality risk in EPIs or ELBWIs, and offer valuable guidance for early clinical management.