ArticleChildren (Basel, Switzerland)2024
Identification of Neonatal Factors Predicting Pre-Discharge Mortality in Extremely Preterm or Extremely Low Birth Weight Infants: A Historical Cohort Study.
Article in Children (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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7 authors.
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Abstract
BACKGROUND/
objectivesThis 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.
methodsWe 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.
resultsThe 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 FiO
conclusionsWe identified six significant predictors for pre-discharge mortality. The findings highlighted the modifiable factors (FiO
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