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Real-time predictive model of extrauterine growth retardation in preterm infants with gestational age less than

Liang Gao1, Wei Shen1, Fan Wu2

  • 1Department of Neonatology, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, 361000, Fujian, China.

Scientific Reports
|June 5, 2024
PubMed

Insights

This study developed a real-time risk model for extrauterine growth retardation (EUGR) in very preterm infants. The model accurately predicts EUGR using key clinical factors, aiding early intervention.

Area of Science:

  • Neonatalogy
  • Pediatric Medicine
  • Clinical Prediction Modeling

Background:

  • Extrauterine growth retardation (EUGR) is a significant concern in very preterm infants, impacting long-term health outcomes.
  • Accurate and timely prediction of EUGR is crucial for implementing effective interventions and improving infant well-being.

Purpose of the Study:

  • To develop and validate a real-time risk prediction model for extrauterine growth retardation (EUGR) in very preterm infants.
  • To identify optimal predictors for EUGR and present them in an accessible nomogram format for clinical use.

Main Methods:

  • A cohort of 2514 very preterm infants was divided into training and external validation sets.
  • Univariate analysis, Lasso regression, and binary multivariate logistic regression were employed to screen variables and construct the prediction model.
  • Nomograms, calibration plots, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were used for visualization, calibration, and clinical efficacy assessment.

Main Results:

  • Eight optimal predictors were identified: birth weight, small for gestational age (SGA), hypertensive disease complicating pregnancy (HDCP), gestational diabetes mellitus (GDM), multiple births, cumulative duration of fasting, growth velocity, and postnatal corticosteroids.
  • The prediction model demonstrated strong performance with an area under the ROC curve of 83.1% in the training set and 84.6% in the external validation set.
  • The model showed good calibration and clinical utility across a wide risk threshold (0-95%) as indicated by DCA.

Conclusions:

  • The developed EUGR risk prediction model, incorporating key clinical variables, is effective for identifying very preterm infants at high risk.
  • The nomogram provides a user-friendly tool for clinicians to assess EUGR risk in real-time, facilitating timely management strategies.
  • This validated model can improve clinical decision-making and potentially enhance growth outcomes for very preterm infants.

Keywords:
Extrauterine growth retardationNomogramPredictionVery preterm infant

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