Predicting death and survival without major morbidity for extremely preterm infants using information on hospital

Xincheng Cao1,2, Shujuan Li1,2, Xinyue Gu2

  • 1Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.

PubMed

Insights

Accurate prediction models for extremely preterm infants (EPIs) were developed using NICU admission data. These models predict mortality and survival without major morbidity, aiding clinical decisions.

Area of Science:

  • Neonatalogy
  • Medical Informatics
  • Predictive Analytics

Background:

  • Accurate prediction of outcomes for extremely preterm infants (EPIs) is crucial for clinical and parental decision-making.
  • Early prediction aids in managing care for vulnerable infants born between 24 and 28 weeks' gestation.

Purpose of the Study:

  • To develop and validate predictive models for mortality and survival without major morbidity in EPIs.
  • To utilize readily available information from neonatal intensive care unit (NICU) admission for prediction.

Main Methods:

  • Utilized two large contemporary cohorts of EPIs in China (development and validation).
  • Developed logistic regression models to predict mortality and survival without major morbidity.
  • Identified predictors including gestational age, birth weight, sex, antenatal steroids, 5-min Apgar score, and invasive ventilation on admission.

Main Results:

  • Mortality rate was 17.7% in the development cohort and 9.2% in the validation cohort.
  • Survival without major morbidity was 52.5% (development) and 59.1% (validation).
  • Mortality model AUC was 0.77 (development) and 0.76 (validation); survival model AUC was 0.72 (development) and 0.70 (validation).

Conclusions:

  • Successfully developed and validated two distinct models for predicting EPI outcomes.
  • Models use common NICU admission predictors, offering acceptable performance.
  • These tools can assist in managing extremely preterm infants.
Abstract

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