[Risk Prediction of Feeding Intolerance in Preterm Infants]

Qiong Chen1, Jin-Bo Fang2, Wen-Tao Peng3

  • 1Department of Neonatology, West China Second University Hospital, Sichuan University, Key Laboratory of Birth Defects and Related Disease of Women and Children (Sichuan University),Ministry of Education,Chengdu 610041,China.

Insights

Feeding intolerance (FI) in preterm infants is linked to low gestational age, low birth weight, fetal distress, and certain medications. Early identification of high-risk infants is crucial for better outcomes.

Area of Science:

  • Neonatology
  • Pediatric Gastroenterology
  • Perinatal Medicine

Background:

  • Feeding intolerance (FI) is a common complication in preterm infants, impacting growth and increasing morbidity.
  • Identifying reliable risk factors for FI is essential for timely intervention and improved neonatal care.

Purpose of the Study:

  • To determine the independent risk factors associated with feeding intolerance in preterm infants.
  • To develop a predictive model for identifying preterm infants at high risk of developing feeding intolerance.

Main Methods:

  • A cohort of 207 preterm infants was studied between August 2014 and January 2015.
  • Data on homeostatic factors were collected, and logistic regression analysis was used to identify predictors of FI.
  • A prediction model was developed and validated for its forecasting accuracy, sensitivity, specificity, and overall accuracy.

Main Results:

  • The incidence of feeding intolerance was 33.8%, often occurring within 72 hours of feeding initiation.
  • Key risk factors identified included fetal distress, aminophylline use, and an interval of over 3 days between stools.
  • Increased gestational age and birth body mass were protective factors against feeding intolerance.

Conclusions:

  • Low gestational age, low birth body mass, fetal distress, aminophylline administration, and prolonged intervals between stools are significant independent risk factors for feeding intolerance in preterm infants.
  • The developed prediction model demonstrated high accuracy in identifying infants at risk for feeding intolerance, with a 91.30% accuracy rate.
Abstract

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