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[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.
Objectives:
To identify risk factors associated with feeding intolerance (FI) in preterm infants.
Methods:
Preterm infants treated in the neonatal unit of a hospital from August 2014 to January 2015 were recruited in this study. A clinical observation table was developed based on the reactive scope model. Data in relation to predictive homeostasis, reactive homeostasis, homeostatic overload, homeostatic failure and other aspects were collected and compared between those with and without FI.Alogistic regression model was established to determine predictors of FI.
Results:
1.A total of 207 preterm infants were included in the study: 125 male and 82 female. They had an average gestational age of (33.48±1.66) weeks (ranging from 27+2 to 37 weeks) and an average birth body mass of (2 019.55±334.38) g(ranging from 830 g to 3 120 g).2.The incidence of FI was 33.8%. FI in preterm infants often occurred during the period of being fed within 72 h.The main clinical manifestation of FI was gastric retentionin early-preterm infants and emesis in late-preterm infants.3.Gestational age, birth body mass, fetal distress, aminophylline application, intrauterine infection, breast milk feeding and interval between stools were associated with FI. Gestational age and birth body mass were found to be significant protectors of FI in the logistic regression model. FI declined with increased gestational age and birth body mass. Fetal distress, aminophylline application, and >3 d interval between stools were found to be significant risks of FI in the logistic regression model.4.The prediction model had a 92.73% forecast generation rate of return, with 97.14% sensitivity,88.32%specificity, and 91.30% accuracy.
Conclusions:
Low gestational age, low birth body mass, fetal distress,aminophylline application, and >3 d interval between stools are independent risk factors associate with FI. The prediction model can identify high risk cases of FI.
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