Time to full enteral feeds in hospitalised preterm and very low birth weight infants in Nigeria and Kenya.

PloS one(2024)

引用 0|浏览11
暂无评分
摘要
BACKGROUND:Preterm (born < 37 weeks' gestation) and very low birthweight (VLBW; <1.5kg) infants are at the greatest risk of morbidity and mortality within the first 28 days of life. Establishing full enteral feeds is a vital aspect of their clinical care. Evidence predominantly from high income countries shows that early and rapid advancement of feeds is safe and reduces length of hospital stay and adverse health outcomes. However, there are limited data on feeding practices and factors that influence the attainment of full enteral feeds among these vulnerable infants in sub-Saharan Africa. AIM:To identify factors that influence the time to full enteral feeds, defined as tolerance of 120ml/kg/day, in hospitalised preterm and VLBW infants in neonatal units in two sub-Saharan African countries. METHODS:Demographic and clinical variables were collected for newborns admitted to 7 neonatal units in Nigeria and Kenya over 6-months. Multiple linear regression analysis was conducted to identify factors independently associated with time to full enteral feeds. RESULTS:Of the 2280 newborn infants admitted, 484 were preterm and VLBW. Overall, 222/484 (45.8%) infants died with over half of the deaths (136/222; 61.7%) occurring before the first feed. The median (inter-quartile range) time to first feed was 46 (27, 72) hours of life and time to full enteral feeds (tFEF) was 8 (4.5,12) days with marked variation between neonatal units. Independent predictors of tFEF were time to first feed (unstandardised coefficient B 1.69; 95% CI 1.11 to 2.26; p value <0.001), gestational age (1.77; 0.72 to 2.81; <0.001), the occurrence of respiratory distress (-1.89; -3.50 to -0.79; <0.002) and necrotising enterocolitis (4.31; 1.00 to 7.62; <0.011). CONCLUSION:The use of standardised feeding guidelines may decrease variations in clinical practice, shorten tFEF and thereby improve preterm and VLBW outcomes.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要