Singh Avantika, Arya Sugandha, Chellani Harish, Aggarwal K C, Pandey R M
Division of Neonatology, Department of Pediatrics, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, 110029, India.
Indian J Pediatr. 2014 Jan;81(1):24-8. doi: 10.1007/s12098-013-1161-1. Epub 2013 Aug 16.
To evaluate the factors associated with low birth weight (LBW) and to formulate a scale to predict the probability of having a LBW infant.
This hospital based case-control study was conducted in a tertiary care university hospital in North India. The study included 250 LBW neonates and 250 neonates with birth weight ≥2,500 g. Data were collected by interviewing mothers using pre-designed structured questionnaire and from hospital records.
Factors significantly associated with LBW were inadequate weight gain by the mother during pregnancy (<8.9 kg), inadequate proteins in diet (<47 g/d), previous preterm baby, previous LBW baby, anemic mother and passive smoking. The prediction model made on these six variables has a sensitivity of 71.6 %, specificity 67.0 %, positive LR 2.17 and negative LR of 0.42 for a cut-off score of ≥29.25. On validation, it has a sensitivity of 72 % and specificity of 64 %.
It is possible to predict LBW using a prediction model based on significant risk factors associated with LBW.
评估与低出生体重（LBW）相关的因素，并制定一个量表来预测生出低出生体重婴儿的概率。
这项基于医院的病例对照研究在印度北部一家三级护理大学医院开展。该研究纳入了250名低出生体重新生儿和250名出生体重≥2500克的新生儿。通过使用预先设计的结构化问卷对母亲进行访谈并从医院记录中收集数据。
与低出生体重显著相关的因素包括母亲孕期体重增加不足（<8.9千克）、饮食中蛋白质不足（<47克/天）、既往早产婴儿、既往低出生体重婴儿、贫血母亲和被动吸烟。基于这六个变量建立的预测模型，对于≥29.25的截断分数，其灵敏度为71.6%，特异度为67.0%，阳性似然比为2.17，阴性似然比为0.42。在验证时，其灵敏度为72%，特异度为64%。
使用基于与低出生体重相关的显著危险因素的预测模型来预测低出生体重是可行的。
