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首页> 外文期刊>Prostaglandins, Leukotrienes, and Essential Fatty Acids >Predicting the effect of maternal docosahexaenoic acid (DHA) supplementation to reduce early preterm birth in Australia and the United States using results of within country randomized controlled trials
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Predicting the effect of maternal docosahexaenoic acid (DHA) supplementation to reduce early preterm birth in Australia and the United States using results of within country randomized controlled trials

机译:使用国家内部随机对照试验的结果,预测母体二十二碳六烯酸(DHA)补充对减少澳大利亚和美国早产的影响

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摘要

The DHA to Optimize Mother Infant Outcome (DOMInO) and Kansas DHA Outcomes Study (KUDOS) were randomized controlled trials that supplemented mothers with 800 and 600 mg DHA/day, respectively, or a placebo during pregnancy. DOMInO was conducted in Australia and KUDOS in the United States. Both trials found an unanticipated and statistically significant reduction in early preterm birth (ePTB; i.e., birth before 34 weeks gestation). However, in each trial, the number of ePTBs were small. We used a novel Bayesian approach to estimate statistically derived low, moderate or high risk for ePTB, and to test for differences between the DHA and placebo groups. In both trials, the model predicted DHA would significantly reduce the expected proportion of deliveries in the high risk group under the trial conditions of the parent studies. Among the next 300,000 births in Australia we estimated that 1112 ePTB (95% credible interval 51-2189) could be avoided by providing DHA. And in the USA we estimated that 106,030 ePTB (95% credible interval 6400 to 175,700) could be avoided with DHA. (C) 2016 Elsevier Ltd. All rights reserved.
机译:优化母婴结局的DHA(DOMInO)和堪萨斯州DHA结局研究(KUDOS)是随机对照试验,分别为母亲每天补充800和600 mg DHA,或在孕期补充安慰剂。 DOMInO在澳大利亚进行,而KUDOS在美国进行。两项试验均发现早产(ePTB;即,妊娠34周之前的出生)出现了意料之外且统计学上显着的下降。但是,在每个试验中,ePTB的数量都很少。我们使用一种新颖的贝叶斯方法来估计ePTB在统计学上的低,中或高风险,并测试DHA和安慰剂组之间的差异。在两项试验中,模型均预测DHA将在母体研究的试验条件下显着降低高风险组中预期的分娩比例。在澳大利亚,接下来的300,000例出生中,我们估计通过提供DHA可以避免1112个ePTB(95%可信区间51-2189)。在美国,我们估计使用DHA可以避免106,030 ePTB(95%可信区间6400至175,700)。 (C)2016 Elsevier Ltd.保留所有权利。

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