California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA.
Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, USA.
Pediatr Res. 2021 May;89(6):1405-1413. doi: 10.1038/s41390-020-01148-0. Epub 2020 Oct 1.
Identifying preterm infants at risk for mortality or major morbidity traditionally relies on gestational age, birth weight, and other clinical characteristics that offer underwhelming utility. We sought to determine whether a newborn metabolic vulnerability profile at birth can be used to evaluate risk for neonatal mortality and major morbidity in preterm infants.
This was a population-based retrospective cohort study of preterm infants born between 2005 and 2011 in California. We created a newborn metabolic vulnerability profile wherein maternal/infant characteristics along with routine newborn screening metabolites were evaluated for their association with neonatal mortality or major morbidity.
Nine thousand six hundred and thirty-nine (9.2%) preterm infants experienced mortality or at least one complication. Six characteristics and 19 metabolites were included in the final metabolic vulnerability model. The model demonstrated exceptional performance for the composite outcome of mortality or any major morbidity (AUC 0.923 (95% CI: 0.917-0.929). Performance was maintained across mortality and morbidity subgroups (AUCs 0.893-0.979).
Metabolites measured as part of routine newborn screening can be used to create a metabolic vulnerability profile. These findings lay the foundation for targeted clinical monitoring and further investigation of biological pathways that may increase the risk of neonatal death or major complications in infants born preterm.
We built a newborn metabolic vulnerability profile that could identify preterm infants at risk for major morbidity and mortality. Identifying high-risk infants by this method is novel to the field and outperforms models currently in use that rely primarily on infant characteristics. Utilizing the newborn metabolic vulnerability profile for precision clinical monitoring and targeted investigation of etiologic pathways could lead to reductions in the incidence and severity of major morbidities associated with preterm birth.
传统上，识别有死亡或主要并发症风险的早产儿依赖于胎龄、出生体重和其他临床特征，但这些特征的实用性有限。我们试图确定新生儿出生时的代谢脆弱性特征是否可用于评估早产儿的新生儿死亡率和主要并发症风险。
这是一项基于人群的回顾性队列研究，纳入了 2005 年至 2011 年期间在加利福尼亚州出生的早产儿。我们创建了一个新生儿代谢脆弱性特征，其中评估了母亲/婴儿特征以及常规新生儿筛查代谢物与新生儿死亡率或主要并发症的相关性。
9639 名（9.2%）早产儿经历了死亡或至少一种并发症。最终代谢脆弱性模型纳入了 6 个特征和 19 个代谢物。该模型对死亡率或任何主要并发症的复合结局具有出色的表现（AUC 0.923（95%CI：0.917-0.929）。在死亡率和发病率亚组中，该模型的性能也得以维持（AUC 0.893-0.979）。
作为常规新生儿筛查一部分测量的代谢物可用于创建代谢脆弱性特征。这些发现为有针对性的临床监测和进一步研究可能增加早产儿死亡或主要并发症风险的生物学途径奠定了基础。
我们构建了一个新生儿代谢脆弱性特征，可以识别有主要并发症和死亡率风险的早产儿。这种方法识别高危婴儿在该领域是新颖的，优于目前主要依赖婴儿特征的模型。利用新生儿代谢脆弱性特征进行精确的临床监测和对病因途径的针对性研究，可能会降低与早产相关的主要并发症的发生率和严重程度。
