Section of Neonatal-Perinatal Medicine, Department of Pediatrics, Thomas Jefferson University College of Medicine, Nemours/Alfred I. DuPont Hospital for Children , Philadelphia , PA , USA.
Section of Neonatal-Perinatal Medicine, Department of Pediatrics, Drexel University College of Medicine, St. Christopher's Hospital for Children , Philadelphia , PA , USA.
Expert Rev Respir Med. 2019 Sep;13(9):871-884. doi: 10.1080/17476348.2019.1648215. Epub 2019 Aug 4.
: Bronchopulmonary dysplasia (BPD) is the most common serious pulmonary morbidity in premature infants. Despite ongoing advances in neonatal care, the incidence of BPD has not improved. A potential explanation for this phenomenon is the limited ability for accurate early prediction of the risk of BPD. BPD continues to represent a therapeutic challenge and no single effective therapy exists for this condition. : Here, we review risk factors of BPD derived from clinical data, biological fluid biomarkers, respiratory management data, and scientific advancements using 'omics' technologies, and their ability to predict the pathogenesis of BPD in preterm neonates. Risk factors and biomarkers were identified via literature search with a focus on the last 5 years of data. : The most accurate predictive tools utilize risk factors that encompass a variety of categories. Numerous predictive models have been proposed but suffer from a lack of adequate validation. An ideal model should include multiple, easily measurable variables validated across a heterogeneous population. In addition to evaluating recent BPD prediction models, we suggest approaches to enhance future models.
支气管肺发育不良（BPD）是早产儿最常见的严重肺部疾病。尽管新生儿护理不断取得进展，但 BPD 的发病率并未改善。造成这种现象的一个潜在原因是，BPD 风险的准确早期预测能力有限。BPD 仍然是一个治疗挑战，目前尚无针对这种疾病的单一有效治疗方法。在这里，我们回顾了源自临床数据、生物体液生物标志物、呼吸管理数据和使用“组学”技术的科学进展的 BPD 风险因素，以及它们预测早产儿 BPD 发病机制的能力。通过文献检索确定了风险因素和生物标志物，重点关注过去 5 年的数据。最准确的预测工具利用包含多种类别的风险因素。已经提出了许多预测模型，但缺乏充分的验证。理想的模型应包括经过验证的多个易于测量的变量，这些变量在异质人群中具有一致性。除了评估最近的 BPD 预测模型外，我们还提出了增强未来模型的方法。
