基于患有慢性肺病的早产婴儿的氧气需求的诊断标签和集群:在两个独立队列中进行数据驱动的探索性集群分析
Damian Alvarez-Paggi1, Anhar Ullah2, Gaston Ofman3
1Fundacion Infant, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina; Centro Infant de Medicina Traslacional, Escuela de Bio y Nanotecnologías, Universidad Nacional de San Martín, San Martin Provincia de Buenos Aires, Argentina; Centro de Rediseño de Proteínas, Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Provincia de Buenos Aires, Argentina.
在早产婴儿中灵感氧气需求的轨迹确定了不同的肺部疾病模式. 这种数据驱动的方法可能能够更早地识别高风险的婴儿,以便进行精确的干预.
科学领域:
- 新生儿科学 新生儿科学
- 儿科肺病学 儿科肺病学
- 数据科学在医学中的数据科学
背景情况:
- 在出生时体重非常低的婴儿中,支气管肺功能障碍 (BPD) 诊断往往会延迟.
- 调查氧气需求轨迹可能会揭示明显的早产期肺病模式.
研究的目的:
- 为了确定从出生开始的吸氧 (FiO2) 轨迹是否反映出明显的早产期肺病集群.
- 为了更早地识别患重症肺病和死亡的高风险婴儿.
主要方法:
- 隐性类轨迹建模用于在前30天FiO2波动的集群分析.
- 对两组出生体重<1250克的新生儿的多中心前队列 (D-BPD和PROP) 进行了分析.
- 集群根据传统的BPD分类进行评估,以死亡率为主要结局.
主要成果:
- 确定了四个FiO2需求集群:持久低需求 (PLR),早期高与随后的改善 (EHRI),晚期高需求 (LOHR) 和持久高需求 (PHR).
- 在LOHR和PHR集群中,死亡率明显高 (p<0.0001).
- 结果在一个独立的队列中得到验证,显示了类似的集群模式和死亡风险.
结论:
- 目前的BPD定义可能不完全涵盖早产婴儿肺部疾病轨迹的范围.
- 数据驱动的氧气需求聚类为早期风险分层提供了一个有希望的方法.
- 这种方法可以为患有肺部疾病的早产婴儿提供及时和有针对性的干预措施.
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