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Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children

机译:在严重疾病儿童中使用时间趋势对多器官功能不全综合征进行表型分型

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Multiple organ dysfunction syndrome (MODS) is one of the most common causes of death in critically ill children. However, despite decades of clinical trials, there are no comprehensive approaches to the management of MODS or effective targeted therapies that have consistently improved outcomes. Better understanding the heterogeneity of MODS and characterizing subgroups of MODS patients could improve our understanding of the syndrome and help us develop new management strategies. We analyzed a cohort of 5,297 children with MODS from two children's hospitals and used subgraph-augmented non-negative matrix factorization (SANMF) to identify unique temporal patterns in organ dysfunction across four novel subgroups. We demonstrate that these subgroups are composed of patients with distinct clinical characteristics and are independently predictive of clinical outcomes. Our work suggests that these subgroups represent four relevant phenotypes of pediatric MODS that could be used to identify novel management strategies.
机译:多器官功能障碍综合症(MODS)是重症儿童最常见的死亡原因之一。然而,尽管进行了数十年的临床试验,但仍没有全面的方法来治疗MODS或有效靶向治疗,其疗效一直得到改善。更好地了解MODS的异质性并确定MODS患者亚组的特征可以增进我们对综合征的了解,并帮助我们制定新的治疗策略。我们分析了来自两家儿童医院的5297名患有MODS的儿童,并使用子图增强型非负矩阵分解(SANMF)来识别四个新亚组中器官功能异常的独特时空模式。我们证明,这些亚组由具有不同临床特征的患者组成,并且可以独立预测临床结果。我们的工作表明,这些亚组代表了小儿MODS的四种相关表型,可用于确定新的治疗策略。

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