Gaudio Hunter A, Padmanabhan Viveknarayanan, Landis William P, Silva Luiz E V, Slovis Julia, Starr Jonathan, Weeks M Katie, Widmann Nicholas J, Forti Rodrigo M, Laurent Gerard H, Ranieri Nicolina R, Mi Frank, Degani Rinat E, Hallowell Thomas, Delso Nile, Calkins Hannah, Dobrzynski Christiana, Haddad Sophie, Kao Shih-Han, Hwang Misun, Shi Lingyun, Baker Wesley B, Tsui Fuchiang, Morgan Ryan W, Kilbaugh Todd J, Ko Tiffany S
Resuscitation Science Center and Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
Translational Research Informatics Group, Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
Sci Rep. 2024 Dec 28;14(1):30710. doi: 10.1038/s41598-024-79973-0.
Pediatric neurological injury and disease is a critical public health issue due to increasing rates of survival from primary injuries (e.g., cardiac arrest, traumatic brain injury) and a lack of monitoring technologies and therapeutics for treatment of secondary neurological injury. Translational, preclinical research facilitates the development of solutions to address this growing issue but is hindered by a lack of available data frameworks and standards for the management, processing, and analysis of multimodal datasets. Here, we present a generalizable data framework that was implemented for large animal research at the Children's Hospital of Philadelphia to address this technological gap. The presented framework culminates in a custom, interactive dashboard for exploratory analysis and filtered dataset download. Compared with existing clinical and preclinical data management solutions, the presented framework better enables management of various data types (single measure, repeated measures, time series, and imaging), integration of datasets for comparison across experimental models, cohorts, and groups, and facilitation of predictive modeling from integrated datasets. Further, a predictive model development use case demonstrated utilization and value of the data framework. The general outline of a preclinical data framework presented here can serve as a template for other translational research labs that generate heterogeneous datasets and require a dynamic platform that can easily evolve alongside their research.
儿科神经损伤和疾病是一个关键的公共卫生问题，这是由于原发性损伤（如心脏骤停、创伤性脑损伤）的存活率不断提高，以及缺乏用于治疗继发性神经损伤的监测技术和治疗方法。转化性临床前研究有助于开发解决这一日益严重问题的方案，但由于缺乏用于多模态数据集管理、处理和分析的可用数据框架和标准而受到阻碍。在此，我们提出了一个可推广的数据框架，该框架已在费城儿童医院用于大型动物研究，以弥补这一技术差距。所提出的框架最终形成了一个定制的交互式仪表板，用于探索性分析和筛选数据集下载。与现有的临床和临床前数据管理解决方案相比，所提出的框架更能有效地管理各种数据类型（单一测量、重复测量、时间序列和成像），整合数据集以便在实验模型、队列和组之间进行比较，并促进从整合数据集中进行预测建模。此外，一个预测模型开发用例展示了该数据框架的实用性和价值。这里介绍的临床前数据框架的总体大纲可以作为其他生成异构数据集且需要一个能够随着研究轻松发展的动态平台的转化研究实验室的模板。
