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
bioRxiv. 2023 Jul 19:2023.07.17.547582. doi: 10.1101/2023.07.17.547582.
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 the 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 data sets.
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 an interactive dashboard for exploratory analysis and filtered data set download.
Compared with existing clinical and preclinical data management solutions, the presented framework accommodates heterogeneous data types (single measure, repeated measures, time series, and imaging), integrates data sets across various experimental models, and facilitates dynamic visualization of integrated data sets. We present a use case of this framework for predictive model development for intra-arrest prediction of cardiopulmonary resuscitation outcome.
The described preclinical data framework may serve as a template to aid in data management efforts in other translational research labs that generate heterogeneous data sets and require a dynamic platform that can easily evolve alongside their research.
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，且缺乏用于治疗继发性神经损伤的监测技术和治疗方法，小儿神经损伤和疾病成为一个关键的公共卫生问题。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，但由于缺乏用于管理、处理和分析多模态数据集的可用数据框架和标准而受到阻碍。
在此，我们展示了一个可推广的数据框架，该框架在费城儿童医院用于大型动物研究，以弥补这一技术差距。所展示的框架最终形成了一个用于探索性分析和筛选数据集下载的交互式仪表板。
与现有的临床和临床前数据管理解决方案相比，所展示的框架可容纳多种异构数据类型（单一测量、重复测量、时间序列和成像），整合各种实验模型中的数据集，并便于对整合后的数据集进行动态可视化。我们展示了该框架在开发用于心肺复苏结果的骤停期预测的预测模型中的一个应用案例。
所描述的临床前数据框架可作为一个模板，以协助其他产生异构数据集且需要一个能随研究轻松发展的动态平台的转化研究实验室进行数据管理工作。
