Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach.
Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach.
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心脏骤停中的精准护理：ICECAP (PRECICECAP) 研究方案和信息学方法。
DOI：
10.1007/s12028-022-01464-9
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发表时间：
2022-08
影响因子：
3.5
通讯作者：
中科院分区：
文献类型：
--
作者：
大多数重症监护试验都是中立的，部分原因是患者之间的异质性意味着并非所有患者对相同治疗的反应都相同。心脏骤停的精确护理- ICECAP （PRECICECAP）研究将把机器学习应用于从院外心脏骤停（OCHA）复苏的患者收集的高分辨率、多模态数据。我们的目标是发现新的生物标志物特征，以预测治疗性低温的最佳持续时间和90天的功能结果。同时，我们正在开发一个免费的软件平台，用于ML应用程序的icu采集数据的标准化管理。冷却时间对心脏骤停患者疗效的影响（ICECAP）研究是一项反应-适应性剂量寻找试验，测试不同时间的治疗性低温。12个ICECAP站点将从OHCA后常规使用的多种方式收集PRECICECAP数据，包括ICECAP病例报告表格、详细的用药数据、心肺和脑电图波形以及成像dicom。我们与Moberg Analytics合作开发了一个免费的软件平台，使高分辨率的重症监护数据能够得到有效的利用。我们将使用自编码器神经网络来创建所有原始波形和衍生特征的低维表示，在重新加热时进行审查，以确保临床可用性，以指导低温的最佳持续时间。我们还将考虑历史上被认为是重要的简单特性。最后，我们将创建一个有监督的深度学习神经网络算法，从大量新特征中直接预测90天的功能结果。PRECICECAP目前正在招生，将于2025年底完成。心脏骤停是一种异质性疾病，发病率和死亡率都很高。PRECICECAP将推进基于个人需求和治疗反应性的稳健测量的个性化神经危重症护理的总体目标。我们开发的软件平台将广泛适用于急性疾病或损伤后的医院研究。
Most trials in critical care have been neutral, in part because between-patient heterogeneity means not all patients respond identically to the same treatment. The PREcision Care In Cardiac arrest – ICECAP (PRECICECAP) study will apply machine learning to high-resolution, multimodality data collected from patients resuscitated from out-of-hospital cardiac arrest (OCHA). We aim to discover novel biomarker signatures to predict optimal duration of therapeutic hypothermia and 90-day functional outcomes. In parallel, we are developing a freely available software platform for standardized curation of ICU-acquired data for ML applications. The Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (ICECAP) study is a response-adaptive dose-finding trial testing different durations of therapeutic hypothermia. Twelve ICECAP sites will collect data for PRECICECAP from multiple modalities routinely used after OHCA, including ICECAP case report forms, detailed medication data, cardiopulmonary and electroencephalographic waveforms, and imaging DICOMs. We partnered with Moberg Analytics to develop a freely available software platform to allow high-resolution critical care data to be used efficiently and effectively. We will use an auto-encoder neural network to create low-dimensional representations of all raw waveforms and derivative features, censored at rewarming to ensure clinical usability to guide optimal duration of hypothermia. We will also consider simple features historically considered to be important. Finally, we will create a supervised deep learning neural network algorithm to directly predict 90-day functional outcome from large sets of novel features. PRECICECAP is currently enrolling and will be completed in late 2025. Cardiac arrest is a heterogeneous disease causing substantial morbidity and mortality. PRECICECAP will advance the overarching goal of titrating personalized neurocritical care based on robust measures of individual need and treatment responsiveness. The software platform we develop will be broadly applicable to hospital-based research after acute illness or injury.
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