Data-Driven Phenotyping of Severe Traumatic Brain Injury

项目摘要 这个博士前奖学金将提供申请人（海莉福尔克），在系博士生 密歇根大学的计算医学和生物信息学，具备成为一名 独立研究调查员，在TBI机器学习的新应用方面具有专业知识。有限 当前模型早期预测GCS 3-8 TBI（通常称为重度TBI）结局的准确性 （预后模型）是改善GCS 3-8 TBI患者临床护理的主要障碍。小于 20%的GCS 3-8 TBI患者神经功能恢复良好，目前没有 改善长期结果的治疗剂。在GCS 3-8 TBI临床试验中， 代理，有利的结果通常被定义为比预期的结果更好，考虑到 考虑到每个病人的预测预后。因此，预测的准确估计 预后对于评估新治疗剂的功效是至关重要的。领先的预测模型 GCS 3-8 TBI、IMPACT（TBI临床试验预后和分析国际使命）和CRASH （严重头部损伤后皮质类固醇随机化），已经过广泛的外部验证， 然而，判别准确性高度依赖于队列，在某些患者中AUC低至0.60 组IMPACT和CRASH模型的两个主要局限性包括： 临床预测变量和基于回归的方法，其设计不用于处理复杂的， 多维数据集我们的目标是推导出一个动态预测模型， 随着新数据的出现，预测结果。然后，我们将开发一个聚类算法来识别 根据连续高频数据得出的GCS 3-8 TBI的生理学不同亚型（簇） 溪流我们提出的目标将通过以下具体目标来实现：1）我们将获得一个动态的 预测模型使用基于RNN（递归神经网络）的框架和第一次收集的数据， BOOST-2（严重创伤性脑损伤的脑氧优化：第2阶段） 每24小时提供更新的6个月结果预测; 2）使用时间序列分层 从入组BOOST-2的受试者中收集的生理参数的聚类和连续测量 在受伤后的前两周，我们将确定GCS 3-8 TBI的不同亚型，并检查 亚型与6个月结果之间的关系。在目标1中，我们假设我们的动态预后 从时变数据导出的模型将具有比静态预测模型更高的判别准确度（AUC）。 使用受伤当天收集的单个时间点数据得出的模型（类似于IMPACT和CRASH）。在 目的2，我们假设GCS 3-8 TBI的亚型以连续的生理参数为特征， 随着颅内压（ICP）升高，脑灌注压（CPP）降低， PbtO 2（脑组织氧合）将与6个月不良结局相关。

Project Summary This predoctoral fellowship will provide the applicant (Hayley Falk), a doctoral candidate in the Department of Computational Medicine & Bioinformatics at the University of Michigan, with the skills necessary to become an independent research investigator with expertise in novel applications of machine learning for TBI. The limited accuracy of current models for early prediction of GCS 3-8 TBI (commonly referred to as severe TBI) outcomes (prognostic models) is a major barrier to improving the clinical care of patients with GCS 3-8 TBI. Less than 20% of patients with GCS 3-8 TBI experience a good neurologic recovery and currently there are no therapeutic agents that improve long-term outcomes. In GCS 3-8 TBI clinical trials of promising therapeutic agents, a favorable outcome is typically defined as a better outcome than would be expected, taking into account the predicted prognosis for each individual patient. Therefore, accurate estimation of predicted prognosis is critical to assessing the efficacy of novel therapeutic agents. The leading prognostic models for GCS 3-8 TBI, IMPACT (International Mission for Prognosis and Analysis of Clinical Trials in TBI) and CRASH (Corticosteroid Randomization After Significant Head Injury), have undergone extensive external validation, however, the discriminative accuracy is highly cohort dependent with AUCs as low as 0.60 in some patient groups. The two major limitations of the IMPACT and CRASH models include one-time measurements of clinical predictor variables and regression-based methods, which are not designed to handle complex, multidimensional datasets. Our objective is to derive a dynamic prognostic model which provides updated outcome predictions as new data becomes available. We will then develop a clustering algorithm to identify physiologically distinct subtypes (clusters) of GCS 3-8 TBI derived from continuous, high frequency data streams. Our proposed goals will be achieved by the following specific aims: 1) we will derive a dynamic prognostic model using a RNN (recurrent neural network)-based framework and data collected during the first two weeks post-injury from BOOST-2 (Brain Oxygen Optimization in Severe Traumatic Brain Injury: Phase 2) which provides updated 6-month outcome predictions every 24 hours; and 2) using time series hierarchical clustering and continuous measures of physiologic parameters collected from subjects enrolled in BOOST-2 during the first two weeks post-injury, we will identify distinct subtypes of GCS 3-8 TBI and examine the association between subtype and 6-month outcome. In Aim 1, we hypothesize that our dynamic prognostic model derived from time-varying data will have a higher discriminative accuracy (AUC) than a static prognostic model (similar to IMPACT and CRASH) derived using single timepoint data collected on the day of injury. In Aim 2, we hypothesize that subtypes of GCS 3-8 TBI characterized by continuous physiologic parameters such as increasing ICP (intracranial pressure), decreasing CPP (cerebral perfusion pressure), and decreasing PbtO2 (brain tissue oxygenation) will be associated with poor 6-month outcomes.