Machine Learning to Optimize Management of Acute Hydrocephalus

项目总结/摘要 急性脑积水经常并发脑损伤，包括脑内（ICH）和蛛网膜下腔 出血（SAH），需要紧急放置脑室外引流（EVD）。EVD允许快速 积聚的血液排出，立即缓解危险的大脑压力增加。大多数患者 在此之后不需要EVD，而18-30%的患者发生慢性脑积水并需要永久性脑脊液 液体（CSF）分流放置。各中心EVD的管理存在很大差异，特别是 关于何时放弃EVD以及调查和诊断EVD相关感染的方法。越长 存在EVD，EVD用于CSF样本（检测感染）的频率越高，风险越高 导致高发病率和死亡率的感染。SAH和ICH患者的EVD持续时间为11.5-16 天（最大> 30），典型的脑室炎发作发生在9.5天。这种恶性循环隐藏在成本中： 在美国，每年有37，000名患者在急性脑积水的情况下接受EVD，产生- 每位病人的医院费用为151，672美元，即每年56亿美元。非常需要优化EVD 通过识别EVD相关感染进行管理，同时减少CSF采样并准确确定 需要永久性分流（或能够从临时引流中解放出来），并尽早这样做 以尽量缩短引流时间和住院时间。我们的中心假设是存在时间信息 在反映颅内动力学的数字化患者数据中，可以收集这些数据以打破阴性 脑室炎和分流依赖性周期。在以前的工作中，我们发现颅内压波形， 在临床诊断脑室炎前两天的形态学变化。此外，我们发现了一个预测因子， 最早在EVD置入后4天，基于以下相关性， 影像学脑积水与并发CSF引流量的变化。我们的目标是建立一个多中心的 用于急性脑积水管理的专用数据集，包括生理数据，如颅内 压力波形、成像和临床数据。使用这个数据集，我们将能够改进和验证我们的 用于检测脑室炎和预测分流依赖性的机器学习模型。我们将利用 数据输入的多样性，同时还通过使用 用于模型训练和验证的联合学习框架。最后，我们将调查医生， 围绕EVD管理进行决策，并评估采用计算预测评分的开放性。

Project Summary/Abstract Acute hydrocephalus frequently complicates brain injury including intracerebral (ICH) and subarachnoid hemorrhage (SAH), requiring emergent placement of an external ventricular drain (EVD). The EVD allows rapidly accumulated blood to exit, immediately relieving dangerous increased pressure on the brain. Most patients do not need the EVD after this, while 18-30% develop chronic hydrocephalus and require permanent cerebrospinal fluid (CSF) shunt placement. There is great variability in the management of EVDs across centers, particularly about when to wean EVDs and the approach to surveying and diagnosing EVD-related infection. The longer the EVD is present and the more frequently the EVD is accessed to sample CSF (to test infection), the higher the risk for infection which contributes to high morbidity and mortality. SAH and ICH patients endure EVDs for 11.5-16 days (max > 30), with typical ventriculitis onset occurring at 9.5 days. This vicious cycle is hidden in the cost: 37,000 patients a year receive an EVD in the setting of acute hydrocephalus in the US annually, generating in- hospital charges of $151,672 per patient, or $5.6 billion dollars a year. There is a great need to optimize EVD management by recognizing EVD-related infection while reducing CSF sampling and accurately determining need for permanent shunting (or ability to liberate from temporary drainage), and to do so as early as possible to minimize duration of drainage and length of stay. Our central hypothesis is that there is temporal information in digitized patient data that is reflective of intracranial dynamics that can be harvested to break the negative cycle of ventriculitis and shunt dependency. In previous work, we discovered that intracranial pressure waveform morphology changes two days prior to the clinical diagnosis of ventriculitis. Additionally, we identified a predictor of future CSF shunt dependency as early as four days after EVD placement, building on the correlation of radiographic hydrocephalus changes with concurrent CSF drainage volume. We aim to develop a multicenter purpose-built dataset for the management of acute hydrocephalus including physiologic data such as intracranial pressure waveform, imaging, and clinical data. Using this dataset, we will be able to improve and validate our machine learning models for detection of ventriculitis and prediction of shunt dependence. We will leverage the diversity of the data inputs for model generalizability while also identifying and reducing bias by using a Federated Learning framework for model training and validation. Finally, we will survey physicians to evaluate decision making around EVD management and assess openness to adopting computed prediction scores.