Machine Learning to Optimize Management of Acute Hydrocephalus

机器学习优化急性脑积水的治疗

基本信息

批准号：
10639454
负责人：
Soojin Park
金额：
$ 70.6万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-15 至 2028-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10639454
关键词：
Acute Adopted Adoption Adverse event Automated Clinical Decision Support Blood Brain Brain Injuries Caring Case Report Form Cerebral hemisphere hemorrhage Cerebrospinal Fluid Cerebrospinal fluid shunts procedure Chronic Classification Clinical Clinical Data Clinical Decision Support Systems Closure by clamp Common Data Element Communities Dangerousness Data Data Science Data Set Decision Making Dependence Detection Diagnosis Drainage procedure Early Diagnosis Electrocardiogram Equipoise Excision Frequencies Future Harvest Hemorrhage Hospital Charges Hydrocephalus Image Infection Institution Intensive Care Units Intracranial Pressure Length of Stay Machine Learning Measurement Methods Modeling Morbidity - disease rate Morphology Outcome Output Patients Pattern Persons Physicians Physiological Process Readiness Research Risk Sampling Shunt Device Site Structure Subarachnoid Hemorrhage Surveys Testing Training Translating Validation Ventricular Weaning Work accurate diagnosis clinical diagnosis cost cranium diverse data federated learning high risk improved improved outcome infection risk intraventricular hemorrhage large datasets machine learning classifier machine learning model mortality open data pressure prospective prospective test radiological imaging tool

项目摘要

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.

项目摘要/摘要急性脑积水经常使包括脑内（ICH）和蛛网膜下腔的脑损伤复杂化出血（SAH），需要突发放置外部心室漏极（EVD）。 EVD允许迅速积累血液以退出，立即减轻危险的大脑压力。大多数患者都这样做此后不需要EVD，而18-30％的人会发展出慢性脑积水，需要永久性大脑流体（CSF）分流放置。整个中心的EVD管理有很大的差异，特别是关于何时断奶EVD以及测量和诊断与EVD相关的感染的方法。越长 EVD存在，EVD访问样本CSF（测试感染）的频率越高，风险越高对于有助于高发病率和死亡率的感染。 SAH和ICH患者忍受11.5-16的EVD 天（最大> 30），典型的心室发作发生在9.5天。这个恶性循环的成本隐藏了：每年在美国，每年37,000名患者在美国的急性脑积水中获得EVD，从而产生内部医院费用为每位患者151,672美元，每年56亿美元。非常需要优化EVD 通过识别与EVD相关的感染的管理，同时还要减少CSF采样并准确确定需要永久分类（或从临时排水中解放的能力），并尽早这样做为了最大程度地减少排水持续时间和住宿时间。我们的中心假设是有时间信息在数字化的患者数据中，反映了颅内动力学，可以收获以破坏阴性心室炎和分流依赖性的循环。在以前的工作中，我们发现颅内压波形形态学在临床诊断心室炎前两天变化。此外，我们确定了一个预测指标 EVD放置后四天，未来的CSF分流依赖射线照相型脑积水随并发的CSF引流量变化。我们旨在发展一个多中心用于管理急性脑积水的专用数据集，包括生理数据，例如颅内压力波形，成像和临床数据。使用此数据集，我们将能够改进和验证我们的用于检测心室炎和分流依赖性预测的机器学习模型。我们将利用模型概括性的数据输入的多样性，同时还通过使用A来识别和减少偏差用于模型培训和验证的联合学习框架。最后，我们将调查医生评估围绕EVD管理的决策并评估采用计算预测分数的开放性。