Machine Learning to Optimize Management of Acute Hydrocephalus Patients

机器学习优化急性脑积水患者的管理

基本信息

批准号：
10057040
负责人：
Soojin Park
金额：
$ 44.55万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10057040
关键词：
Acute Adverse event Antibiotic Therapy Antibiotics Caring Cerebral hemisphere hemorrhage Cerebrospinal Fluid Cerebrospinal Fluid Proteins Clinical Clostridium difficile Closure by clamp Complex Data Dependence Diagnosis Diagnostic Diagnostic radiologic examination Drainage procedure Drug resistance Early Diagnosis Exposure to Frequencies Harvest Healthcare Hospital Charges Hour Hydrocephalus Infection Inflammatory Information Retrieval Institution Intracranial Pressure Learning Length of Stay Machine Learning Methods Minority Modeling Morbidity - disease rate Morphology Motivation Natural Language Processing Neuraxis Neurosurgeon Output Pathology Patients Pattern Process Resolution Risk Risk Factors Sampling Shunt Device Signal Transduction Subarachnoid Hemorrhage Techniques Testing Time Translating Ventricular Weaning Work base cost improved infection rate infection risk intraventricular hemorrhage long short term memory recurrent neural network vector

项目摘要

37,000 patients a year receive an external ventricular drain (EVD) in the setting of acute hydrocephalus in the US, generating in-hospital charges of $151,672 per patient, or $5.6 billion dollars a year. There is great motivation in the neurointensive care unit for the optimization of EVD management to reduce infection rates, accurately determine need for permanent shunting, and to do so efficiently in order to minimize duration of drainage and length of stay (LOS). Risk factors for ventriculitis include EVD duration, cerebrospinal fluid (CSF) sampling frequency, presence of intraventricular hemorrhage (IVH), and insertion technique. Severe CSF disturbances in patients with IVH and EVDs limit the value of routine CSF analysis for ventriculitis prediction. And ventriculitis diagnosis is imprecise, with only a minority declaring culture positivity while all still demanding antibiotic treatment and delay of permanent shunt. This leads to unnecessary empiric antibiotic treatment and increased LOS (30.8 vs 22.6 days), with the associated cost ($30,335 more) and morbidity (e.g. Clostridium difficile infection, emergence of drug-resistant pathology). The process of determining permanent shunt dependence is variable between institutions, particularly around the decision of when to begin weaning the EVD or predicting delayed resolution. These decisions in the subacute period determine LOS and associated adverse events, exposure to radiography, and commitment to potentially unnecessary permanent foreign materials in the CNS, which then carry lifelong risks for infection and blockage. There is no accurate noninvasive test (that does not further introduce infection) to diagnose ventriculitis nor is there a timely method to predict need for permanent shunt after acute hydrocephalus. To fill this gap, we propose developing a quantitative model from intracranial pressure (ICP) waveform analysis to increase precision in the diagnosis of ventriculitis and accurately predict need for permanent shunt. In previous work, we were able to predict with good accuracy who would need permanent shunt placement using ICP waveform analysis collected during a 24 hour clamp trial. However, a complex model can only be justified if it achieves a diagnosis earlier or more accurately than traditional clinical methods. In preliminary work, we clustered raw ICP waveforms and found a pattern of waveforms specific for ventriculitis that appears 1 day before diagnostic cultures are sent. Our central hypothesis is that there is a temporal quantitative signal in ICP waveform reflective of intracranial dynamics that can be harvested to optimize acute hydrocephalus management. Impact and Significance: Noninvasive quantitative models based on ICP waveform analysis that diagnose ventriculitis and accurately predict need for permanent shunt would decrease the duration of EVD and the frequency of CSF sampling, two of the risk factors for ventriculitis, while also decreasing LOS, associated adverse events of ICU stay, and empiric antibiotics.

每年有 37,000 名急性脑积水患者接受脑室外引流术 (EVD) 美国，每位患者的住院费用为 151,672 美元，即每年 56 亿美元。有很棒的神经重症监护病房优化埃博拉病毒病管理以降低感染率的动机，准确确定永久分流的需要，并有效地这样做，以尽量减少持续时间排水量和停留时间 (LOS)。脑室炎的危险因素包括 EVD 持续时间、脑脊液 (CSF) 采样频率、脑室内出血 (IVH) 的存在以及插入技术。严重 IVH 和 EVD 患者的脑脊液紊乱限制了脑室炎常规脑脊液分析的价值预言。脑室炎的诊断并不精确，只有少数人宣称培养呈阳性，而所有人仍然存在需要抗生素治疗并延迟永久性分流。这导致不必要的经验性抗生素治疗和 LOS 增加（30.8 天 vs 22.6 天），以及相关费用（多 30,335 美元）和发病率（例如艰难梭菌感染、耐药病理的出现）。确定的过程永久性分流依赖性在不同机构之间存在差异，特别是在决定何时进行分流时开始断奶 EVD 或预测延迟缓解。亚急性期的这些决定决定了 LOS 和相关不良事件、射线照相暴露以及对潜在不必要的承诺中枢神经系统中存在永久性异物，从而带来终生感染和阻塞的风险。有没有准确的无创测试（不会进一步引入感染）来诊断脑室炎，也没有是否有及时的方法来预测急性脑积水后是否需要永久性分流？为了填补这个差距，我们建议开发一个从颅内压（ICP）波形分析到提高脑室炎诊断的精确度并准确预测永久性分流的需要。在在之前的工作中，我们能够非常准确地预测谁需要永久性分流器放置使用 24 小时钳夹试验期间收集的 ICP 波形分析。然而，复杂的模型只能如果它比传统临床方法更早或更准确地实现诊断，那么它就是合理的。在初步在工作中，我们对原始 ICP 波形进行了聚类，发现了脑室炎特有的波形模式，在发送诊断培养物前 1 天出现。我们的中心假设是存在一个时间 ICP 波形中的定量信号反映了颅内动态，可以采集这些信号来优化急性颅内压脑积水管理。影响与意义：基于ICP的无创定量模型诊断脑室炎并准确预测是否需要永久性分流的波形分析将减少埃博拉病毒病的持续时间和脑脊液采样的频率，这是脑室炎的两个危险因素，同时还减少 LOS、ICU 住院相关不良事件以及经验性抗生素的使用。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Optimal Cerebral Perfusion Pressure During Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage.

动脉瘤性蛛网膜下腔出血后迟发性脑缺血期间的最佳脑灌注压。

DOI：
10.1097/ccm.0000000000005396
发表时间：
2022
期刊：
Critical care medicine
影响因子：
8.8
作者：
Weiss,Miriam;Albanna,Walid;Conzen,Catharina;Megjhani,Murad;Tas,Jeanette;Seyfried,Katharina;Kastenholz,Nick;Veldeman,Michael;Schmidt,TobiasPhilip;Schulze-Steinen,Henna;Wiesmann,Martin;Clusmann,Hans;Park,Soojin;Aries,Marcel;Schu
通讯作者：
Schu

Predicting Shunt Dependency from the Effect of Cerebrospinal Fluid Drainage on Ventricular Size.

DOI：
10.1007/s12028-022-01538-8
发表时间：
2022-12
期刊：
Neurocritical care
影响因子：
3.5
作者：
通讯作者：

Cognitive-motor dissociation and time to functional recovery in patients with acute brain injury in the USA: a prospective observational cohort study.

DOI：
10.1016/s1474-4422(22)00212-5
发表时间：
2022-08
期刊：
LANCET NEUROLOGY
影响因子：
48
作者：
Egbebike, Jennifer;Shen, Qi;Doyle, Kevin;Der-Nigoghossian, Caroline A.;Panicker, Lucy;Gonzales, Ian Jerome;Grobois, Lauren;Carmona, Jerina C.;Vrosgou, Athina;Kaur, Arshnell;Boehme, Amelia;Velazquez, Angela;Rohaut, Benjamin;Roh, David;Agarwal, Sachin;Park, Soojin;Connolly, E. Sander;Claassen, Jan
通讯作者：
Claassen, Jan

Artificial Intelligence and Big Data Science in Neurocritical Care.

神经重症监护中的人工智能和大数据科学。