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

• 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
• 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

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名患者在急性脑积水的情况下接受脑室体外引流（EVD）， 在美国，每位患者的住院费用为151，672美元，即每年56亿美元。有很大 神经重症监护病房优化EVD管理以降低感染率的动机， 准确地确定永久分流的需要，并有效地这样做，以最大限度地减少 引流和住院时间（LOS）。脑室炎的风险因素包括EVD持续时间、脑脊液 (CSF)采样频率、是否存在脑室内出血（IVH）和插入技术。严重 IVH和EVD患者的CSF紊乱限制了常规CSF分析对脑室炎的价值 预测.脑室炎的诊断是不准确的，只有少数人宣布文化阳性，而所有仍然 需要抗生素治疗和永久性分流的延迟。这导致不必要的经验性抗生素 治疗和增加的LOS（30.8 vs 22.6天），相关费用（30，335美元以上）和发病率 (e.g.艰难梭菌感染，出现耐药病理）。的确定过程 永久性分流依赖在机构之间是可变的，特别是在决定何时 开始撤机EVD或预测延迟消退。亚急性期的这些决定决定 LOS和相关不良事件、放射学暴露以及可能不必要的 CNS中的永久性异物，从而带来感染和堵塞的终身风险。有 没有准确的非侵入性测试（不会进一步引入感染）来诊断脑室炎， 是否有一种及时的方法来预测急性脑积水后是否需要永久性分流。填补这一 间隙，我们建议开发一个定量模型，从颅内压（ICP）波形分析， 提高脑室炎诊断的精确度，并准确预测是否需要永久性分流。在 在以前的工作中，我们能够很准确地预测谁需要永久性分流安置 使用在24小时钳夹试验期间收集的ICP波形分析。然而，一个复杂的模型只能 如果它比传统的临床方法更早或更准确地实现诊断，则是合理的。初步 工作中，我们聚集了原始ICP波形，并发现了一种特定于脑室炎的波形模式， 在发送诊断培养物前1天出现。我们的中心假设是， 反映颅内动力学的ICP波形中的定量信号，可采集以优化急性 脑积水管理。影响和意义：基于ICP的无创定量模型 诊断脑室炎并准确预测永久性分流需要波形分析 减少EVD的持续时间和CSF采样的频率，这是脑室炎的两个风险因素， 同时也减少了LOS、ICU住院相关不良事件和经验性抗生素。

项目成果

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

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。

• DOI: 10.1016/j.ccc.2022.07.008
• 发表时间: 2023
• 期刊: Critical care clinics
• 影响因子: 4.3
• 作者: Mainali,Shraddha;Park,Soojin
• 通讯作者: Park,Soojin

Convexity subarachnoid haemorrhage - Authors' reply.

凸性蛛网膜下腔出血 - 作者的回复。

• DOI: 10.1016/s0140-6736(23)00007-7
• 发表时间: 2023
• 期刊: Lancet (London, England)
• 作者: Claassen,Jan;Park,Soojin
• 通讯作者: Park,Soojin