Hemostasis, Hematoma Expansion, and Outcomes After Intracerebral Hemorrhage

脑出血后的止血、血肿扩张和结果

基本信息

批准号：
10598712
负责人：
ANDREW M NAIDECH
金额：
$ 30.01万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10598712
关键词：
Accountability Acute Acute Disease Address Affect Aging Algorithms American Americas Anticoagulants Artificial Intelligence Award Biological Markers Blood coagulation Brain Caregivers Caring Catchment Area Cerebral hemisphere hemorrhage Cessation of life Clinical Clinical Data Consumption Data Data Set Data Sources Decision Making Development Diagnosis Diagnostic Disease Disease model Electronic Health Record Ethical Issues Ethics Ethnic Origin Event Exposure to Family member Fever Fright Future Gastrostomy Geography Growth Hand Health Hematoma Hemorrhage Hemostatic Agents Hemostatic function Hour Hypertension Informatics Ingestion Interview Lead Life Location Machine Learning Measures Medical Methods Minority Modeling Myocardial Infarction Neurology Nurses Outcome Patient Care Patient-Focused Outcomes Patients Performance Pharmaceutical Preparations Pharmacists Physicians Population Heterogeneity Prevalence Process Qualitative Research ROC Curve Race Recommendation Research Resources Rural Savings Scanning Seizures Severities Status Epilepticus Stroke Structure Symptoms Testing Therapeutic Thrombosis Time Training Travel Treatment outcome Trust United States National Institutes of Health Vascular Diseases Vulnerable Populations Work X-Ray Computed Tomography adverse event risk artificial intelligence algorithm base brain tissue clinical practice clopidogrel cranium disability diverse data follow-up health care availability health care service health related quality of life high risk model development neuroimaging patient population prediction algorithm prevent prothrombin complex concentrates racial difference risk minimization rural area sex side effect suburb

项目摘要

PROJECT SUMMARY Intracerebral hemorrhage (ICH), bleeding into brain tissue, is often disabling or deadly. Complications of ICH compound the impact on patient outcome. Hematoma expansion is growth of the hematoma from the first (diagnostic) computed tomography (CT) scan to a follow-up CT scan, and occurs in 10-25% of patients. Reducing hematoma expansion reduces death and disability by keeping small hematomas small. Otherwise, hematoma expansion will compress brain tissue in the limited space in the skull. Hemostatic medication (e.g., prothrombin complex concentrate) is effective in reducing hematoma expansion in patients at high risk for hematoma expansion. However, our ability to predict hematoma expansion and select patients for hemostatic medication is limited. Unfortunately, hemostatic medication has potential side effects, such as myocardial infarction. Separately, seizures occur in about 10% of patients and may progress to status epilepticus (continual seizures). Unfortunately, a practice of widespread seizure medications also leads to complications, worse patient outcomes, and reduced health-related quality of life at follow-up. Patient care is harmed because we cannot select patients for potentially life-saving treatments (hemostatic treatment, seizure medications). ML/AI provide a solution to these roadblocks. We have developed ML/AI algorithms to solve these roadblocks of targeting hemostatic treatment and seizure medications in the context of an ongoing award to predict hematoma expansion using ML of multiple measures of blood clotting (biomarkers of hemostasis). However, significant ethical issues remain unresolved. ML/AI algorithms may be subject to unforeseen bias in the patient populations from which they are developed. Bias may lead to different performance in patients considered vulnerable by the NIH (e.g., minority race, rural location, access to healthcare services). Previous qualitative interviews with clinicians (nurses, pharmacists, physicians) have revealed deep ambivalence about the use of ML/AI. There is excitement about consistent treatment recommendations and availability at all hours, but also concerns about bias, accountability, and oversight. Such qualitative research has not been performed in the setting of ICH, which requires emergent decision making because hematoma expansion and seizures occur within hours of ICH symptom onset. In the context of our ongoing award, we will determine if preliminary ML/AI algorithms we have developed have different performance in vulnerable patients; if needed, we will develop methods to correct it, assisted by diverse data sources in hand and established predictors of hematoma expansion and seizures. We will perform qualitative interviews with clinicians, patients, and family members affected by ICH to identify and address concerns that must be addressed if ML/AI is to be trusted in ICH. This proposal will serve as a model for other acute diseases that require emergent decision making.

项目概要脑出血（ICH），即脑组织出血，通常会导致致残或致命。脑出血的并发症加剧对患者治疗结果的影响。血肿扩张是血肿从最初开始的增长（诊断）计算机断层扫描 (CT) 扫描到后续 CT 扫描，发生在 10-25% 的患者中。减少血肿扩张可以使小血肿保持较小，从而减少死亡和残疾。否则，血肿扩张会压迫颅骨有限空间内的脑组织。止血药物（例如，凝血酶原复合物浓缩物）可有效减少高危患者的血肿扩张血肿扩张。然而，我们预测血肿扩大和选择患者进行止血的能力药物治疗是有限的。不幸的是，止血药物有潜在的副作用，例如心肌损伤梗塞。另外，约 10% 的患者会发生癫痫发作，并可能进展为癫痫持续状态（持续癫痫发作）。不幸的是，广泛使用癫痫药物也会导致并发症，患者预后较差，随访时健康相关生活质量下降。患者护理受到损害，因为我们无法选择患者接受可能挽救生命的治疗（止血治疗、癫痫药物）。机器学习/人工智能为这些障碍提供了解决方案。我们开发了 ML/AI 算法来解决靶向止血治疗和癫痫发作的这些障碍正在获奖的背景下使用多种措施的机器学习来预测血肿扩张血液凝固（止血的生物标志物）。然而，重大的道德问题仍未解决。机器学习/人工智能算法可能会受到其开发的患者群体中不可预见的偏差的影响。偏见可能会导致 NIH 认为易受伤害的患者（例如少数民族、农村患者）表现出不同的表现位置、获得医疗保健服务的机会）。之前对临床医生（护士、药剂师、医生）对机器学习/人工智能的使用表现出深深的矛盾心理。一致令人兴奋全天候提供治疗建议和可用性，但也担心偏见、问责制和监督。此类定性研究尚未在 ICH 背景下进行，这需要紧急的由于血肿扩张和癫痫发作发生在 ICH 症状出现后数小时内，因此需要做出决策。在我们持续获奖的背景下，我们将确定我们开发的初步 ML/AI 算法是否具有弱势患者的不同表现；如果需要，我们将在以下人员的协助下制定纠正方法：现有不同的数据来源以及血肿扩大和癫痫发作的既定预测因素。我们将表演对受 ICH 影响的临床医生、患者和家庭成员进行定性访谈，以识别和解决问题如果要让 ICH 信任 ML/AI，就必须解决这些问题。该提案将作为其他提案的典范需要紧急决策的急性疾病。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Prothrombin Complex Concentrate for Emergent Reversal of Intracranial Hemorrhage in Patients with Ventricular Assist Devices.

凝血酶原复合物浓缩物用于紧急逆转心室辅助装置患者的颅内出血。

DOI：
发表时间：
2021
期刊：
影响因子：
3.5
作者：
Lai, Grace Y;Maas, Matthew B;Leong, Christopher R;Liotta, Eric M;Rich, Jonathan D;Pham, Duc T;Vorovich, Esther E;Naidech, Andrew M;Jahromi, Babak S;Potts, Matthew B
通讯作者：
Potts, Matthew B

P2Y12 inhibitor use predicts hematoma expansion in patients with intracerebral hemorrhage.

P2Y12 抑制剂的使用可预测脑出血患者的血肿扩大。

DOI：
发表时间：
2024-04-23
期刊：
影响因子：
0
作者：
Houskamp, Ethan J;Liu, Yuzhe;Silva Pinheiro do Nascimento, Juliana;Jahromi, Babak S;Lindholm, Paul F;Kwaan, Hau C;Naidech, Andrew M
通讯作者：
Naidech, Andrew M

Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing.

使用机器学习和自然语言处理自动进行缺血性中风亚型分类。

DOI：
10.1016/j.jstrokecerebrovasdis.2019.02.004
发表时间：
2019-07-01
期刊：
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
影响因子：
0
作者：
Ravi Garg;Elissa Oh;A. Naidech;Konrad Paul Kording;S. Prabhakaran
通讯作者：
S. Prabhakaran

Seizure frequency in patients with isolated subdural hematoma and preserved consciousness.

孤立性硬膜下血肿且意识保留的患者的癫痫发作频率。