Hemostasis, Hematoma Expansion, and Outcomes After Intracerebral Hemorrhage

项目总结 脑内出血(ICH)，出血到脑组织，通常是致残或致命的。脑出血的并发症 加剧了对患者结局的影响。血肿扩大是指血肿从第一个开始生长 (诊断)计算机体层摄影(CT)扫描到后续CT扫描，发生在10%-25%的患者中。 减少血肿扩大通过保持小血肿的小来减少死亡和残疾。否则， 血肿扩大会在头骨有限的空间内压缩脑组织。止血药物(例如， 凝血酶原复合体)在减少高危患者的血肿扩大方面有效 血肿扩大。然而，我们预测血肿扩大和选择患者止血的能力 药物治疗是有限的。不幸的是，止血药物有潜在的副作用，如心肌梗死 脑梗塞。另外，约10%的患者会出现癫痫发作，并可能发展为癫痫持续状态。 (持续癫痫发作)。不幸的是，广泛服用癫痫药物也会导致并发症， 患者预后更差，随访时与健康相关的生活质量降低。病人护理受到损害是因为 我们不能选择可能挽救生命的患者进行治疗(止血治疗、癫痫药物治疗)。 ML/AI为这些障碍提供了解决方案。 我们开发了ML/AI算法来解决这些靶向止血治疗和癫痫发作的障碍 使用多项指标的ML预测血肿扩大的正在进行的奖项背景下的药物治疗 凝血(止血的生物标志物)。然而，重大的道德问题仍未得到解决。ML/AI 算法可能会受到患者群体中不可预见的偏见的影响，这些患者就是从这些患者群体中开发出来的。偏倚 可能会导致NIH认为易受伤害的患者的表现不同(例如，少数民族、农村 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)揭示了对ML/AI的使用的深刻矛盾心理。始终如一令人兴奋 治疗建议和全天候可获得性，但也担心偏见、责任和 疏忽。这样的定性研究还没有在非物质文化遗产的背景下进行，这需要紧急 因为血肿扩大和癫痫发作在脑出血症状出现后的几个小时内就会发生。 在我们正在进行的颁奖中，我们将确定我们开发的初步ML/AI算法是否具有 在脆弱的患者中表现不同；如果需要，我们将开发纠正它的方法，并在 手头有不同的数据来源，并建立了血肿扩大和癫痫发作的预测指标。我们将表演 对临床医生、患者和受脑出血影响的家属进行定性访谈，以确定和解决 如果要在非物质文化遗产中信任ML/AI，就必须解决这些问题。这项提议将为其他国家提供借鉴 需要紧急决策的急性疾病。

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.