The Subdural Hematoma Outcomes in a Population (SD HOP) Study

硬膜下血肿人群 (SD HOP) 研究结果

• 批准号: 10591861
• 负责人: David Robinson
• 金额: $ 20.95万
• 依托单位: UNIVERSITY OF CINCINNATI
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591861
• 关键词: Acute; Address; Affect; Attention; Benefits and Risks; Big Data; Blood Vessels; Case Fatality Rates; Case Series; Cephalic; Cerebral hemisphere hemorrhage; Cessation of life; Chronic; Clinical; Clinical Data; Computerized Medical Record; County; Craniocerebral Trauma; Data Science; Development; Diagnosis; Disabled Persons; Disease; Disparity; Epidemiologist; Epidemiology; Epilepsy; Ethnic Origin; Event; Fibrinolytic Agents; Future; Goals; Head; Hematoma; Hemorrhage; Hospital Mortality; Hospitalization; Hospitals; Incidence; Incidence Study; Informatics; Infrastructure; Insurance; Insurance Coverage; International Classification of Disease Codes; Intracranial Hemorrhages; Ischemia; Ischemic Stroke; Kentucky; Machine Learning; Measures; Medicaid; Mentorship; Modeling; Modernization; Myocardial Infarction; Operative Surgical Procedures; Outcome; Outcome Measure; Patients; Persons; Pharmaceutical Preparations; Physicians; Population; Population Study; Public Health; Race; Randomized, Controlled Trials; Recurrence; Research; Research Personnel; Resources; Risk; Scientist; Seizures; Status Epilepticus; Stroke; Subdural Hematoma; Survivors; Techniques; Time; Trauma; United States; Work; acute care; adverse outcome; clinical database; cost; follow-up; health care availability; health care disparity; high risk; hospital readmission; improved; indexing; large datasets; machine learning algorithm; neurosurgery; novel; outcome prediction; predictive modeling; readmission rates; skills; social health determinants; stroke incidence; stroke outcome; thrombotic

Project Summary/Abstract In the United States, subdural hematomas (SDHs) are projected to become the most common cranial neurosurgical condition by 2030. This has major public health implications, as nearly 50% of SDH patients are dead or severely disabled at three months. Despite its importance, there is very little study of this disease at the population-level, particularly with regard to outcomes after patients leave the hospital. Nearly 1 in 6 patients that survive an initial SDH hospitalization are rehospitalized within 90 days, and this risk may be impacted heavily by social determinants of health (SocDH). Regardless, there is no predictive model available to identify SDH survivors at high risk of rehospitalization. Further, SDHs are tightly associated with premorbid antithrombotic use, and these medications are commonly held at the time of presentation, but there is little evidence about the risks and benefits of antithrombotic resumption in SDH survivors. To address these limitations, we will conduct the first population-level study of SDH outcomes in the United States. We will accomplish this relatively quickly and at low cost by utilizing the well-validated infrastructure of the Greater Cincinnati/Northern Kentucky Stroke Study, which has been studying population-level outcomes in stroke and intracranial hemorrhage for more than 30 years. This infrastructure will allow us to determine the 3-year risk of major ischemic and hemorrhagic events after an SDH and determine the predictors for each outcome. We will also a build a predictive model of 90-day rehospitalization or death among SDH patients that utilizes both clinical and SocDH variables. We will use conventional predictive modeling along with modern machine learning techniques, allowing us to maximize predictive ability and potentially identify new variables and interactions that lead to adverse outcomes in SDH survivors. Through this proposal, Dr. Robinson will become an expert in the epidemiology of SDH and in the use of novel data science techniques to analyze large clinical databases. These skills will prepare him to become the next PI of the overall Greater Cincinnati/Northern Kentucky Stroke Study. Dr. Robinson will conduct this work under the guidance of a distinguished mentorship committee: Dr. Brett Kissela, a stroke epidemiologist with expertise in using big data techniques; Dr. Dan Woo, a clinician scientist that studies disparities in intracerebral hemorrhage; Opeolu Adeoye, a neurointensivist and researcher with expertise in acute care research; and Hooman Kamel, a stroke epidemiologist and neurointensivist with expertise in the population-level study of SDHs.

项目概要/摘要 在美国，硬膜下血肿 (SDH) 预计将成为最常见的颅脑血肿 到 2030 年，神经外科疾病将得到改善。这对公共健康具有重大影响，因为近 50% 的 SDH 患者患有 三个月时死亡或严重残疾。尽管这种疾病很重要，但目前对这种疾病的研究却很少。 人口层面，特别是患者出院后的结果。近六分之一的患者幸存 初次 SDH 住院后 90 天内再次住院，这种风险可能会受到社会影响 健康决定因素（SocDH）。无论如何，没有可用的预测模型来识别高危 SDH 幸存者 的再住院。此外，SDH 与病前抗血栓药物的使用密切相关，并且这些药物 通常在就诊时进行，但几乎没有证据表明抗血栓治疗的风险和益处 SDH 幸存者的恢复。为了解决这些局限性，我们将进行首次人口水平的 SDH 研究 在美国的成果。我们将利用经过充分验证的技术，相对快速、低成本地实现这一目标 大辛辛那提/北肯塔基州中风研究的基础设施，该研究一直在研究人口水平 30 多年来对中风和颅内出血的治疗结果。该基础设施将使我们能够确定 SDH 后 3 年发生重大缺血和出血事件的风险，并确定每个结果的预测因素。 我们还将建立一个 SDH 患者 90 天再住院或死亡的预测模型，该模型利用 临床和 SocDH 变量。我们将使用传统的预测模型和现代机器学习 技术，使我们能够最大限度地提高预测能力，并有可能识别出新的变量和相互作用，从而导致 SDH 幸存者的不良后果。通过这项提议，罗宾逊博士将成为流行病学专家 SDH 并使用新颖的数据科学技术来分析大型临床数据库。这些技能将使他做好准备 成为整个大辛辛那提/北肯塔基州中风研究的下一位 PI。罗宾逊博士将进行这项工作 在杰出的导师委员会的指导下工作：Brett Kissela 博士，一位中风流行病学家， 使用大数据技术的专业知识； Dan Woo 博士是一位临床科学家，研究大脑内的差异 出血； Opeolu Adeoye，神经重症科医生和研究员，在急症护理研究方面拥有专业知识；和胡曼 Kamel 是一位中风流行病学家和神经重症医师，在 SDH 人群水平研究方面拥有专业知识。