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.

项目摘要/摘要 在美国，硬膜下血肿（SDHS）预计将成为最常见的颅骨 到2030年，神经外科状况。这具有重大的公共卫生影响，因为近50％的SDH患者是 三个月后死亡或严重残疾。尽管它很重要，但对这种疾病的研究很少 人群级别，特别是在患者离开医院后的结局方面。幸存下来的近1例患者中有近1例 最初的SDH住院治疗在90天内重新住院，这种风险可能会受到社会的重大影响 健康决定因素（SOCDH）。无论如何，尚无可用的预测模型来识别高风险的SDH幸存者 重新建立。此外，SDHS与前抗血栓形成型紧密相关，这些药物是 通常在演讲时举行，但几乎没有证据表明反血栓的风险和好处 SDH幸存者的恢复。为了解决这些限制，我们将进行SDH的首次人口级研究 美国的结果。我们将通过利用有效的验证，以相对较快的成本来实现这一目标 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群级别 中风和颅内出血的结果超过30年。这种基础架构将使我们能够确定 SDH后发生的主要缺血性和出血事件的三年风险，并确定每个结果的预测因子。 我们还将在SDH患者中建立90天重新寄养或死亡的预测模型，以利用这两者 临床和SOCDH变量。我们将使用常规的预测建模以及现代的机器学习 技术，使我们能够最大化预测能力，并可能识别导致的新变量和相互作用 SDH幸存者的不利结果。通过这项建议，鲁滨逊博士将成为 SDH和使用新颖的数据科学技术来分析大型临床数据库。这些技能会为他做好准备 成为整体辛辛那提/北肯塔基州中风研究的下一个PI。鲁滨逊博士将进行此事 在杰出的指导委员会的指导下工作：布雷特·基拉（Brett Kissela） 使用大数据技术的专业知识；临床医生Dan Woo博士研究脑内差异 出血； Opeolu Adeoye是一位具有急性护理研究专业知识的神经化学主义者和研究人员；和Houman 卡梅尔（Kamel）是一位中风流行病学家和神经化学家，在人群级别的SDHS研究中具有专业知识。