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.

项目总结/摘要 在美国，硬膜下血肿（SDH）预计将成为最常见的颅内血肿。 到2030年的神经外科状况。这具有重大的公共卫生影响，因为近50%的SDH患者 三个月内死亡或严重残疾尽管它的重要性，有很少的研究，这种疾病在 人口水平，特别是关于病人离开医院后的结果。六分之一的患者存活 初次SDH住院患者在90天内再次住院，这种风险可能受到社会因素的严重影响。 健康决定因素（SocDH）。无论如何，没有预测模型可用于识别高风险的SDH幸存者 再次住院此外，SDH与发病前抗血栓形成药物的使用密切相关，这些药物是 但关于抗血栓药物的风险和益处的证据很少 恢复SDH幸存者。为了解决这些局限性，我们将进行第一次人群水平的SDH研究 在美国的成果。我们将通过利用经过验证的 大辛辛那提/北方肯塔基州卒中研究的基础设施，该研究一直在研究人群水平 在脑卒中和颅内出血方面的研究超过30年。这个基础设施将使我们能够确定 SDH术后3年发生主要缺血性和出血性事件的风险，并确定每种结局的预测因子。 我们还将建立一个预测模型的90天再住院或死亡的SDH患者，利用两者 临床和SocDH变量。我们将使用传统的预测建模沿着现代机器学习 技术，使我们能够最大限度地提高预测能力，并有可能确定新的变量和相互作用， SDH幸存者的不良结局。通过这项建议，罗宾逊博士将成为流行病学专家， SDH和使用新的数据科学技术来分析大型临床数据库。这些技能会让他 成为整个大辛辛那提/北方肯塔基州卒中研究的下一位PI。罗宾逊医生将负责 在一个杰出的导师委员会的指导下工作：布雷特·基塞拉博士，中风流行病学家， 在使用大数据技术的专业知识;丹吴博士，临床科学家，研究大脑内的差异， 出血; Opeolu Adeoye，一位神经重症监护医生和研究人员，在急性护理研究方面具有专业知识;和Hooman Kamel博士说，他是一位中风流行病学家和神经强化专家，擅长SDH的人群水平研究。