Application of Data Sciences in Traumatic Brain Injury

摘要 创伤性脑损伤(TBI)是导致发病率和死亡率的主要原因，中-重度脑损伤患者 创伤性脑外伤通常需要紧急手术和麻醉护理。患有脑损伤的患者 手术结果很差，归因于围手术期二次侮辱的发生率很高(50%)，例如 低血压和低碳酸血症，减少脑血流，导致脑缺血。麻醉师 提供止痛、镇静、不动和健忘，并旨在提供预期的生理稳定性 患者反应、实时生理数据和专业判断，但不幸的是无法 实时准确预测哪些脑外伤患者会出现低血压和低碳酸血症。然而，避免 这些第二次侮辱增加了脑外伤患者的出院存活率。预测和预防 因此，颅脑损伤护理期间的低血压和低碳酸血症是至关重要的，并可避免低血压和低碳酸血症。 是颅脑损伤围手术期护理的关键绩效指标。小数据科学研究表明，机器 学习(ML)技术可以模拟和预测脑损伤的病理生理学，并帮助减少不必要的秒 在TBI之后的侮辱。该项目的目标是使用ML方法来预防二次侮辱(低血压和 低碳酸血症)在急诊/急诊颅脑损伤围术期护理。针对PA-16-161，我们提出2个具体建议 目的：1)构建和识别最能准确预测的脑损伤生理性ML模型 围手术期低血压和低碳酸血症，以及2)开发ML衍生的个性化处方 用于预防围手术期低血压和低碳酸血症。这个项目是创新的，将是 效果显著，因为该方法以强大的数据科学、急性护理和实施为基础 科学框架，因为它开发ML派生的处方来预防低血压和低碳酸血症， 因为我们使用ML解决方案来改善TBI后的护理质量和结果。

ABSTRACT Traumatic brain injury (TBI) is a leading cause of morbidity and mortality, and patients with moderate-severe traumatic brain TBI often require urgent/emergent surgical and anesthesia care. Patients with TBI who have surgery have poor outcomes, attributed to a high (>50%) prevalence of perioperative second insults such as hypotension and hypocarbia, which reduce cerebral perfusion and cause cerebral ischemia. Anesthesiologists provide analgesia, sedation, immobility, and amnesia, and aim to confer physiological stability, expected patient response, real-time physiological data, and professional judgement but are unfortunately unable to accurately predict in real time which patients with TBI will have hypotension and hypocarbia. Yet, avoidance of these second insults increases discharge survival among patients with TBI. Predicting and preventing hypotension and hypocarbia during TBI care is, therefore, vital, and avoidance of hypotension and hypocarbia are key performance indicators for perioperative TBI care. Small data science studies suggest that machine learning (ML) techniques can model and predict TBI pathophysiology and help reduce unwanted second insults after TBI. The project goal is to use ML methods to prevent second insults (hypotension and hypocarbia) during urgent/emergent perioperative TBI care. In response to PA-16-161, we propose 2 Specific Aims: 1) To construct and identify the TBI physiological ML model that most accurately predicts perioperative hypotension and hypocarbia, and 2) To develop ML derived personalized prescriptions for prevention of perioperative hypotension and hypocarbia. This project is innovative and will be impactful because the approach is grounded in strong data science, and acute care, and implementation science frameworks, because it develops ML derived prescriptions to prevent hypotension and hypocarbia, and because we use ML solutions to improve care quality and outcomes after TBI.