Application of Data Sciences in Traumatic Brain Injury

摘要 创伤性脑损伤（TBI）是发病率和死亡率的主要原因，并且具有中度至重度脑损伤的患者 创伤性脑TBI通常需要紧急/紧急手术和麻醉护理。TBI患者， 外科手术结果较差，这归因于围手术期二次损伤的高发生率（> 50%），例如 低血压和低碳酸血症，其减少脑灌注并引起脑缺血。麻醉医师 提供镇痛、镇静、不动和健忘，并旨在赋予预期的生理稳定性， 患者反应、实时生理数据和专业判断，但不幸的是， 准确地预测真实的时间，TBI患者将有低血压和低碳酸血症。然而，避免 这些二次损伤增加了TBI患者的出院存活率。预测和预防 因此，TBI护理期间的低血压和低碳酸血症至关重要， 是围手术期TBI护理的关键绩效指标。小型数据科学研究表明， 学习（ML）技术可以建模和预测TBI病理生理学，并帮助减少不必要的第二次损伤。 TBI后的侮辱该项目的目标是使用ML方法来防止二次损伤（低血压和 低碳酸血症）。针对PA-16 - 161，我们提出了2个具体的 目的：1）构建和识别最准确预测TBI的生理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 预防围手术期低血压和低碳酸血症。该项目具有创新性， 影响力，因为该方法基于强大的数据科学，急性护理和实施 科学框架，因为它开发了ML衍生的处方，以防止低血压和低碳酸， 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.