Evaluation of the Requirements and Critical Features of a Drone-Deployed AED Network to Improve Community-Level Survival after OHCA

评估无人机部署的 AED 网络的要求和关键特征，以提高 OHCA 后社区的生存率

• 批准号: 10219358
• 负责人: Monique Anderson Starks
• 金额: $ 18.52万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-17 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10219358
• 关键词: 911 call; Accounting; Advisory Committees; Affect; Age; American; Arrhythmia; Automated External Defibrillator; Brain; Cardiopulmonary Resuscitation; Censuses; Characteristics; Chest; Communities; Data; Data Science; Databases; Drops; Education; Effectiveness; Elderly; Electric Countershock; Emergency Situation; Emergency medical service; Emergency treatment; Ensure; Europe; Evaluation; Funding; Gender; Geographic Locations; Grant; Heart Arrest; Hispanics; Hospitals; Household; Incidence; Income; Institute of Medicine (U.S.); Intervention; Lead; Location; Long-Term Survivors; Measurable; Medical; Medical emergency; Mentors; Mentorship; Methods; Minority; Modeling; Neighborhoods; Nervous System Physiology; North Carolina; Organ; Outcome; Patients; Pattern; Performance; Phenotype; Positioning Attribute; Pragmatic clinical trial; Race; Refractory; Registries; Research; Resuscitation; Risk; Rural; Shock; Supervision; Survival Rate; System; Testing; Time; Training; Treatment Effectiveness; United States; United States National Institutes of Health; Universities; Use Effectiveness; Ventricular Tachycardia; Work; base; design; emergency service responder; first responder; geographic risk; heart function; high risk; improved; innovative technologies; models and simulation; multiple data sources; network models; out-of-hospital cardiac arrest; rural counties; sex; simulation; training opportunity; treatment effect; treatment responders

Abstract Out-of-hospital cardiac arrest (OHCA) affects over 350,000 Americans annually and survival rates are very low. For every 1-minute delay in achieving return of effective heart function after collapse, the chance of survival drops by 10%. Bystanders can aid in the emergency treatment of OHCA victims by performing chest compressions and by using automated external defibrillators (AEDs). However, current bystander use of static AEDs is very low and defibrillation is primarily administered by first responders and emergency medical services (EMS) whose median arrival time (8 minutes) is too late to save most OHCA patients. Using a drone to deliver AEDs to OHCA victims within 3 to 5 minutes of the 911 call is an exciting new concept that is based on current technical capabilities of drones. Early work with simulation models has demonstrated the potential of a strategically designed drone network to deliver an AED to an OHCA substantially more rapidly than EMS can achieve. However, these early simulations assumed complete effectiveness of AED use when delivered to an OHCA scene without considering the bystander variables. It is well-known that bystanders may hesitate to perform CPR and to apply an AED, and that select demographic and neighborhood factors (age, sex, race/gender, education) may be predictive of such treatment variability. The time it takes a bystander to extract an AED and apply it successfully in OHCA may critically impact overall survival gains from timely drone AED delivery. An accurate understanding of potential treatment effectiveness should account for expected bystander performance. The overarching aim of this application is to utilize data science and simulation research to estimate end-user performance and treatment-effectiveness of a drone network accounting for community, first responder, and EMS performance. Aim 1 will determine the optimal placement of drone stations to ensure timely AED arrival in high-OHCA risk geographic areas (within 3 to 5 minutes) across North Carolina. Aim 2 will define and determine the association of community phenotypic clusters on OHCA treatment patterns in high-incidence NC communities. Aim 3 will use simulated drone AED OHCA scenarios to define drone-AED-bystander treatment intervals among community phenotypic clusters (e.g., minority, rural, low education, elderly) in high-OHCA risk NC neighborhoods. Results from Aims 2 and 3 will be used to refine our optimization model (Aim 1) to estimate treatment effectiveness and efficiency. The proposed work will be carried out under the direct supervision of Dr. Starks mentorship team: mentor (Dr. Daniel Mark), co-mentor (Dr. Christopher Granger), and her advisory team (Drs. Billy Williams and Graham Nichol). This K23 application with the support and guidance of her mentorship team and advisory committee will position Dr. Starks to eventually lead independent NIH funded studies focused on community treatment of OHCA, including developing/testing interventions to improve AED use in OHCA and pragmatic clinical trials to determine if our model-based EMS drone AED delivery system measurably improves empirical outcomes in OHCA victims.

摘要 院外心脏骤停（OHCA）每年影响超过35万美国人，存活率非常低。 低在虚脱后，每延迟1分钟恢复有效的心脏功能， 存活率下降10%旁观者可以通过执行胸部手术来帮助OHCA受害者的紧急治疗 按压和使用自动体外除颤器（AED）。然而，目前旁观者使用静态 AED的使用率非常低，除颤主要由急救人员和急救医疗人员管理。 紧急医疗服务（EMS）的平均到达时间（8分钟）太晚，无法挽救大多数OHCA患者。使用无人机 在911报警后3到5分钟内向OHCA受害者提供AED是一个令人兴奋的新概念， 无人机的技术能力。模拟模型的早期工作已经证明了 一个战略性设计的无人机网络，以比EMS更快的速度将AED运送到OHCA achieve.然而，这些早期的模拟假设了AED使用的完全有效性， 不考虑旁观者变量的OHCA场景。众所周知，旁观者可能会犹豫， 进行心肺复苏术和应用AED，以及选择人口统计学和邻里因素（年龄，性别， 种族/性别、教育）可以预测这种治疗变异性。一个旁观者从你的大脑中 AED并在OHCA中成功应用可能会对及时无人驾驶AED的整体生存率产生关键影响 交付.对潜在治疗有效性的准确理解应考虑预期的 旁观者的表现该应用程序的首要目标是利用数据科学和模拟 研究评估最终用户性能和无人机网络的治疗效果， 社区，第一响应者和EMS性能。目标1将决定无人机的最佳位置 站，以确保及时AED到达高OHCA风险的地理区域（在3至5分钟内），在整个北 卡罗莱纳。目标2将定义和确定OHCA上群落表型聚类的关联性 高发病率NC社区的治疗模式。Aim 3将使用模拟无人机AED OHCA场景， 在群体表型集群中定义无人机-AED-旁观者治疗间隔（例如，少数民族，农村， 低教育，老年人）在高OHCA风险NC社区。目标2和3的结果将用于改进 我们的优化模型（目标1），以估计治疗效果和效率。拟议的工作将是 在斯塔克斯博士导师团队的直接监督下进行：导师（丹尼尔·马克博士）、联合导师 (Dr.克里斯托弗格兰杰），和她的顾问团队（博士比利威廉姆斯和格雷厄姆尼科尔）。K23 在她的导师团队和咨询委员会的支持和指导下，申请将被定位为博士。 斯塔克斯最终领导独立的NIH资助的研究，重点是社区治疗OHCA，包括 开发/测试干预措施，以改善OHCA和实用临床试验中AED的使用，以确定我们的 基于模型的EMS无人机AED递送系统可显著改善OHCA受害者的经验结果。