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: 10041523
• 负责人: Monique Anderson Starks
• 金额: $ 18.43万
• 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/10041523
• 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.

抽象的 院外心脏骤停（OHCA）每年影响35万美国人，生存率非常 低的。倒塌后每1分钟延迟实现有效心脏功能的回归 生存下降了10％。旁观者可以通过表演胸部来帮助对OHCA受害者进行紧急治疗 通过使用自动化的外部除颤器（AEDS）。但是，当前的旁观者使用静态 AEDS非常低，除颤主要由急救人员和紧急医疗管理 中位到达时间（8分钟）的服务（EMS）为时已晚，无法节省大多数OHCA患者。使用无人机 在911呼叫的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 关于当前无人机的技术能力。与模拟模型的早期工作已经证明了 战略性设计的无人机网络，可以比EMS更快地向OHCA提供AED 达到。但是，这些早期模拟在交付给AED的使用中具有完全的有效性 OHCA场景没有考虑旁观者变量。众所周知，旁观者可能会犹豫 执行CPR并应用AED，并选择人口统计和邻里因素（年龄，性别， 种族/性别，教育）可以预测这种治疗变异性。旁观者提取的时间 在OHCA中成功应用它的AED可能会严重影响及时的无人机AED的总体生存增长 送货。对潜在治疗有效性的准确理解应解释 旁观者的性能。该应用程序的总体目的是利用数据科学和仿真 估计无人机网络的最终用户性能和治疗效应的研究 社区，第一响应者和EMS性能。 AIM 1将确定无人机的最佳位置 电台以确保AED及时到达北部的高OHCA风险地理区域（3至5分钟内） 卡罗来纳州。 AIM 2将定义并确定OHCA社区表型簇的关联 高收入NC社区的治疗模式。 AIM 3将使用模拟无人机AED OHCA场景 定义社区表型集群之间的无人机AED BYSTANDER治疗间隔（例如，少数民族，农村， 高等教育，老年人）在高级OHCA风险NC社区中。目标2和3的结果将用于完善 我们的优化模型（AIM 1）估计治疗效率和效率。拟议的工作将是 在Starks指导团队的直接监督下进行：Mentor（Daniel Mark博士），同事 （Christopher Granger博士）及其顾问团队（Billy Williams博士和Graham Nichol博士）。这个K23 在她的指导团队和咨询委员会的支持和指导下的申请将定位博士。 最终领导独立NIH资助研究的Starks，专注于OHCA的社区治疗，包括 开发/测试干预措施以改善OHCA和实用临床试验中的AED使用，以确定我们的 基于模型的EMS无人机AED输送系统可测量地改善了OHCA受害者的经验结果。