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) 每年影响超过 350,000 名美国人，存活率非常高 低的。崩溃后每延迟 1 分钟恢复有效心脏功能，就有机会 生存率下降 10%。旁观者可以通过胸腔检查来帮助紧急治疗 OHCA 受害者 按压和使用自动体外除颤器 (AED)。然而，目前旁观者使用静电 AED 的使用率非常低，除颤主要由急救人员和紧急医疗人员进行 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 在接到 911 电话后 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 无人机目前的技术能力。仿真模型的早期工作已经证明了 经过战略设计的无人机网络，可以比 EMS 更快地向 OHCA 提供 AED 达到。然而，这些早期模拟假设 AED 使用在交付给患者时完全有效。 OHCA 场景不考虑旁观者变量。众所周知，旁观者可能会犹豫 执行心肺复苏术并应用 AED，并选择人口和社区因素（年龄、性别、 种族/性别、教育程度）可以预测这种治疗的变异性。旁观者解救所需的时间 AED 并在 OHCA 中成功应用可能会严重影响及时无人机 AED 带来的总体生存收益 送货。准确理解潜在的治疗效果应考虑预期的 旁观者表现。该应用程序的总体目标是利用数据科学和模拟 研究估计无人机网络的最终用户性能和治疗效果 社区、急救人员和 EMS 绩效。目标 1 将确定无人机的最佳放置位置 确保 AED 及时到达北部 OHCA 高风险地理区域（3 至 5 分钟内） 卡罗莱纳.目标 2 将定义并确定 OHCA 上群落表型簇的关联 高发北卡罗来纳州社区的治疗模式。目标 3 将使用模拟无人机 AED OHCA 场景来 定义社区表型集群（例如少数民族、农村、 北卡罗来纳州高 OHCA 风险社区中的受教育程度低、老年人）。目标 2 和 3 的结果将用于改进 我们的优化模型（目标 1）来估计治疗效果和效率。拟议的工作将是 在 Starks 博士导师团队的直接监督下进行：导师（Daniel Mark 博士）、共同导师 （克里斯托弗·格兰杰博士）和她的顾问团队（比利·威廉姆斯博士和格雷厄姆·尼科尔博士）。这个K23 在她的导师团队和咨询委员会的支持和指导下，她的申请将定位博士。 Starks 最终将领导 NIH 资助的独立研究，重点关注 OHCA 的社区治疗，包括 开发/测试干预措施以改善 OHCA 中 AED 的使用，并进行实用的临床试验以确定我们的 基于模型的 EMS 无人机 AED 输送系统可显着改善 OHCA 受害者的经验结果。