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
基本信息
- 批准号:10041523
- 负责人:
- 金额:$ 18.43万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:911 callAccountingAdvisory CommitteesAffectAgeAmericanArrhythmiaAutomated External DefibrillatorBrainCardiopulmonary ResuscitationCensusesCharacteristicsChestCommunitiesDataData ScienceDatabasesDropsEducationEffectivenessElderlyElectric CountershockEmergency SituationEmergency medical serviceEmergency treatmentEnsureEuropeEvaluationFundingGenderGeographic LocationsGrantHeart ArrestHispanicsHospitalsHouseholdIncidenceIncomeInstitute of Medicine (U.S.)InterventionLeadLocationLong-Term SurvivorsMeasurableMedicalMedical emergencyMentorsMentorshipMethodsMinorityModelingNeighborhoodsNervous System PhysiologyNorth CarolinaOrganOutcomePatientsPatternPerformancePhenotypePositioning AttributePragmatic clinical trialRaceRefractoryRegistriesResearchResuscitationRiskRuralShockSupervisionSurvival RateSystemTestingTimeTrainingTreatment EffectivenessUnited StatesUnited States National Institutes of HealthUniversitiesUse EffectivenessVentricular TachycardiaWorkbasedesignemergency service responderfirst respondergeographic riskheart functionhigh riskimprovedinnovative technologiesmodels and simulationmultiple data sourcesnetwork modelsout-of-hospital cardiac arrestrural countiessexsimulationtraining opportunitytreatment effecttreatment responders
项目摘要
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)在高级北卡罗来纳州的高级风险居民区。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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Monique Anderson Starks其他文献
Correlation between hospital rates of survival to discharge and long-term survival for in-hospital cardiac arrest: Insights from Get With The Guidelines®-Resuscitation registry
- DOI:
10.1016/j.resuscitation.2024.110322 - 发表时间:
2024-09-01 - 期刊:
- 影响因子:
- 作者:
Rohan Khera;Arya Aminorroaya;Kevin F. Kennedy;Paul S. Chan;Anne Grossestreuer;Ari Moskowitz;Joseph Ornato;Matthew Churpek;Monique Anderson Starks;Saket Girotra;Sarah Perman - 通讯作者:
Sarah Perman
Monique Anderson Starks的其他文献
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{{ truncateString('Monique Anderson Starks', 18)}}的其他基金
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:
10439455 - 财政年份:2020
- 资助金额:
$ 18.43万 - 项目类别:
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 - 财政年份:2020
- 资助金额:
$ 18.43万 - 项目类别:
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:
10655355 - 财政年份:2020
- 资助金额:
$ 18.43万 - 项目类别:
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