Cardiology Unit, Department of Medicine, Peninsula Health, Frankston, Victoria, Australia.
Peninsula Clincal School, Monash University, Melbourne, Victoria, Australia.
Emerg Med J. 2022 Sep;39(9):701-707. doi: 10.1136/emermed-2020-210334. Epub 2021 Dec 22.
Access to individual percutaneous coronary intervention (PCI) centres has traditionally been determined by historical referral patterns along arbitrarily defined geographic boundaries. We set out to produce predictive models of ST-elevation myocardial infarction (STEMI) demand and time-efficient access to PCI centres.
Travel times from random addresses to PCI centres in Melbourne, Australia, were estimated using Google map application programming interface (API). Departures at 08:15 and 17:15 were compared with 23:00 to determine the effect of peak hour traffic congestion. Real-world ambulance travel times were compared with estimated travel times using Google map developer software. STEMI incidence per postcode was estimated by merging STEMI incidence per age group data with age group per postcode census data. PCI centre network configuration changes were assessed for their effect on hospital STEMI loading, catchment size, travel times and the number of STEMI cases within 30 min of a PCI centre.
Nearly 10% of STEMI cases travelled more than 30 min to a PCI centre, increasing to 20% by modelling the removal of large outer metropolitan PCI centres (p<0.05). A model of 7 PCI centres compared favourably to the current existing network of 11 PCI centres (p=0.18 (afternoon), p=0.5 (morning and night)). The intraclass correlation between estimated travel times and ambulance travel times was 0.82, p<0.001.
This paper provides a framework to integrate prehospital environmental variables, existing or altered healthcare resources and health statistics to objectively model STEMI demand and consequent access to PCI. Our methodology can be modified to incorporate other inputs to compute optimum healthcare efficiencies.
传统上，个体经皮冠状动脉介入治疗（PCI）中心的准入是根据任意定义的地理边界的历史转诊模式来决定的。我们旨在建立预测 ST 段抬高型心肌梗死（STEMI）需求和高效获得 PCI 中心的模型。
使用 Google 地图应用程序编程接口（API）估计从澳大利亚墨尔本的任意地址到 PCI 中心的旅行时间。比较 08:15 和 17:15 出发与 23:00 出发的旅行时间，以确定高峰时段交通拥堵的影响。使用 Google 地图开发者软件比较实际救护车旅行时间和估计旅行时间。通过将每个年龄组的 STEMI 发生率数据与每个邮政编码的年龄组人口普查数据合并，估计每个邮政编码的 STEMI 发生率。评估 PCI 中心网络配置变化对医院 STEMI 负荷、服务范围大小、旅行时间以及在 PCI 中心 30 分钟内 STEMI 病例数的影响。
近 10%的 STEMI 患者前往 PCI 中心的旅行时间超过 30 分钟，通过模拟去除大型外都会区 PCI 中心，这一比例增加到 20%（p<0.05）。与现有的 11 个 PCI 中心网络相比，7 个 PCI 中心的模型表现良好（下午：p=0.18；早上和晚上：p=0.5）。估计旅行时间和救护车旅行时间之间的组内相关系数为 0.82，p<0.001。
本文提供了一个框架，将院前环境变量、现有或改变的医疗资源和健康统计数据整合起来，客观地模拟 STEMI 需求以及随之而来的 PCI 准入。我们的方法可以修改，以纳入其他输入来计算最佳医疗效率。
