Background 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. Methods 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. Results 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. Conclusion 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. Data are available on reasonable request.

中文翻译：

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STEMI 服务交付建模：概念验证研究

背景 传统上，个体经皮冠状动脉介入治疗 (PCI) 中心的访问是由沿任意定义的地理边界的历史转诊模式决定的。我们着手制作 ST 段抬高心肌梗塞 (STEMI) 需求的预测模型，并能高效地进入 PCI 中心。方法 使用谷歌地图应用程序编程接口 (API) 估计从随机地址到澳大利亚墨尔本 PCI 中心的旅行时间。将 08:15 和 17:15 的出发时间与 23:00 进行比较，以确定高峰时段交通拥堵的影响。使用谷歌地图开发软件将现实世界的救护车旅行时间与估计的旅行时间进行比较。每个邮政编码的 STEMI 发病率是通过将每个年龄组的 STEMI 发病率数据与每个邮政编码人口普查数据的年龄组合并来估计的。评估 PCI 中心网络配置变化对医院 STEMI 负荷、集水区规模、旅行时间和 PCI 中心 30 分钟内 STEMI 病例数的影响。结果 近 10% 的 STEMI 病例前往 PCI 中心的时间超过 30 分钟，通过对大型外围城市 PCI 中心的移除建模，这一比例增加到 20% (p<0.05)。与现有的 11 个 PCI 中心的网络相比，7 个 PCI 中心的模型具有优势（p=0.18（下午），p=0.5（早晚））。估计出行时间和救护车出行时间之间的组内相关性为 0.82，p<0.001。结论 本文提供了一个框架来整合院前环境变量、现有或改变的医疗保健资源和健康统计数据，以客观地模拟 STEMI 需求和随后的 PCI 访问。我们可以修改我们的方法以结合其他输入来计算最佳医疗保健效率。可根据合理要求提供数据。