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Modelling STEMI service delivery: a proof of concept study
  1. Justin Cole1,2,
  2. Richard Beare2,3,
  3. Thanh Phan4,
  4. Velandai Srikanth2,
  5. Dion Stub5,6,7,
  6. Karen Smith6,
  7. Karen Murdoch6,
  8. Jamie Layland1,2
  1. 1 Cardiology Unit, Department of Medicine, Peninsula Health, Frankston, Victoria, Australia
  2. 2 Peninsula Clincal School, Monash University, Melbourne, Victoria, Australia
  3. 3 Developmental Imaging, Murdoch Children's Research Institute, Doncaster, Victoria, Australia
  4. 4 School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
  5. 5 Department of Cardiology, Alfred Hospital, Melbourne, Victoria, Australia
  6. 6 Center for Research and Evaluation, Ambulance Victoria, Doncaster, Victoria, Australia
  7. 7 Department of Epidemiology and Preventative Medicine Monash University, Victoria, Australia, Monash University, Melbourne, Victoria, Australia
  1. Correspondence to Professor Jamie Layland, Cardiology, Frankston, VIC 3199, Australia; jlayland{at}


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.

  • acute coronary syndrome
  • cardiac care
  • acute coronary syndrome
  • cardiac care
  • acute myocardal infarct
  • cardiac care
  • care systems
  • cardiac care
  • treatment

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Data are available on reasonable request.

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  • Handling editor Loren De Freitas

  • Contributors JC contributed to planning, conducting and reporting all work. JC wrote all drafts and collated all revisions. JC contributed to statistical analysis. RB contributed to planning and writing the software code to enable data extraction from google maps. RB contributed to statistical analysis and revision of all drafts.TP contributed to planning and revision of all drafts. TP was fundamental in the initiation of this work based on prior stroke studies. VS contributed to planning and revision of all drafts. DS contributed to planning and linking with Ambulance Victoria data. DS revised all drafts. KS provided Ambulance Victoria data and breakdown of Ambulance Victoria data. KS contributed to revision of drafts. KM contributed to all revisions from an Ambulance Victoria perspective. JL is guarantor and contributed in all facets of this study including planning, conducting, statistical analysis, reporting and revision of all drafts.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Map disclaimer The inclusion of any map (including the depiction of any boundaries therein), or of any geographic or locational reference, does not imply the expression of any opinion whatsoever on the part of BMJ concerning the legal status of any country, territory, jurisdiction or area or of its authorities. Any such expression remains solely that of the relevant source and is not endorsed by BMJ. Maps are provided without any warranty of any kind, either express or implied.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.