Association between prehospital time intervals and ST-elevation myocardial infarction system performance

Jonathan R Studnek1, Lee Garvey, Tom Blackwell

  • 1Carolinas Medical Center, Center for Prehospital Medicine, Charlotte, NC 28232, USA. jonst@medic911.com

Circulation
|September 30, 2010
PubMed

Insights

Optimizing prehospital care for ST-elevation myocardial infarction (STEMI) is crucial. Key prehospital time intervals, especially scene departure to cardiac catheterization laboratory table time, significantly impact achieving the recommended 90-minute goal for percutaneous coronary intervention.

Area of Science:

  • Cardiology
  • Emergency Medicine
  • Health Systems Research

Background:

  • Current guidelines recommend percutaneous coronary intervention (PCI) within 90 minutes for ST-segment-elevation myocardial infarction (STEMI).
  • Prehospital time intervals are critical for STEMI system performance.

Purpose of the Study:

  • To assess the association between prehospital time intervals and STEMI system performance.
  • To identify key time intervals influencing the achievement of the 90-minute PCI goal.

Main Methods:

  • Analysis of prehospital ECG data from STEMI patients between May 2007 and March 2009.
  • Evaluation of specific time intervals: 9-1-1 call to ambulance arrival, ambulance to ECG, on-scene time, ECG to STEMI team notification, and scene departure to catheterization lab table.
  • Logistic regression to determine the influence of time intervals on achieving PCI within 90 minutes.

Main Results:

  • 165 of 181 STEMI patients had complete data.
  • Scene departure to catheterization lab table time (≤30 minutes) was strongly associated with achieving PCI within 90 minutes (OR 11.1).
  • Response time and on-scene time also significantly influenced system performance.

Conclusions:

  • Prehospital timing benchmarks are associated with STEMI system performance.
  • Focusing on scene departure to table time and on-scene time can improve PCI delivery within the recommended 90 minutes.
  • This model helps identify areas for system improvement with the greatest clinical impact.
Abstract

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