Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine (Aug 2019)

Chest pain in the ambulance; prevalence, causes and outcome - a retrospective cohort study

  • Claus Kjær Pedersen,
  • Carsten Stengaard,
  • Kristian Friesgaard,
  • Karen Kaae Dodt,
  • Hanne Maare Søndergaard,
  • Christian Juhl Terkelsen,
  • Morten Thingemann Bøtker

DOI
https://doi.org/10.1186/s13049-019-0659-6
Journal volume & issue
Vol. 27, no. 1
pp. 1 – 10

Abstract

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Abstract Background Chest pain is common in acute ambulance transports. This study aims to characterize and compare ambulance-transported chest pain patients to non-chest pain patients and evaluate if patient characteristics and accompanying symptoms accessible at the time of emergency call can predict cause and outcome in chest pain patients. Methods Retrospective, observational population-based study, including acute ambulance transports. Patient characteristics and symptoms are included in a multivariable risk model to identify characteristics, associated with being discharged without an acute cardiac diagnosis and surviving 30 days after chest pain event. Results In total, 10,033 of 61,088 (16.4%) acute ambulance transports were due to chest pain. In chest pain patients, 30-day mortality was 2.1% (95%CI 1.8–2.4) compared to 6.0% (95%CI 5.7–6.2) in non-chest pain patients. Of chest pain patients, 1054 (10.5%) were diagnosed with acute myocardial infarction, and 5068 (50.5%) were discharged without any diagnosis of disease. This no-diagnosis group had very low 30-day mortality, 0.4% (95%CI 0.2–0.9). Female gender, younger age, chronic pulmonary disease, absence of accompanying symptoms of dyspnoea, radiation, severe pain for > 5 min, clammy skin, uncomfortable, and nausea were associated with being discharged without an acute cardiac diagnosis and surviving 30 days after a chest pain event. Conclusion Chest pain is a common reason for ambulance transport, but the majority of patients are discharged without a diagnosis and with a high survival rate. Early risk prediction seems to hold a potential for resource downgrading and thus cost-saving in selected chest pain patients.

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