Clinical trial Recruiting at 3 sites Observational Ages 18 and up

Decision support for patients with chest pain in emergency settings

Official title: Development of a Decision Support System in the Assessment of Patients With Chest Pain in the Prehospital Setting

Run by Vastra Gotaland Region · NCT05767619 · Data updated 2026-11-12

This study is testing a new system that uses machine learning to help emergency medical teams better understand and treat patients with chest pain.

Quick facts

Study typeObservational
Ages18 Years and up
SexAll
Enrollment2000 (estimated)
SponsorVastra Gotaland Region
Government
Locations3 sites (Kungsbacka, Region Halland and 2 other locations)
StatusRecruiting
StartedMay 15, 2023
Main results expectedMarch 2025
Study endsMarch 2027
Registry record updatedJune 18, 2023
Trial IDNCT05767619 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Ideal candidates are patients experiencing chest pain or discomfort who are in contact with emergency medical services.

Probably not a fit

Patients outside the geographical catchment area of the participating emergency medical services or those under 18 years old may not benefit from this study.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This observational study aims to develop a machine learning-based decision support system for patients experiencing chest pain while in contact with emergency medical services. Participants will provide information about their symptoms and medical history, and consent to the collection of routine medical data, including a blood sample for troponin and glucose testing. The goal is to improve the assessment and management of chest pain in prehospital settings, ensuring patients receive appropriate care based on their condition. The study focuses on enhancing patient safety and optimizing emergency care responses.

Why it matters

Potential benefit: If successful, this study could lead to more accurate and timely management of chest pain, potentially improving patient outcomes.

How similar studies have gone: Other studies have shown promise in using machine learning for decision support in emergency care, indicating potential for success in this approach.

Who can join: the full rules

Show full inclusion / exclusion criteria
Inclusion Criteria:

* Patient in contact with the emergency medical service and patient main symptom is chest pain or chest discomfort
* Primary assignment (not assessed by physician in primary care, hospital)

Exclusion Criteria:

* Assignment taking place outside participating emergency medical service organisation geographical catchment area
* Under 18 years of age
* Unwillingness to participate
* Unable to participate (language, dementia, etc.)
* Other

Where it's running

  • Department of Prehospital Emergency Care,Region Halland
    Kungsbacka, Region Halland, Sweden
    Recruiting
  • Department of Prehospital Emergency Care, Sahlgrenska University Hospital
    Gothenburg, Region Vastra Gotaland, Sweden
    Recruiting
  • Department of Prehospital Emergency Care, Skaraborg
    Lidköping, Region Vastra Gotaland, Sweden
    Recruiting

Study contacts

  • Principal investigator: Carl Magnusson, PhD,RN — University of Gothenburg, Sahlgrenska University hospital
  • Study coordinator: Carl Magnusson, PhD,RN
  • Email: carl.magnusson@vgregion.se
  • Phone: +46708868620

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.