The goal of this observational study is to develop a decision support system in patients presenting with chest pain in the prehospital setting. The main question it aims to answer is: • Performance of a machine learning based model for decision support of patients in contact with emergency medical services due to chest pain Participants will be asked to: * respond to questions asked by the clinician at the scene regarding previous known risk factors and pain characteristics * consent to the collection of routinely available data from medical records * consent of taking one blood sample capillary or venous (if perifer catheter is placed for standard care reasons) troponin and glucose which is measured at the scene, disposed, and the result is entered in the clinical report form.
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Clinical outcome prediction of the decision support model based on a composite outcome
Timeframe: Time-sensitive condition: from EMS inclusion to hospital discharge follow-up time 1 day up to 100 days; Death: from EMS inclusion up to seven days; Adverse events: from EMS inclusion up to 72 hours.