• Kristoffer Wibring, Markus Lingman, Johan Herlitz, Awaiz Ashfaq, and Angela Bång.
• Department of Ambulance and Prehospital Care, Region Halland, Sweden; Institute of Health and Care Sciences, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden. Electronic address: kristoffer.wibring@gu.se.
• Am J Emerg Med. 2022 Jan 1; 51: 26-31.

IntroductionChest pain is one of the most common reasons for contacting the emergency medical services (EMS). About 15% of these chest pain patients have a high-risk condition, while many of them have a low-risk condition with no need for acute hospital care. It is challenging to at an early stage distinguish whether patients have a low- or high-risk condition. The objective of this study has been to develop prediction models for optimising the identification of patients with low- respectively high-risk conditions in acute chest pain early in the EMS work flow.MethodsThis prospective observational cohort study included 2578 EMS missions concerning patients who contacted the EMS in a Swedish region due to chest pain in 2018. All the patients were assessed as having a low-, intermediate- or high-risk condition, i.e. occurrence of a time-sensitive diagnosis at discharge from hospital. Multivariate regression analyses using data on symptoms and symptom onset, clinical findings including ECG, previous medical history and Troponin T were carried out to develop models for identification of patients with low- respectively high-risk conditions. Developed models where then tested hold-out data set for internal validation and assessing their accuracy.ResultsPrediction models for risk-stratification based on variables mutual for both low- and high-risk prediction were developed. The variables included were: age, sex, previous medical history of kidney disease, atrial fibrillation or heart failure, Troponin T, ST-depression on ECG, paleness, pain debut during activity, constant pain, pain in right arm and pressuring pain quality. The high-risk model had an area under the receiving operating characteristic curve of 0.85 and the corresponding figure for the low-risk model was 0.78.ConclusionsModels based on readily available information in the EMS setting can identify high- and low-risk conditions with acceptable accuracy. A clinical decision support tool based on developed models may provide valuable clinical guidance and facilitate referral to less resource-intensive venues.Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

27 keywords...

hide…