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Development of a prehospital prediction model for risk stratification of patients with chest pain
Göteborgs universitet.
Göteborgs universtitet.
University of Borås, Faculty of Caring Science, Work Life and Social Welfare.ORCID iD: 0000-0003-4139-6235
Högskolan i Halmstad.
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2022 (English)In: American Journal of Emergency Medicine, ISSN 0735-6757, E-ISSN 1532-8171, Vol. 51, p. 26-31Article in journal (Refereed) Published
Abstract [en]

Introduction: Chest 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. Methods: This 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. Results: Prediction 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. Conclusions: Models 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. © 2021 The Authors

Place, publisher, year, edition, pages
W.B. Saunders , 2022. Vol. 51, p. 26-31
Keywords [en]
Chest pain, Emergency medical services, Prehospital care, Risk assessment, Triage, troponin T, adult, Article, atrial fibrillation, cohort analysis, controlled study, demography, diagnostic accuracy, diagnostic test accuracy study, electrocardiogram, emergency health service, female, heart failure, high risk patient, human, male, observational study, prediction, receiver operating characteristic, ST segment depression, thorax pain, workflow
National Category
Cardiac and Cardiovascular Systems Health Sciences
Research subject
The Human Perspective in Care
Identifiers
URN: urn:nbn:se:hb:diva-26985DOI: 10.1016/j.ajem.2021.09.079ISI: 000711974100001Scopus ID: 2-s2.0-85117137341OAI: oai:DiVA.org:hb-26985DiVA, id: diva2:1616304
Available from: 2021-12-02 Created: 2021-12-02 Last updated: 2022-01-06Bibliographically approved

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Herlitz, Johan

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