Stroke Sensitivity Calculation in Medical Emergency Calls and Factors Associated With Stroke Suspicion: A Retrospective Registry-Based Study - 17/10/25

Doi : 10.1016/j.annemergmed.2025.04.028

Emil Iversen, MD ^a,^b,^j,^⁎ , Hege Ihle-Hansen, PhD ^c, Kari Krizak Halle, PhD ^d, Alexander Selvikvåg Lundervold, PhD ^e, Lars Myrmel, RN ^f, Anders Strand Vestbø, PhD ^g, Annette Fromm, PhD, MSc ^h, Christian Autenried, PhD ⁱ, Guttorm Brattebø, MD ^a,^j
^a Norwegian National Advisory Unit on Emergency Medical Communication, Haukeland University Hospital, Bergen, Norway
^b Oslo Emergency Medical Communication Centre, Pre-hospital Clinic, Oslo University Hospital, Oslo, Norway
^c Department of Medicine, Vestre Viken Hospital Trust, Drammen, Norway
^d Department of Medical Quality Registries, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
^e Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
^f Department of Anaesthesia and Surgical Services, Haukeland University Hospital, Bergen, Norway
^g Department of Research and Development, Haukeland University Hospital, Bergen, Norway
^h Department of Neurology, Haukeland University Hospital, Bergen, Norway
ⁱ Team Artificial Intelligence, Helse Vest ICT, Bergen, Norway
^j Department of Clinical Medicine, University of Bergen, Bergen, Norway

^∗Corresponding Author.

Abstract

Study objective

Sensitivity for stroke detection in emergency medical communication centers (EMCCs) varies widely. Few studies offer detailed insights into the out-of-hospital pathways of patients with stroke. This study explored the ability of EMCCs to detect strokes in medical emergency calls using an alternative method for estimating stroke sensitivity in EMCCs, thereby laying the foundation for developing artificial intelligence decision-support tools. It also identified factors associated with stroke recognition in medical emergency calls.

Methods

Overall, 1,164 patients with stroke in the catchment area of Bergen EMCC in 2018 and 2019 were included, and a data set from the EMCC was established manually and linked with data from the Norwegian Stroke Registry for analysis. Descriptive analysis and multivariable logistic regression were performed on data obtained from patients primarily assessed by the EMCC (n=838).

Results

Stroke detection sensitivity was 76.8% in our study using the alternative method compared with 63.4% with traditional methods. Logistic regression analysis showed a positive association between stroke suspicion and ischemic strokes (odds ratio [OR]=0.317 [0.209 to 0.481]) and wake-up strokes (OR=1.716 [1.110 to 2.653]). Among Norwegian Stroke Registry symptoms, only aphasia/dysarthria was positively associated with stroke suspicion (OR=1.600 [1.087 to 2.353]), whereas leg paresis (OR=0.609 [0.390 to 0.953]) and vertigo (OR=0.376 [0.204 to 0.694]) were negatively associated.

Conclusion

We employed an alternative method for estimating EMCC stroke sensitivity and identified factors of particular interest for future EMCC research, relevant when developing and testing decision-support tools, such as artificial intelligence.

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Keywords : Decision support, Emergency medical dispatch, Emergency medical communication center, Medical emergency call, Stroke

Plan

Introduction

Background

Importance

Goals of This Investigation

Materials and Methods

Study Design and Setting

Patient Characteristics

EMCC Stroke Sensitivity

Factors Associated With Stroke Suspicion

Limitations

Discussion

	Please see page 534 for the Editor’s Capsule Summary of this article.
	Supervising editors: Diane Kuhn, MD, PhD; David L. Schriger, MD, MPH. Specific detailed information about possible conflict of interest for individual editors is available at editors.
	Author contributions: GB and EI researched the literature, conceived the study, and developed the protocol in collaboration with ASL and CA. EI wrote the first draft of the manuscript, obtained ethical approval, and was responsible for all patient contact. LM, CA, KKH, and EI extracted and analyzed the data. All authors read and contributed substantially to the manuscript and approved the final version. EI takes responsibility for the paper as a whole.
	Data sharing statement: The data set generated and analyzed during the current study is not publicly available due to legal regulations concerning sensitive health data. A deidentified data set, data dictionary, and analytic code for this investigation is available on reasonable request, from the date of publication. Please contact Emil Iversen, MD, at email emil.iversen@helse-bergen.no.
	All authors attest to meeting the four ICMJE.org authorship criteria: (1) Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND (2) Drafting the work or revising it critically for important intellectual content; AND (3) Final approval of the version to be published; AND (4) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
	Funding and support: By Annals’ policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org/). The authors have stated that no such relationships exist. This work was supported by The Research Council of Norway (grant number 331965), in addition to departmental funding from Bergen Health Trust. The project also received a grant from Laerdal Foundation. The authors declare that they have no competing interests.
	Trial registration number: ClinicalTrials.gov ID NCT04648449
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