Portable stroke detection devices: a systematic scoping review of prehospital applications.


Journal

BMC emergency medicine
ISSN: 1471-227X
Titre abrégé: BMC Emerg Med
Pays: England
ID NLM: 100968543

Informations de publication

Date de publication:
16 06 2022
Historique:
received: 08 02 2022
accepted: 13 05 2022
entrez: 16 6 2022
pubmed: 17 6 2022
medline: 22 6 2022
Statut: epublish

Résumé

The worldwide burden of stroke remains high, with increasing time-to-treatment correlated with worse outcomes. Yet stroke subtype determination, most importantly between stroke/non-stroke and ischemic/hemorrhagic stroke, is not confirmed until hospital CT diagnosis, resulting in suboptimal prehospital triage and delayed treatment. In this study, we survey portable, non-invasive diagnostic technologies that could streamline triage by making this initial determination of stroke type, thereby reducing time-to-treatment. Following PRISMA guidelines, we performed a scoping review of portable stroke diagnostic devices. The search was executed in PubMed and Scopus, and all studies testing technology for the detection of stroke or intracranial hemorrhage were eligible for inclusion. Extracted data included type of technology, location, feasibility, time to results, and diagnostic accuracy. After a screening of 296 studies, 16 papers were selected for inclusion. Studied devices utilized various types of diagnostic technology, including near-infrared spectroscopy (6), ultrasound (4), electroencephalography (4), microwave technology (1), and volumetric impedance spectroscopy (1). Three devices were tested prior to hospital arrival, 6 were tested in the emergency department, and 7 were tested in unspecified hospital settings. Median measurement time was 3 minutes (IQR: 3 minutes to 5.6 minutes). Several technologies showed high diagnostic accuracy in severe stroke and intracranial hematoma detection. Numerous emerging portable technologies have been reported to detect and stratify stroke to potentially improve prehospital triage. However, the majority of these current technologies are still in development and utilize a variety of accuracy metrics, making inter-technology comparisons difficult. Standardizing evaluation of diagnostic accuracy may be helpful in further optimizing portable stroke detection technology for clinical use.

Sections du résumé

BACKGROUND
The worldwide burden of stroke remains high, with increasing time-to-treatment correlated with worse outcomes. Yet stroke subtype determination, most importantly between stroke/non-stroke and ischemic/hemorrhagic stroke, is not confirmed until hospital CT diagnosis, resulting in suboptimal prehospital triage and delayed treatment. In this study, we survey portable, non-invasive diagnostic technologies that could streamline triage by making this initial determination of stroke type, thereby reducing time-to-treatment.
METHODS
Following PRISMA guidelines, we performed a scoping review of portable stroke diagnostic devices. The search was executed in PubMed and Scopus, and all studies testing technology for the detection of stroke or intracranial hemorrhage were eligible for inclusion. Extracted data included type of technology, location, feasibility, time to results, and diagnostic accuracy.
RESULTS
After a screening of 296 studies, 16 papers were selected for inclusion. Studied devices utilized various types of diagnostic technology, including near-infrared spectroscopy (6), ultrasound (4), electroencephalography (4), microwave technology (1), and volumetric impedance spectroscopy (1). Three devices were tested prior to hospital arrival, 6 were tested in the emergency department, and 7 were tested in unspecified hospital settings. Median measurement time was 3 minutes (IQR: 3 minutes to 5.6 minutes). Several technologies showed high diagnostic accuracy in severe stroke and intracranial hematoma detection.
CONCLUSION
Numerous emerging portable technologies have been reported to detect and stratify stroke to potentially improve prehospital triage. However, the majority of these current technologies are still in development and utilize a variety of accuracy metrics, making inter-technology comparisons difficult. Standardizing evaluation of diagnostic accuracy may be helpful in further optimizing portable stroke detection technology for clinical use.

Identifiants

pubmed: 35710360
doi: 10.1186/s12873-022-00663-z
pii: 10.1186/s12873-022-00663-z
pmc: PMC9204948
doi:

Types de publication

Journal Article Review Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

111

Informations de copyright

© 2022. The Author(s).

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Auteurs

Susmita Chennareddy (S)

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building, 8th Floor, New York, NY, 10029, USA. susmita.chennareddy@icahn.mssm.edu.

Roshini Kalagara (R)

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building, 8th Floor, New York, NY, 10029, USA.

Colton Smith (C)

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building, 8th Floor, New York, NY, 10029, USA.

Stavros Matsoukas (S)

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building, 8th Floor, New York, NY, 10029, USA.

Abhiraj Bhimani (A)

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building, 8th Floor, New York, NY, 10029, USA.

John Liang (J)

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building, 8th Floor, New York, NY, 10029, USA.

Steven Shapiro (S)

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building, 8th Floor, New York, NY, 10029, USA.

Reade De Leacy (R)

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building, 8th Floor, New York, NY, 10029, USA.

Maxim Mokin (M)

Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL, USA.

Johanna T Fifi (JT)

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building, 8th Floor, New York, NY, 10029, USA.

J Mocco (J)

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building, 8th Floor, New York, NY, 10029, USA.

Christopher P Kellner (CP)

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building, 8th Floor, New York, NY, 10029, USA.

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