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    Development and validation of a pragmatic prehospital tool to identify stroke mimic patients

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    Author
    McClelland, Graham cc
    Rodgers, Helen
    Flynn, Darren
    Price, Christopher
    Keyword
    Emergency Medical Services
    Ambulance Services
    Blood Pressure
    Stroke
    Diagnostic Techniques and Procedures
    Journal title
    BMJ Open
    
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/20.500.12417/359
    DOI
    10.1136/bmjopen-2018-EMS.6
    Abstract
    Aim Stroke mimics (SM) are non-stroke conditions producing stroke-like symptoms. Prehospital stroke identification tools prioritise sensitivity over specificity.1 It is estimated that >25% of prehospital suspected stroke patients are SM.2 Failure to identify SM creates inefficient use of ambulances and specialist stroke services. We developed a pragmatic tool to identify SM amongst suspected prehospital stroke patients. Method The tool was developed using regression analysis of clinical variables documented in ambulance records of suspected stroke patients linked to primary hospital diagnoses (derivation dataset, n=1,650, 40% SM).3 It was refined using feedback from paramedics (n=3) and hospital clinicians (n=9), and analysis of an expanded prehospital derivation dataset (n=3,797, 41% SM (original 1650 patients included)). Results The STEAM tool combines six variables: 1 point for Systolic blood pressure <90 mmHg; 1 point for Temperature >38.5°C with Abstracts A2 BMJ Open 2018;8(Suppl 1):A1–A34 (NHS). Protected by copyright. on 14 August 2019 at Manchester University NHS Foundation Trust http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2018-EMS.6 on 16 April 2018. Downloaded from heart rate >90 bpm; 1 point for seizures or 2 points for seizures with known diagnosis of Epilepsy; 1 point for Age <40 years or 2 points for age <30 years; 1 point for headache with known diagnosis of Migraine; 1 point for FAST-ve. A score of 2 on STEAM predicted SM diagnosis in the derivation dataset with 5.5% sensitivity, 99.6% specificity and positive predictive value (PPV) of 91.4%. External validation (n=1,848, 33% SM) showed 5.5% sensitivity, 99.4% specificity and a PPV of 82.5%. Conclusion STEAM uses common clinical characteristics to identify SM patients with high certainty. The benefits of using STEAM to reduce SM admissions to stroke services need to be weighed up against delayed admissions for stroke patients wrongly identified as SM. https://bmjopen.bmj.com/content/8/Suppl_1/A2.3 This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ http://dx.doi.org/10.1136/bmjopen-2018-EMS.6
    ae974a485f413a2113503eed53cd6c53
    10.1136/bmjopen-2018-EMS.6
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