Show simple item record

dc.contributor.authorMcClelland, Graham
dc.contributor.authorRodgers, Helen
dc.contributor.authorFlynn, Darren
dc.contributor.authorPrice, Christopher I.M.
dc.date.accessioned2019-10-16T09:16:10Z
dc.date.available2019-10-16T09:16:10Z
dc.date.issued2018-04
dc.identifier.citationMcClelland, G. et al, 2018. Development and validation of a pragmatic prehospital tool to identify stroke mimic patients. BMJ Open, 8 (Suppl. 1), A2-A3.en_US
dc.identifier.issn2044-6055
dc.identifier.doi10.1136/bmjopen-2018-EMS.6
dc.identifier.urihttp://hdl.handle.net/20.500.12417/359
dc.description.abstractAim 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
dc.language.isoenen_US
dc.subjectEmergency Medical Servicesen_US
dc.subjectAmbulance Servicesen_US
dc.subjectBlood Pressureen_US
dc.subjectStrokeen_US
dc.subjectDiagnostic Techniques and Proceduresen_US
dc.titleDevelopment and validation of a pragmatic prehospital tool to identify stroke mimic patientsen_US
dc.typeConference Paper/Proceeding/Abstract
dc.source.journaltitleBMJ Openen_US
dcterms.dateAccepted2019-08-14
rioxxterms.versionNAen_US
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden_US
rioxxterms.licenseref.startdate2019-08-14
refterms.panelUnspecifieden_US
refterms.dateFirstOnline2018-04
html.description.abstractAim 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.6en_US


This item appears in the following Collection(s)

Show simple item record