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

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 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.