PhD ThesisBackground
Stroke mimic (SM) conditions produce stroke-like symptoms through diverse mechanisms.
Up to 43% of pre-hospital suspected stroke patients are SM because identification tools
prioritise sensitivity over specificity, leading to inefficient use of ambulances and stroke
services. No existing pre-hospital SM identification tools could be identified. A pragmatic SM
identification tool using easily available information from suspected stroke patients was
developed.
Methods
A systematic literature review and a national paramedic survey generated possible tool
content. Independent predictors were isolated by regression analysis of selected variables
documented in ambulance records of suspected stroke patients linked to primary hospital
diagnoses (derivation dataset, n=1,650, 40% SM). The tool was refined using an expanded
dataset (n=3,797, 41% SM), usability testing and professional focus groups. The potential
clinical impact was evaluated through basic service efficiency modelling and focus groups.
Results
The “STEAM tool” combines six variables:
1 point for Systolic blood pressure<90mmHg
1 point for Temperature>38.5oC with heart rate>90bpm
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 suspected stroke
A score of ≥2 on STEAM predicted SM diagnosis in the refinement dataset with 5.5%
sensitivity, 99.6% specificity and positive predictive value (PPV) of 91.4%. External validation
(n=1,848, 33% SM) showed 5.6% sensitivity, 99.5% specificity and a PPV of 85.0%.
Focus groups with paramedics and hospital clinicians identified benefits and risks to patients
ii
and clinical services from using STEAM.
Conclusions
A multi-method approach developed and validated a tool using common clinical
characteristics to identify a small proportion of SM patients with a high degree of certainty.
The tool appears feasible for pre-hospital use but its impact will depend upon local models
of stroke care.The Stroke Associatio