BACKGROUND AND OBJECTIVE: Accurate identification is one of the cornerstones of prehospital care for patients with suspected acute ischaemic stroke (AIS). In the Lausanne Ischemic Stroke Pathway triage system, emergency medical service (EMS) providers use dedicated criteria to identify acute reperfusion candidates and call a coordinated medical team (CMT, including a neurologist and an emergency physician), which can activate the stroke code pathway. The objective of this study was to evaluate the performance of the system in identifying acute reperfusion candidates. METHODS: This was a retrospective single-centre quality-control study conducted in a Swiss university-based stroke centre, from 1 January 2019 to 31 May 2022. Inclusion criteria were patients aged ≥18 years, for whom the EMS called the CMT or patients with AIS eligible for acute reperfusion managed by the EMS. The main outcomes were the performance of both the EMS and the CMT in detecting AIS patients eligible for acute reperfusion, as assessed through their respective under- and overtriage rates. A secondary outcome was the identification of alternative diagnoses in overtriaged patients. RESULTS: Of 1071 patients included, the EMS phone-contacted the CMT for 937 (87.5%) of them. Among the 1071 patients, 440 (41%) had an AIS eligible for reperfusion. The system’s undertriage rate was 30% (133 patients without stroke pathway activation among the 440 patients with an AIS eligible for acute reperfusion). The undertriage rates were 21% for the EMS and 11% for the CMT. The overtriage rates were 63% for the EMS and 57% for the CMT. The main alternative diagnoses in overtriaged patients were seizures (14%), intracranial haemorrhages (9.9%) and functional disorders (6.5%). CONCLUSION: Undertriage and overtriage rates were relatively high, but both were reduced by the CMT call, highlighting the added value of a second expert assessment step, with a higher level of expertise. Concerning overtriage, a better prehospital identification of alternative diagnoses, such as seizures or functional disorders, could help to better classify our patients.
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Bianchi et al. (2026) studied this question.
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