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Background and purpose: Mobile stroke units (MSU) have been demonstrated to improve prehospital stroke care in metropolitan and rural regions. Due to geographical, social and structural idiosyncrasies of the German city of Mannheim, concepts of established MSU services are not directly applicable to the Mannheim initiative. The aim of the present analysis was to identify major determinants that need to be considered when initially setting up a local MSU service. Methods: Local stroke statistics from 2015 to 2021 were analyzed and circadian distribution of strokes and local incidence rates were calculated. MSU patient numbers and total program costs were estimated for varying operating modes, daytime coverage models, staffing configurations which included several resource sharing models with the hospital. Additional case-number simulations for expanded catchment areas were performed. Results: Median time of symptom onset of ischemic stroke patients was 1:00 p.m. 54.3% of all stroke patients were admitted during a 10-h time window on weekdays. Assuming that MSU is able to reach 53% of stroke patients, the average expected number of ischemic stroke patients admitted to MSU would be 0.64 in a 10-h shift each day, which could potentially be increased by expanding the MSU catchment area. Total estimated MSU costs amounted to 815,087 per annum. Teleneurological assessment reduced overall costs by 11.7%. Conclusion: This analysis provides a framework of determinants and considerations to be addressed during the design process of a novel MSU program in order to balance stroke care improvements with the sustainable use of scarce resources.
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BACKGROUND AND PURPOSE: To simulate patient-level costs, analyze the economic potential of telemedicine-based mobile stroke units for acute prehospital stroke care, and identify major determinants of cost-effectiveness, based on two recent prospective trials from the United States and Germany. METHODS: A Markov decision model was developed to simulate lifetime costs and outcomes of mobile stroke unit. The model compares diagnostic and therapeutic pathways of ischemic stroke, hemorrhagic stroke, and stroke mimic patients by conventional care or by mobile stroke units. The treatment outcomes were derived from the B_PROUD and the BEST-mobile stroke unit trials and further input parameters were derived from recent literature. Uncertainty was addressed by deterministic and probabilistic sensitivity analyses. A lifetime horizon based on the US healthcare system was adopted to evaluate different cost thresholds for mobile stroke unit and the resulting cost-effectiveness. Willingness-to-pay thresholds were set at 1x and 3x gross domestic product per capita, as recommended by the World Health Organization. RESULTS: In the base case scenario, mobile stroke unit care yielded an incremental gain of 0.591 quality-adjusted life years per dispatch. Mobile stroke unit was highly cost-effective up to a maximum average cost of 43,067 US dollars per patient. Sensitivity analyses revealed that MSU cost-effectiveness is mainly affected by reduction of long-term disability costs. Also, among other parameters, the rate of stroke mimics patients diagnosed by MSU plays an important role. CONCLUSION: This study demonstrated that mobile stroke unit can possibly be operated on an excellent level of cost-effectiveness in urban areas in North America with number of stroke mimic patients and long-term stroke survivor costs as major determinants of lifetime cost-effectiveness.
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OBJECTIVE: Referrals to neurology in emergency departments (ED) are continuously increasing, currently representing 15% of all admissions. Existing triage systems were developed for general medical populations and have not been validated for patients with neurological symptoms. METHODS: To characterize neurological emergencies, we first retrospectively analyzed symptoms, service times and resources of the cohort of neurological referrals to a German interdisciplinary ED (IED) during 2017 according to urgency determined by final IED diagnosis. In a second step, we performed a retrospective assignment of consecutive patients presenting in April 2017 according to internal guidelines as either acute (requiring diagnostic/therapeutic procedures within 24 hrs) or non-acute neurological conditions as well as a retrospective classification according to the Emergency Severity Index (ESI). Both assessments were compared with the urgency according to the final ER diagnosis. RESULTS: In a 12-month period, 36.4% of 5340 patients were rated as having an urgent neurological condition; this correlated with age, door-to-doctor time, imaging resource use and admission (p < 0.001, respectively). In a subset of 275 patients, 59% were retrospectively triaged as acute according to neurological expertise and 48% according to ESI categories 1 and 2. Neurological triage identified urgency with a significantly higher sensitivity (94.8, p < 0.01) but showed a significantly lower specificity (55.1, p < 0.05) when compared to ESI (80.5 and 65.2, respectively). CONCLUSION: The ESI may not take specific aspects of neurological emergency (eg, time-sensitivity) sufficiently into account. Refinements of existing systems or supplementation with dedicated neurological triage tools based on neurological expertise and experience may improve the triage of patients with neurological symptoms.