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Clinical benefit of improved Prehospital stroke scales to detect stroke patients with large vessel occlusions: results from a conditional probabilistic model

Overview of attention for article published in BMC Neurology, February 2018
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Title
Clinical benefit of improved Prehospital stroke scales to detect stroke patients with large vessel occlusions: results from a conditional probabilistic model
Published in
BMC Neurology, February 2018
DOI 10.1186/s12883-018-1021-8
Pubmed ID
Authors

Ludwig Schlemm, Eckhard Schlemm

Abstract

Clinical scales to detect large vessel occlusion (LVO) may help to determine the optimal transport destination for patients with suspected acute ischemic stroke (AIS). The clinical benefit associated with improved diagnostic accuracy of these scales has not been quantified. We used a previously reported conditional model to estimate the probability of good outcome (modified Rankin scale sore ≤2) for patients with AIS and unknown vessel status occurring in regions with greater proximity to a primary than to a comprehensive stroke center. Optimal rapid arterial occlusion evaluation (RACE) scale cutoff scores were calculated based on time-dependent effect-size estimates from recent randomized controlled trials. Probabilities of good outcome were compared between a triage strategy based on these cutoffs and a strategy based on a hypothetical perfect LVO detection tool with 100% diagnostic accuracy. In our model, the additional benefit of a perfect LVO detection tool as compared to optimal transport-time dependent RACE cutoff scores ranges from 0 to 5%. It is largest for patients with medium stroke symptom severity (RACE score 5) and in geographic environments with longer transfer time between the primary and comprehensive stroke center. Based on a probabilistic conditional model, the results of our simulation indicate that more accurate prehospital clinical LVO detections scales may be associated with only modest improvements in the expected probability of good outcome for patients with suspected acute ischemic stroke and unknown vessel status.

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Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 17%
Student > Bachelor 7 11%
Student > Master 6 9%
Student > Ph. D. Student 5 8%
Student > Doctoral Student 4 6%
Other 11 17%
Unknown 21 32%
Readers by discipline Count As %
Medicine and Dentistry 18 28%
Nursing and Health Professions 7 11%
Psychology 3 5%
Neuroscience 3 5%
Economics, Econometrics and Finance 2 3%
Other 9 14%
Unknown 23 35%