Simulated Patients to Demonstrate Common Stroke Syndromes: Accurate and Convincing

Abstract

Introduction: Stroke detection in the preclinical setting is challenging, resulting in more than 1/3 of missed strokes by emergency medical services (EMS) personnel. Recently, prehospital identification of anterior large vessel occlusion (LVO) stroke has come into focus. Cortical signs have a high predictive value for the presence of LVO stroke but are often missed. Simulated patients (SPs) could be an excellent tool to train EMS personnel in the evaluation of stroke syndromes with cortical symptoms, but it has not been studied whether they can simulate these important signs convincingly. The main objective of this study was, thus, to examine whether SPs can simulate stroke syndromes and symptoms so that stroke experts can identify them correctly and reliably, applying the NIH stroke scale (NIHSS). Methods: Lay actors were trained to simulate one of 8 stroke syndromes either typical of a lacunary stroke or of an anterior LVO stroke and then videotaped during an examination according to the NIHSS. Stroke experts were asked to rate each item of the NIHSS based on the videos, determine which stroke syndrome was being demonstrated, and rate the quality of simulation. The primary outcome was the correct identification of the target stroke syndrome by the expert raters. Secondary outcomes were the agreement of the rating of the NIHSS score with the target NIHSS score and the expert raters’ assessment of the quality of simulation. Results: Seven of eight syndromes were rated correctly by at least twelve of fifteen raters and the mean rated NIHSS score was within one point of the target score for six of eight syndromes. The mean rating for the quality of simulation was between 3.54 and 3.98 (as rated on a scale from 1 to 4) for each syndrome. Discussion/Conclusion: SPs are capable of simulating acute stroke symptoms and syndromes accurately and convincingly. Thus, they represent a great resource to improve educational interventions that improve stroke recognition.