Ambulance Clinical Triage for Acute Stroke Treatment

Zhao, Henry; Pesavento, Lauren; Coote, Skye; Rodrigues, Edrich; Salvaris, Patrick; Smith, Karen; Bernard, Stephen; Stephenson, Michael; Чурилов, Леонид; Yassi, Nawaf; Davis, Stephen M.; Campbell, Bruce

doi:10.1161/strokeaha.117.019307

Cited by 59 publications

(43 citation statements)

References 23 publications

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“…Comparing the FAST's validity features with previous study findings, Zhao et al (15) found that the sensitivity, specificity, and PPV of EMS personnel were 85.7%, 93.5%, and 80%, respectively, using FAST for the diagnosis of acute stroke in the pre-hospital emergency. Therefore, their study found more sensitivity and specificity than our study (15). In the study by Caceres et al (16), patients diagnosed with stroke by EMDs were transferred to the hospital significantly sooner than those not diagnosed with stroke by EMDs.…”

Section: Discussioncontrasting

confidence: 54%

Accuracy of Stroke Diagnosis Using FAST (Face, Arm, Speech, Time) Tool by Emergency Medical Service Dispatchers and Technicians and its Impact on Transport Time

et al. 2019

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Objectives: This study aimed to determine the accuracy of the Face-Arm-Speech-Time (FAST) screening tool used by emergency medical dispatchers (EMDs) and emergency medical technicians (EMTs) for the diagnosis of acute stroke and its effect on the patient's transfer time from the scene to the hospital. Methods: This study was conducted retrospectively during a one-year period. Via census sampling, we recruited all patients over 18-years-old diagnosed with acute stroke whether by EMDs or by EMTs. Pre-hospital operation forms and hospital records were used for data gathering. Final diagnoses were finally determined according to the patients' brain magnetic resonance imaging (MRI). Results: Totally, 1,280 patients with a mean age of 64.1 ± 17.8 years were studied, of whom, 730 were men (57%). Accordingly, 1,016 probable cases of stroke (79.4%) were reported by EMDs, while only 543 cases (42.4%) were reported as suspected cases of stroke by EMTs at the scene of the incident. Ultimately, stroke was confirmed in 519 (40.5%) cases. Accordingly, the Area under the curves (AUCs) for EMDs and EMTs were 0.50 (0.48 to 0.53) and 0.74 (0.71 to 0.76), respectively. Transport time (TT) was shorter if the initial diagnosis of stroke was made by EMTs. The TT was marginally significant and patients with stroke diagnoses were transported to the hospital faster than others (17.3 ± 11.5 vs. 18.5 ± 11.9 min; P = 0.065). Conclusions: The use of FAST by EMTs for detecting probable acute stroke has acceptable sensitivity and specificity; but when it is used by EMDs, it has higher sensitivity and very low specificity. The diagnosis of stroke by EMTs may lead to faster transport to the hospital.

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Section: Discussioncontrasting

confidence: 54%

Accuracy of Stroke Diagnosis Using FAST (Face, Arm, Speech, Time) Tool by Emergency Medical Service Dispatchers and Technicians and its Impact on Transport Time

et al. 2019

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“…A very recent study by Zhao et al . in stroke[15] that describes a new algorithm called ACT-FAST which uses a three-step evaluation criteria that includes: (1) arm drift within 10 s, (2) language difficulty if left arm is affected or neglect if right arm is affected, and (3) stroke mimic screen. They found that they had an overall accuracy of 81.7%, the sensitivity of 85.7%, and specificity of 93.5% in identifying LVOs, which is higher than all other existing scoring models.…”

Section: Resultsmentioning

confidence: 99%

Prehospital stroke care, a narrative review

Wang

Ding

2018

Brain Circ

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Stroke is a leading cause of disability in the United States and current treatment for stroke is limited to two modalities with well-defined time restraints. The prehospital setting is a significant and relatively easy setting for innovation in stroke care, as the most clinical decisions are made within the first several hours of symptom onset. In this review, we look at recent innovations in improving prehospital care for acute stroke including the conception of mobile stroke units, the ongoing development of stroke models for emergency providers, barriers to prehospital care, and the innovation of new telephone applications. Although there are notable improvements in acute stroke care, additional research is needed to further improve on current models and technologies.

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“…Using very conservative estimate of 10% eligible patients, the “mothership” hospital would face a deluge of patients to process to treat in order to perform ECR on 434 patients at MMC or 543 patients at RMH. A variety of tools are now available to screen patients for LVO (30, 31). However, a formal prospective field testing of these tools and the impact on hospital case load has not yet been evaluated.…”

Section: Discussionmentioning

confidence: 99%

Application of Strategic Transport Model and Google Maps to Develop Better Clot Retrieval Stroke Service

et al. 2019

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Background and purpose: Two hubs are designated to provide endovascular clot retrieval (ECR) for the State of Victoria, Australia. In an earlier study, Google Maps application programming interface (API) was used to perform modeling on the combination of hospitals optimizing for catchment in terms of current traveling time and road conditions. It is not known if these findings would remain the same if the modeling was performed with a large-scale transport demand model such as Victorian Integrated Transport Model (VITM). This model is developed by the Victorian State Government Transport has the capability to forecast travel demand into the future including future road conditions which is not possible with a Google Maps based applications. The aim of this study is to compare the travel time to potential ECR hubs using both VITM and the Google Maps API and model stability in the next 5 and 10 years. Methods: The VITM was used to generate travel time from randomly generated addresses to four existing ECR capable hubs in Melbourne city, Australia (i.e., Royal Melbourne Hospital/RMH, Monash Medical Center/MMC, Alfred Hospital/ALF, and Austin Hospital/AUS) and the optimal service boundaries given a delivering time threshold are then determined. Results: The strategic transport model and Google map methods were similar with the R 2 of 0.86 (peak and off peak) and the Nash-Sutcliffe model of efficiency being 0.83 (peak) and 0.76 (off-peak travel). Futures modeling using VITM found that this proportion decreases to 82% after 5 years and 80% after 10 years. The combination of RMH and ALF provides coverage for 74% of cases, 68% by 5 years, and 66% by 10 years. The combination of RMH and AUS provides coverage for 70% of cases in the base case, 65% at 5 years, and 63% by 10 years. Discussion: The results from strategic transport model are similar to those from Google Maps. In this paper we illustrate how this method can be applied in designing and forecast stroke service model in different cities in Australia and around the world.

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Ambulance Clinical Triage for Acute Stroke Treatment

Cited by 59 publications

References 23 publications

Accuracy of Stroke Diagnosis Using FAST (Face, Arm, Speech, Time) Tool by Emergency Medical Service Dispatchers and Technicians and its Impact on Transport Time

Accuracy of Stroke Diagnosis Using FAST (Face, Arm, Speech, Time) Tool by Emergency Medical Service Dispatchers and Technicians and its Impact on Transport Time

Prehospital stroke care, a narrative review

Application of Strategic Transport Model and Google Maps to Develop Better Clot Retrieval Stroke Service

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