Validation of a shortened FAST-ED algorithm for smartphone app guided stroke triage

Frank, Benedikt; Fabian, Felix; Brune, Bastian; Bozkurt, Bessime; Deuschl, Cornelius; Nogueira, Raul G; Kleinschnitz, Christoph; Köhrmann, Martin

doi:10.1177/17562864211057639

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…It also has a database of all regional stroke centers according to their capability to provide endovascular treatment and Global Positioning System (GPS) technology with real-time traffic information to compute the transportation times to the different neighbouring stroke centers [ 31 ]. A retrospective study conducted on 2,815 patients affected by LVO showed that FAST-ED point ≥4 revealed very good accuracy in detecting LVO with an overall sensitivity of 77% and a specificity of 87% [ 33 ]. Similar to the FAST-ED App is the Egyptian Stroke Network (ESN) App [ 34 ], which features a screening tool based on questions aimed at detecting stroke signs, a video communication system to connect with the in-hospital stroke teams, and a real-time information system to localize the nearest hospital providing endovascular therapy.…”

Section: Resultsmentioning

confidence: 99%

“…31 A retrospective study conducted on 2,815 patients affected by LVO showed that FAST-ED point ≥4 revealed very good accuracy in detecting LVO with an overall sensitivity of 77% and a specificity of 87%. 33 Similar to the FAST-ED App is the Egyptian Stroke Network (ESN) App, 34 which features a screening tool based on questions aimed at detecting stroke signs, a video communication system to connect with the in-hospital stroke teams, and a real-time information system to localize the nearest hospital providing endovascular therapy. Outcomes such as DNT and vessel occlusion detection rate were evaluated by comparing ESN App and non-ESN App triage.…”

Section: Pre-hospital Appsmentioning

confidence: 99%

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Smartphone App in Stroke Management: A Narrative Updated Review

et al. 2022

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The spread of smartphones and mobile-Health (m-health) has progressively changed clinical practice, implementing access to medical knowledge and communication between doctors and patients. Dedicated software called Applications (or Apps), assists the practitioners in the various phases of clinical practice, from diagnosis to follow-up and therapy management. The impact of this technology is even more important in diseases such as stroke, which are characterized by a complex management that includes several moments: primary prevention, acute phase management, rehabilitation, and secondary prevention. This review aims to evaluate and summarize the available literature on Apps for the clinical management of stroke. We described their potential and weaknesses, discussing potential room for improvement. Medline databases were interrogated for studies concerning guideline-based decision support Apps for stroke management and other medical scenarios from 2007 (introduction of the first iPhone) until January 2022. We found 551 studies. Forty-three papers were included because they fitted the scope of the review. Based on their purpose, Apps were classified into three groups: primary prevention Apps, acute stroke management Apps, and post-acute stroke Apps. We described the aim of each App and, when available, the results of clinical studies. For acute stroke, several Apps have been designed with the primary purpose of helping communication and sharing of patients’ clinical data among healthcare providers. However, interactive systems Apps aiming to assist clinicians are still lacking, and this field should be developed because it may improve stroke patients’ management.

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Section: Resultsmentioning

confidence: 99%

Section: Pre-hospital Appsmentioning

confidence: 99%

Smartphone App in Stroke Management: A Narrative Updated Review

et al. 2022

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“…The FAST score is a traditional tool for identifying strokes with large vessel occlusion [37]. For patients suspected of acute stroke, questions including facial drooping, arm weakness, and slurred speech should be evaluated according to the FAST score [38,39].…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital

Pan

et al. 2023

BMC Neurol

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Objectives The early detection and identification of stroke are essential to the prognosis of patients with suspected stroke symptoms out-of-hospital. We aimed to develop a risk prediction model based on the FAST score to identify the different types of strokes early for emergency medical services (EMS). Methods This retrospective observational study enrolled 394 stroke patients at a single center from January 2020 to December 2021. Demographic data, clinical characteristics, and stroke risk factors with patients were collected from the EMS record database. Univariate and multivariate logistic regression analysis was used to identify the independent risk predictors. The nomogram was developed based on the independent predictors, in which the discriminative value and calibration of the nomogram were verified by the receiver operator characteristic (ROC) curve and calibration plots. Results A total of 31.90% (88/276) of patients were diagnosed with hemorrhagic stroke in the training set, while 36.40% (43/118) in the validation set. The nomogram was developed based on the multivariate analysis, including age, systolic blood pressure, hypertension, vomiting, arm weakness, and slurred speech. The area under the curve (AUC) of the ROC with nomogram was 0.796 (95% CI: 0.740–0.852, P < 0.001) and 0.808 (95% CI:0.728–0.887, P < 0.001) in the training set and validation set, respectively. In addition, the AUC with the nomogram was superior to the FAST score in both two sets. The calibration curve showed a good agreement with the nomogram and the decision curves analysis also demonstrated that the nomogram had a wider range of threshold probabilities than the FAST score in the prediction risk of hemorrhagic stroke. Conclusions This novel noninvasive clinical nomogram shows a good performance in differentiating hemorrhagic and ischemic stroke for EMS staff prehospital. Moreover, all of the variables of nomogram are acquired in clinical practice easily and inexpensively out-of-hospital.

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Section: Discussionmentioning

confidence: 99%

Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital

Pan

et al. 2022

Preprint

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Objectives: The early detection and identification of stroke are essential to the prognosis of patients with suspected stroke symptoms out-of-hospital. We aimed to develop a risk prediction model based on the FAST score to identify the different types of strokes early for emergency medical services (EMS). Methods: This retrospective observational study enrolled 394 stroke patients at a single center from January 2020 to December 2021. Demographic data, clinical characteristics, and stroke risk factors with patients were collected from the EMS record database. Univariate and multivariate logistic regression analysis was used to identify the independent risk predictors. The nomogram was developed based on the independent predictors, in which the discriminative value and calibration of the nomogram were verified by the receiver operator characteristic (ROC) curve and calibration plots. Results: A total of 31.90% (88/276) of patients were diagnosed with hemorrhagic stroke in the training set, while 36.40% (43/118) in the validation set. The nomogram was developed based on the multivariate analysis, including age, systolic blood pressure, hypertension, vomiting, arm weakness, and slurred speech. The area under the curve (AUC) of the ROC with nomogram was 0.796 (95% CI: 0.740-0.852,P<0.001) and 0.808 (95% CI:0.728-0.887, P<0.001) in the training set and validation set, respectively. In addition, the AUC with the nomogram was superior to the FAST score in both two sets. The calibration curve showed a good agreement with the nomogram and the decision curves analysis also demonstrated that the nomogram had a wider range of threshold probabilities than the FAST score in the prediction risk of hemorrhagic stroke. Conclusions: This novel noninvasive clinical nomogram shows a good performance in differentiating hemorrhagic and ischemic stroke for EMS staff prehospital. Moreover, all of the variables of nomogram are acquired in clinical practice easily and inexpensively out-of-hospital.

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Validation of a shortened FAST-ED algorithm for smartphone app guided stroke triage

Cited by 5 publications

References 22 publications

Smartphone App in Stroke Management: A Narrative Updated Review

Smartphone App in Stroke Management: A Narrative Updated Review

Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital

Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital

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