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

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...

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Autores principales: Ye, Sheng, Pan, Huiqing, Li, Weijia, Wang, Jinqiang, Zhang, Hailong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9983153/
https://www.ncbi.nlm.nih.gov/pubmed/36864378
http://dx.doi.org/10.1186/s12883-023-03138-1
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author Ye, Sheng
Pan, Huiqing
Li, Weijia
Wang, Jinqiang
Zhang, Hailong
author_facet Ye, Sheng
Pan, Huiqing
Li, Weijia
Wang, Jinqiang
Zhang, Hailong
author_sort Ye, Sheng
collection PubMed
description 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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spelling pubmed-99831532023-03-04 Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital Ye, Sheng Pan, Huiqing Li, Weijia Wang, Jinqiang Zhang, Hailong BMC Neurol Research 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. BioMed Central 2023-03-03 /pmc/articles/PMC9983153/ /pubmed/36864378 http://dx.doi.org/10.1186/s12883-023-03138-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Ye, Sheng
Pan, Huiqing
Li, Weijia
Wang, Jinqiang
Zhang, Hailong
Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital
title Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital
title_full Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital
title_fullStr Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital
title_full_unstemmed Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital
title_short Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital
title_sort development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9983153/
https://www.ncbi.nlm.nih.gov/pubmed/36864378
http://dx.doi.org/10.1186/s12883-023-03138-1
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