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Nomogram to predict hemorrhagic transformation for acute ischemic stroke in Western China: a retrospective analysis

BACKGROUND AND PURPOSE: Hemorrhagic transformation (HT) is the most alarming complication of acute ischemic stroke. We aimed to identify risk factors for HT in Chinese patients and attempted to develop a nomogram to predict individual cases. METHODS: A retrospective study was used to collect the dem...

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Autores principales: Zhang, Keming, Luan, Jianfang, Li, Changqing, Chen, Mingli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9040382/
https://www.ncbi.nlm.nih.gov/pubmed/35468774
http://dx.doi.org/10.1186/s12883-022-02678-2
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author Zhang, Keming
Luan, Jianfang
Li, Changqing
Chen, Mingli
author_facet Zhang, Keming
Luan, Jianfang
Li, Changqing
Chen, Mingli
author_sort Zhang, Keming
collection PubMed
description BACKGROUND AND PURPOSE: Hemorrhagic transformation (HT) is the most alarming complication of acute ischemic stroke. We aimed to identify risk factors for HT in Chinese patients and attempted to develop a nomogram to predict individual cases. METHODS: A retrospective study was used to collect the demographic and clinical characteristics of ischemic stroke patients at the Second Affiliated Hospital of Chongqing Medical University (development cohort) and Chongqing Sanbo Changan Hospital (validation cohort) from October 2013 to August 2020. Univariate analysis and multivariate analysis were used to identify the risk factors of patients in the development cohort. The nomogram was generated, and internal validation was performed. We used the area under the receiver-operating characteristic curve (AUC-ROC) to assess the discrimination and used the Hosmer–Lemeshow test to calibrate the model. To further verify the predictability and accuracy of the model, we performed an external validation of the patients in the validation cohort. RESULTS: A total of 570 patients were used to generate the nomogram. After univariate analysis and multivariate logistic regression, the remaining 7 variables (diabetes mellitus, atrial fibrillation, total cholesterol, fibrous protein, cerebral infarction area, NIHSS score and onset-to-treatment) were independent predictors of HT and used to compose the nomogram. The area under the receiver-operating characteristic curve of the model was 0.889 (95% CI, 0.841–0.938), and the calibration was good (P = 0.487 for the Hosmer–Lemeshow test). The model was validated externally with an AUC-ROC value of 0.832 (95% CI, 0.727–0.938). CONCLUSIONS: The nomogram prediction model in this study has good predictive ability, accuracy and discrimination, which can improve the diagnostic efficiency of HT in patients with acute ischemic stroke. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12883-022-02678-2.
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spelling pubmed-90403822022-04-27 Nomogram to predict hemorrhagic transformation for acute ischemic stroke in Western China: a retrospective analysis Zhang, Keming Luan, Jianfang Li, Changqing Chen, Mingli BMC Neurol Research BACKGROUND AND PURPOSE: Hemorrhagic transformation (HT) is the most alarming complication of acute ischemic stroke. We aimed to identify risk factors for HT in Chinese patients and attempted to develop a nomogram to predict individual cases. METHODS: A retrospective study was used to collect the demographic and clinical characteristics of ischemic stroke patients at the Second Affiliated Hospital of Chongqing Medical University (development cohort) and Chongqing Sanbo Changan Hospital (validation cohort) from October 2013 to August 2020. Univariate analysis and multivariate analysis were used to identify the risk factors of patients in the development cohort. The nomogram was generated, and internal validation was performed. We used the area under the receiver-operating characteristic curve (AUC-ROC) to assess the discrimination and used the Hosmer–Lemeshow test to calibrate the model. To further verify the predictability and accuracy of the model, we performed an external validation of the patients in the validation cohort. RESULTS: A total of 570 patients were used to generate the nomogram. After univariate analysis and multivariate logistic regression, the remaining 7 variables (diabetes mellitus, atrial fibrillation, total cholesterol, fibrous protein, cerebral infarction area, NIHSS score and onset-to-treatment) were independent predictors of HT and used to compose the nomogram. The area under the receiver-operating characteristic curve of the model was 0.889 (95% CI, 0.841–0.938), and the calibration was good (P = 0.487 for the Hosmer–Lemeshow test). The model was validated externally with an AUC-ROC value of 0.832 (95% CI, 0.727–0.938). CONCLUSIONS: The nomogram prediction model in this study has good predictive ability, accuracy and discrimination, which can improve the diagnostic efficiency of HT in patients with acute ischemic stroke. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12883-022-02678-2. BioMed Central 2022-04-26 /pmc/articles/PMC9040382/ /pubmed/35468774 http://dx.doi.org/10.1186/s12883-022-02678-2 Text en © The Author(s) 2022 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, visithttp://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
Zhang, Keming
Luan, Jianfang
Li, Changqing
Chen, Mingli
Nomogram to predict hemorrhagic transformation for acute ischemic stroke in Western China: a retrospective analysis
title Nomogram to predict hemorrhagic transformation for acute ischemic stroke in Western China: a retrospective analysis
title_full Nomogram to predict hemorrhagic transformation for acute ischemic stroke in Western China: a retrospective analysis
title_fullStr Nomogram to predict hemorrhagic transformation for acute ischemic stroke in Western China: a retrospective analysis
title_full_unstemmed Nomogram to predict hemorrhagic transformation for acute ischemic stroke in Western China: a retrospective analysis
title_short Nomogram to predict hemorrhagic transformation for acute ischemic stroke in Western China: a retrospective analysis
title_sort nomogram to predict hemorrhagic transformation for acute ischemic stroke in western china: a retrospective analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9040382/
https://www.ncbi.nlm.nih.gov/pubmed/35468774
http://dx.doi.org/10.1186/s12883-022-02678-2
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