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The difference in red blood cell distribution width from before to after thrombolysis as a prognostic factor in acute ischemic stroke patients: A 2-year follow-up

PURPOSE: The aim of our study was to determine whether delta red blood cell distribution (ΔRDW) improves neurological outcomes in acute ischemic stroke (AIS) patients 2 years after intravenous thrombolysis (IVT) therapy. METHODS: AIS patients who received IVT between January 2013 and December 2019 w...

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Autores principales: Jiang, Yanyan, Ren, Chuancheng, Alimujiang, Aydos, Wu, Yuncheng, Huang, Dongya, Yang, Weiting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9606336/
https://www.ncbi.nlm.nih.gov/pubmed/36313517
http://dx.doi.org/10.3389/fneur.2022.1011946
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author Jiang, Yanyan
Ren, Chuancheng
Alimujiang, Aydos
Wu, Yuncheng
Huang, Dongya
Yang, Weiting
author_facet Jiang, Yanyan
Ren, Chuancheng
Alimujiang, Aydos
Wu, Yuncheng
Huang, Dongya
Yang, Weiting
author_sort Jiang, Yanyan
collection PubMed
description PURPOSE: The aim of our study was to determine whether delta red blood cell distribution (ΔRDW) improves neurological outcomes in acute ischemic stroke (AIS) patients 2 years after intravenous thrombolysis (IVT) therapy. METHODS: AIS patients who received IVT between January 2013 and December 2019 were retrospectively analyzed. In accordance with their mRS scores, the patients were divided into two groups. A binary logistic regression analysis was conducted to determine the influencing factors of adverse functional outcomes. It was decided to evaluate the variables' the predictive ability by using the area under the receiver operating characteristic. For the poor neurological recovery risk model, features were selected using the LASSO regression model. We also developed a predictive model based on logistic regression analysis, which combined the features selected in the minimum absolute contraction and selection operator regression models. An evaluation of the discrimination, calibration, and clinical applicability of the predictive model was conducted using the C index, calibration chart, and decision curve analysis. Internal validation was evaluated via bootstrapping. RESULTS: Binary logistic regression analysis showed that ΔRDW was an independent influencing factor for poor neurofunctional outcomes. The most appropriate ΔRDW cut-off value for predicting the recovery of poor neurological outcomes was 18.9% (sensitivity: 89.9%, specificity: 78.6%, p < 0.001). The predictive factors included in the nomogram were age, the occurrence of CHD, stroke, AF, ΔRDW, NIHSS score at onset, interval time from onset to IVT, and whether there were indwelling urine catheters and gastric tubes. The model has not only a good discrimination ability, which was indicated by an overall C index of 0.891 (95% confidence interval: 0.829–0.953), but also a considerable calibration ability. Decision curve analysis showed that the nomogram of adverse neurological outcomes recovery was useful in the clinical practice when intervention was implemented above the threshold of 1% possibility of adverse neurological outcomes recovery. CONCLUSION: In patients with AIS after thrombolysis, the ΔRDW is a potential influencing factor that can be readily used to predict the likelihood of poor neurological function recovery.
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spelling pubmed-96063362022-10-28 The difference in red blood cell distribution width from before to after thrombolysis as a prognostic factor in acute ischemic stroke patients: A 2-year follow-up Jiang, Yanyan Ren, Chuancheng Alimujiang, Aydos Wu, Yuncheng Huang, Dongya Yang, Weiting Front Neurol Neurology PURPOSE: The aim of our study was to determine whether delta red blood cell distribution (ΔRDW) improves neurological outcomes in acute ischemic stroke (AIS) patients 2 years after intravenous thrombolysis (IVT) therapy. METHODS: AIS patients who received IVT between January 2013 and December 2019 were retrospectively analyzed. In accordance with their mRS scores, the patients were divided into two groups. A binary logistic regression analysis was conducted to determine the influencing factors of adverse functional outcomes. It was decided to evaluate the variables' the predictive ability by using the area under the receiver operating characteristic. For the poor neurological recovery risk model, features were selected using the LASSO regression model. We also developed a predictive model based on logistic regression analysis, which combined the features selected in the minimum absolute contraction and selection operator regression models. An evaluation of the discrimination, calibration, and clinical applicability of the predictive model was conducted using the C index, calibration chart, and decision curve analysis. Internal validation was evaluated via bootstrapping. RESULTS: Binary logistic regression analysis showed that ΔRDW was an independent influencing factor for poor neurofunctional outcomes. The most appropriate ΔRDW cut-off value for predicting the recovery of poor neurological outcomes was 18.9% (sensitivity: 89.9%, specificity: 78.6%, p < 0.001). The predictive factors included in the nomogram were age, the occurrence of CHD, stroke, AF, ΔRDW, NIHSS score at onset, interval time from onset to IVT, and whether there were indwelling urine catheters and gastric tubes. The model has not only a good discrimination ability, which was indicated by an overall C index of 0.891 (95% confidence interval: 0.829–0.953), but also a considerable calibration ability. Decision curve analysis showed that the nomogram of adverse neurological outcomes recovery was useful in the clinical practice when intervention was implemented above the threshold of 1% possibility of adverse neurological outcomes recovery. CONCLUSION: In patients with AIS after thrombolysis, the ΔRDW is a potential influencing factor that can be readily used to predict the likelihood of poor neurological function recovery. Frontiers Media S.A. 2022-10-13 /pmc/articles/PMC9606336/ /pubmed/36313517 http://dx.doi.org/10.3389/fneur.2022.1011946 Text en Copyright © 2022 Jiang, Ren, Alimujiang, Wu, Huang and Yang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Jiang, Yanyan
Ren, Chuancheng
Alimujiang, Aydos
Wu, Yuncheng
Huang, Dongya
Yang, Weiting
The difference in red blood cell distribution width from before to after thrombolysis as a prognostic factor in acute ischemic stroke patients: A 2-year follow-up
title The difference in red blood cell distribution width from before to after thrombolysis as a prognostic factor in acute ischemic stroke patients: A 2-year follow-up
title_full The difference in red blood cell distribution width from before to after thrombolysis as a prognostic factor in acute ischemic stroke patients: A 2-year follow-up
title_fullStr The difference in red blood cell distribution width from before to after thrombolysis as a prognostic factor in acute ischemic stroke patients: A 2-year follow-up
title_full_unstemmed The difference in red blood cell distribution width from before to after thrombolysis as a prognostic factor in acute ischemic stroke patients: A 2-year follow-up
title_short The difference in red blood cell distribution width from before to after thrombolysis as a prognostic factor in acute ischemic stroke patients: A 2-year follow-up
title_sort difference in red blood cell distribution width from before to after thrombolysis as a prognostic factor in acute ischemic stroke patients: a 2-year follow-up
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9606336/
https://www.ncbi.nlm.nih.gov/pubmed/36313517
http://dx.doi.org/10.3389/fneur.2022.1011946
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