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Nomogram to Predict the Number of Thrombectomy Device Passes for Acute Ischemic Stroke with Endovascular Thrombectomy
PURPOSE: This study aimed to determine the risk factors associated with the number of thrombectomy device passes and establish a nomogram for predicting the number of device pass attempts in patients with successful endovascular thrombectomy (EVT). METHODS: We enrolled patients from a signal compreh...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8565981/ https://www.ncbi.nlm.nih.gov/pubmed/34744465 http://dx.doi.org/10.2147/RMHP.S317834 |
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author | Yang, Shijie Zhao, Kaixuan Xi, Huan Xiao, Zaixing Li, Wei Zhang, Yichuan Fan, Zhiqiang Li, Changqing Chai, Erqing |
author_facet | Yang, Shijie Zhao, Kaixuan Xi, Huan Xiao, Zaixing Li, Wei Zhang, Yichuan Fan, Zhiqiang Li, Changqing Chai, Erqing |
author_sort | Yang, Shijie |
collection | PubMed |
description | PURPOSE: This study aimed to determine the risk factors associated with the number of thrombectomy device passes and establish a nomogram for predicting the number of device pass attempts in patients with successful endovascular thrombectomy (EVT). METHODS: We enrolled patients from a signal comprehensive stroke center (CSC) who underwent EVT because of large vessel occlusion stroke. Multivariate logistic regression analysis was used to develop the best-fit nomogram for predicting the number of thrombectomy device passes. The discrimination and calibration of the nomogram were estimated using the area under the receiver operating characteristic curve (AUC-ROC) and a calibration plot with a bootstrap of 1000 resamples. A decision curve analysis (DCA) was used to measure the availability and effect of this predictive tool. RESULTS: In total, 130 patients (mean age 64.9 ± 11.1 years; 83 males) were included in the final analysis. Age (odds ratio [OR], 1.085; 95% confidence interval [CI], 1.005–1.172; p = 0.036), baseline Alberta Stroke Program Early computed tomography (ASPECTS) score (OR, 0.237; 95% CI, 0.115–0.486; p < 0.001), and homocysteine level (OR, 1.090; 95% CI, 1.028–1.155; p = 0.004) were independent predictors of device pass number and were thus incorporated into the nomogram. The AUC-ROC determined the discrimination ability of the nomogram, which was 0.921 (95% CI, 0.860–0.980), which indicated good predictive power. Moreover, the calibration plot revealed good predictive accuracy of the nomogram. The DCA demonstrated that when the threshold probabilities of the cohort ranged between 5.0% and 98.0%, the use of the nomogram to predict a device pass number > 3 provided greater net benefit than did “treat all” or “treat none” strategies. CONCLUSION: The nomogram comprised age, baseline ASPECTS score, and homocysteine level, can predict a device pass number >3 in acute ischemic stroke (AIS) patients who are undergoing EVT. |
format | Online Article Text |
id | pubmed-8565981 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-85659812021-11-05 Nomogram to Predict the Number of Thrombectomy Device Passes for Acute Ischemic Stroke with Endovascular Thrombectomy Yang, Shijie Zhao, Kaixuan Xi, Huan Xiao, Zaixing Li, Wei Zhang, Yichuan Fan, Zhiqiang Li, Changqing Chai, Erqing Risk Manag Healthc Policy Original Research PURPOSE: This study aimed to determine the risk factors associated with the number of thrombectomy device passes and establish a nomogram for predicting the number of device pass attempts in patients with successful endovascular thrombectomy (EVT). METHODS: We enrolled patients from a signal comprehensive stroke center (CSC) who underwent EVT because of large vessel occlusion stroke. Multivariate logistic regression analysis was used to develop the best-fit nomogram for predicting the number of thrombectomy device passes. The discrimination and calibration of the nomogram were estimated using the area under the receiver operating characteristic curve (AUC-ROC) and a calibration plot with a bootstrap of 1000 resamples. A decision curve analysis (DCA) was used to measure the availability and effect of this predictive tool. RESULTS: In total, 130 patients (mean age 64.9 ± 11.1 years; 83 males) were included in the final analysis. Age (odds ratio [OR], 1.085; 95% confidence interval [CI], 1.005–1.172; p = 0.036), baseline Alberta Stroke Program Early computed tomography (ASPECTS) score (OR, 0.237; 95% CI, 0.115–0.486; p < 0.001), and homocysteine level (OR, 1.090; 95% CI, 1.028–1.155; p = 0.004) were independent predictors of device pass number and were thus incorporated into the nomogram. The AUC-ROC determined the discrimination ability of the nomogram, which was 0.921 (95% CI, 0.860–0.980), which indicated good predictive power. Moreover, the calibration plot revealed good predictive accuracy of the nomogram. The DCA demonstrated that when the threshold probabilities of the cohort ranged between 5.0% and 98.0%, the use of the nomogram to predict a device pass number > 3 provided greater net benefit than did “treat all” or “treat none” strategies. CONCLUSION: The nomogram comprised age, baseline ASPECTS score, and homocysteine level, can predict a device pass number >3 in acute ischemic stroke (AIS) patients who are undergoing EVT. Dove 2021-10-29 /pmc/articles/PMC8565981/ /pubmed/34744465 http://dx.doi.org/10.2147/RMHP.S317834 Text en © 2021 Yang et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Yang, Shijie Zhao, Kaixuan Xi, Huan Xiao, Zaixing Li, Wei Zhang, Yichuan Fan, Zhiqiang Li, Changqing Chai, Erqing Nomogram to Predict the Number of Thrombectomy Device Passes for Acute Ischemic Stroke with Endovascular Thrombectomy |
title | Nomogram to Predict the Number of Thrombectomy Device Passes for Acute Ischemic Stroke with Endovascular Thrombectomy |
title_full | Nomogram to Predict the Number of Thrombectomy Device Passes for Acute Ischemic Stroke with Endovascular Thrombectomy |
title_fullStr | Nomogram to Predict the Number of Thrombectomy Device Passes for Acute Ischemic Stroke with Endovascular Thrombectomy |
title_full_unstemmed | Nomogram to Predict the Number of Thrombectomy Device Passes for Acute Ischemic Stroke with Endovascular Thrombectomy |
title_short | Nomogram to Predict the Number of Thrombectomy Device Passes for Acute Ischemic Stroke with Endovascular Thrombectomy |
title_sort | nomogram to predict the number of thrombectomy device passes for acute ischemic stroke with endovascular thrombectomy |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8565981/ https://www.ncbi.nlm.nih.gov/pubmed/34744465 http://dx.doi.org/10.2147/RMHP.S317834 |
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