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A Clinical Prediction Model for Patients with Acute Large Vessel Occlusion Due to Underlying Intracranial Atherosclerotic Stenosis

BACKGROUND: Acute large vessel occlusion due to underlying intracranial atherosclerotic stenosis (ICAS-LVO) increases the difficulty of revascularization, resulting in frequent re-occlusion. The establishment of its pathogenesis before endovascular treatment (EVT) is beneficial for patients. We aime...

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Autores principales: Cai, Yusen, Gu, Yuting, Wang, Yanhong, Wang, Peng, Zhang, Lei, Liu, Chaolai, Chu, Jianfeng, Li, Hui, Lu, Zhe, Zhou, Yafei, Liu, Huakun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220124/
https://www.ncbi.nlm.nih.gov/pubmed/36520189
http://dx.doi.org/10.1007/s00062-022-01241-3
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author Cai, Yusen
Gu, Yuting
Wang, Yanhong
Wang, Peng
Zhang, Lei
Liu, Chaolai
Chu, Jianfeng
Li, Hui
Lu, Zhe
Zhou, Yafei
Liu, Huakun
author_facet Cai, Yusen
Gu, Yuting
Wang, Yanhong
Wang, Peng
Zhang, Lei
Liu, Chaolai
Chu, Jianfeng
Li, Hui
Lu, Zhe
Zhou, Yafei
Liu, Huakun
author_sort Cai, Yusen
collection PubMed
description BACKGROUND: Acute large vessel occlusion due to underlying intracranial atherosclerotic stenosis (ICAS-LVO) increases the difficulty of revascularization, resulting in frequent re-occlusion. The establishment of its pathogenesis before endovascular treatment (EVT) is beneficial for patients. We aimed at developing and validating a clinical prediction model for ICAS-LVO patients before EVT. METHODS: Patients with acute large vessel occlusion at Jining No. 1 People’s Hospital from January 2019 to September 2021 were retrospectively included as the training cohort. The 70 patients who met the inclusion and exclusion criteria were included in the validation cohort (October 2021 to May 2022). Demographics, onset form, medical history, digital subtraction angiography (DSA) imaging data, and laboratory test data were collected. Preprocedural parameters for the ICAS-LVO risk prediction model were established by stepwise logistic regression controlling for the confounding effects. Then, we constructed a nomogram model and evaluated its performance via the Hosmer-Lemeshow goodness-of-fit test, area under the ROC curve (AUC) analysis. RESULTS: The 231 acute LVO patients were included in the final analysis, 74 (32.3%) patients were ICAS-LVO. A preoperative diagnosis prediction model consisting of five predictors for ICAS-LVO, including fluctuating symptoms, NIHSS < 16, atrial fibrillation, tapered sign, and ASITN/SIR score ≥ 2. The model depicted an acceptable calibration (Hosmer-Lemeshow test, p = 0.451) and good discrimination (AUC, 0.941; 95% confidence interval, 0.910–0.971). The optimal cut-off value for the ICAS-LVO scale was 2 points, with 86.5% sensitivity, 91.1% specificity, and 90.5% accuracy. In the validation cohort, the discriminative ability was promising with an AUC value of 0.897, implying a good predictive performance. CONCLUSION: The established ICAS-LVO scale, which is composed of five predictors: fluctuating symptoms, NIHSS < 16, atrial fibrillation, tapered sign, and ASITN/SIR score ≥ 2, has a good predictive value for ICAS-LVO in Chinese populations.
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spelling pubmed-102201242023-05-28 A Clinical Prediction Model for Patients with Acute Large Vessel Occlusion Due to Underlying Intracranial Atherosclerotic Stenosis Cai, Yusen Gu, Yuting Wang, Yanhong Wang, Peng Zhang, Lei Liu, Chaolai Chu, Jianfeng Li, Hui Lu, Zhe Zhou, Yafei Liu, Huakun Clin Neuroradiol Original Article BACKGROUND: Acute large vessel occlusion due to underlying intracranial atherosclerotic stenosis (ICAS-LVO) increases the difficulty of revascularization, resulting in frequent re-occlusion. The establishment of its pathogenesis before endovascular treatment (EVT) is beneficial for patients. We aimed at developing and validating a clinical prediction model for ICAS-LVO patients before EVT. METHODS: Patients with acute large vessel occlusion at Jining No. 1 People’s Hospital from January 2019 to September 2021 were retrospectively included as the training cohort. The 70 patients who met the inclusion and exclusion criteria were included in the validation cohort (October 2021 to May 2022). Demographics, onset form, medical history, digital subtraction angiography (DSA) imaging data, and laboratory test data were collected. Preprocedural parameters for the ICAS-LVO risk prediction model were established by stepwise logistic regression controlling for the confounding effects. Then, we constructed a nomogram model and evaluated its performance via the Hosmer-Lemeshow goodness-of-fit test, area under the ROC curve (AUC) analysis. RESULTS: The 231 acute LVO patients were included in the final analysis, 74 (32.3%) patients were ICAS-LVO. A preoperative diagnosis prediction model consisting of five predictors for ICAS-LVO, including fluctuating symptoms, NIHSS < 16, atrial fibrillation, tapered sign, and ASITN/SIR score ≥ 2. The model depicted an acceptable calibration (Hosmer-Lemeshow test, p = 0.451) and good discrimination (AUC, 0.941; 95% confidence interval, 0.910–0.971). The optimal cut-off value for the ICAS-LVO scale was 2 points, with 86.5% sensitivity, 91.1% specificity, and 90.5% accuracy. In the validation cohort, the discriminative ability was promising with an AUC value of 0.897, implying a good predictive performance. CONCLUSION: The established ICAS-LVO scale, which is composed of five predictors: fluctuating symptoms, NIHSS < 16, atrial fibrillation, tapered sign, and ASITN/SIR score ≥ 2, has a good predictive value for ICAS-LVO in Chinese populations. Springer Berlin Heidelberg 2022-12-15 2023 /pmc/articles/PMC10220124/ /pubmed/36520189 http://dx.doi.org/10.1007/s00062-022-01241-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) .
spellingShingle Original Article
Cai, Yusen
Gu, Yuting
Wang, Yanhong
Wang, Peng
Zhang, Lei
Liu, Chaolai
Chu, Jianfeng
Li, Hui
Lu, Zhe
Zhou, Yafei
Liu, Huakun
A Clinical Prediction Model for Patients with Acute Large Vessel Occlusion Due to Underlying Intracranial Atherosclerotic Stenosis
title A Clinical Prediction Model for Patients with Acute Large Vessel Occlusion Due to Underlying Intracranial Atherosclerotic Stenosis
title_full A Clinical Prediction Model for Patients with Acute Large Vessel Occlusion Due to Underlying Intracranial Atherosclerotic Stenosis
title_fullStr A Clinical Prediction Model for Patients with Acute Large Vessel Occlusion Due to Underlying Intracranial Atherosclerotic Stenosis
title_full_unstemmed A Clinical Prediction Model for Patients with Acute Large Vessel Occlusion Due to Underlying Intracranial Atherosclerotic Stenosis
title_short A Clinical Prediction Model for Patients with Acute Large Vessel Occlusion Due to Underlying Intracranial Atherosclerotic Stenosis
title_sort a clinical prediction model for patients with acute large vessel occlusion due to underlying intracranial atherosclerotic stenosis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220124/
https://www.ncbi.nlm.nih.gov/pubmed/36520189
http://dx.doi.org/10.1007/s00062-022-01241-3
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