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A model to predict unstable carotid plaques in population with high risk of stroke
BACKGROUND: Several models have been developed to predict asymptomatic carotid stenosis (ACS), however these models did not pay much attention to people with lower level of stenosis (<50% or carotid plaques, especially instable carotid plaques) who might benefit from early interventions. Here, we...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7137419/ https://www.ncbi.nlm.nih.gov/pubmed/32264828 http://dx.doi.org/10.1186/s12872-020-01450-z |
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author | Yin, Junxiong Yu, Chuanyong Liu, Hongxing Du, Mingyang Sun, Feng Yu, Cheng Wei, Lixia Wang, Chongjun Wang, Xiaoshan |
author_facet | Yin, Junxiong Yu, Chuanyong Liu, Hongxing Du, Mingyang Sun, Feng Yu, Cheng Wei, Lixia Wang, Chongjun Wang, Xiaoshan |
author_sort | Yin, Junxiong |
collection | PubMed |
description | BACKGROUND: Several models have been developed to predict asymptomatic carotid stenosis (ACS), however these models did not pay much attention to people with lower level of stenosis (<50% or carotid plaques, especially instable carotid plaques) who might benefit from early interventions. Here, we developed a new model to predict unstable carotid plaques through systematic screening in population with high risk of stroke. METHODS: Community residents who participated the China National Stroke Screening and Prevention Project (CNSSPP) were screened for their stroke risks. A total of 2841 individuals with high risk of stroke were enrolled in this study, 266 (9.4%) of them were found unstable carotid plaques. A total of 19 risk factors were included in this study. Subjects were randomly distributed into Derivation Set group or Validation Set group. According to their carotid ultrasonography records, subjects in derivation set group were further categorized into unstable plaque group or stable plaque group. RESULTS: 174 cases and 1720 cases from Derivation Set group were categorized into unstable plaque group and stable plaque group respectively. The independent risk factors for carotid unstable plaque were: male (OR 1.966, 95%CI 1.406–2.749), older age (50–59, OR 6.012, 95%CI 1.410–25.629; 60–69, OR 13.915, 95%CI 3.381–57.267;≥70, OR 31.267, 95%CI 7.472–130.83), married(OR 1.780, 95%CI 1.186–2.672), LDL-C(OR 2.015, 95%CI 1.443–2.814), and HDL-C(OR 2.130, 95%CI 1.360–3.338). A predictive scoring system was generated, ranging from 0 to 10. The cut-off value of this predictive scoring system is 6.5. The AUC value for derivation and validation set group were 0.738 and 0.737 respectively. CONCLUSIONS: For those individuals with high risk of stroke, we developed a new model which could identify those who have a higher chance to have unstable carotid plaques. When an individual’s predictive model score exceeds 6.5, the probability of having carotid unstable plaques is high, and carotid ultrasonography should be conducted accordingly. This model could be helpful in the primary prevention of stroke. |
format | Online Article Text |
id | pubmed-7137419 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-71374192020-04-11 A model to predict unstable carotid plaques in population with high risk of stroke Yin, Junxiong Yu, Chuanyong Liu, Hongxing Du, Mingyang Sun, Feng Yu, Cheng Wei, Lixia Wang, Chongjun Wang, Xiaoshan BMC Cardiovasc Disord Research Article BACKGROUND: Several models have been developed to predict asymptomatic carotid stenosis (ACS), however these models did not pay much attention to people with lower level of stenosis (<50% or carotid plaques, especially instable carotid plaques) who might benefit from early interventions. Here, we developed a new model to predict unstable carotid plaques through systematic screening in population with high risk of stroke. METHODS: Community residents who participated the China National Stroke Screening and Prevention Project (CNSSPP) were screened for their stroke risks. A total of 2841 individuals with high risk of stroke were enrolled in this study, 266 (9.4%) of them were found unstable carotid plaques. A total of 19 risk factors were included in this study. Subjects were randomly distributed into Derivation Set group or Validation Set group. According to their carotid ultrasonography records, subjects in derivation set group were further categorized into unstable plaque group or stable plaque group. RESULTS: 174 cases and 1720 cases from Derivation Set group were categorized into unstable plaque group and stable plaque group respectively. The independent risk factors for carotid unstable plaque were: male (OR 1.966, 95%CI 1.406–2.749), older age (50–59, OR 6.012, 95%CI 1.410–25.629; 60–69, OR 13.915, 95%CI 3.381–57.267;≥70, OR 31.267, 95%CI 7.472–130.83), married(OR 1.780, 95%CI 1.186–2.672), LDL-C(OR 2.015, 95%CI 1.443–2.814), and HDL-C(OR 2.130, 95%CI 1.360–3.338). A predictive scoring system was generated, ranging from 0 to 10. The cut-off value of this predictive scoring system is 6.5. The AUC value for derivation and validation set group were 0.738 and 0.737 respectively. CONCLUSIONS: For those individuals with high risk of stroke, we developed a new model which could identify those who have a higher chance to have unstable carotid plaques. When an individual’s predictive model score exceeds 6.5, the probability of having carotid unstable plaques is high, and carotid ultrasonography should be conducted accordingly. This model could be helpful in the primary prevention of stroke. BioMed Central 2020-04-07 /pmc/articles/PMC7137419/ /pubmed/32264828 http://dx.doi.org/10.1186/s12872-020-01450-z Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article Yin, Junxiong Yu, Chuanyong Liu, Hongxing Du, Mingyang Sun, Feng Yu, Cheng Wei, Lixia Wang, Chongjun Wang, Xiaoshan A model to predict unstable carotid plaques in population with high risk of stroke |
title | A model to predict unstable carotid plaques in population with high risk of stroke |
title_full | A model to predict unstable carotid plaques in population with high risk of stroke |
title_fullStr | A model to predict unstable carotid plaques in population with high risk of stroke |
title_full_unstemmed | A model to predict unstable carotid plaques in population with high risk of stroke |
title_short | A model to predict unstable carotid plaques in population with high risk of stroke |
title_sort | model to predict unstable carotid plaques in population with high risk of stroke |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7137419/ https://www.ncbi.nlm.nih.gov/pubmed/32264828 http://dx.doi.org/10.1186/s12872-020-01450-z |
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