Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Yin, Junxiong, Yu, Chuanyong, Liu, Hongxing, Du, Mingyang, Sun, Feng, Yu, Cheng, Wei, Lixia, Wang, Chongjun, Wang, Xiaoshan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
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
_version_ 1783518423531126784
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
work_keys_str_mv AT yinjunxiong amodeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT yuchuanyong amodeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT liuhongxing amodeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT dumingyang amodeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT sunfeng amodeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT yucheng amodeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT weilixia amodeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT wangchongjun amodeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT wangxiaoshan amodeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT yinjunxiong modeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT yuchuanyong modeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT liuhongxing modeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT dumingyang modeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT sunfeng modeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT yucheng modeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT weilixia modeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT wangchongjun modeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke
AT wangxiaoshan modeltopredictunstablecarotidplaquesinpopulationwithhighriskofstroke