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A model to predict unstable carotid plaques in population with high risk of stroke

机译:一种预测人口中不稳定的颈动脉斑块的模型,中风风险高

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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. 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. 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. 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.
机译:已经开发了几种模型来预测无症状的颈动脉狭窄(ACS),但这些模型并没有关注狭窄水平较低的人(<50%或颈动脉斑块,尤其是不稳定的颈动脉斑块),他们可能会受益于早期干预措施。在这里,我们开发了一种新型模型,通过系统筛选具有高风险的人群来预测不稳定的颈动脉斑块。参加了中国国家中风筛查和预防项目(CNSSPP)的社区居民被筛选出卒中风险。在本研究中注册了2841名具有高风险的个体,其中266(9.4%)被发现不稳定的颈动脉斑块。本研究共纳入了19个风险因素。受试者被随机分布到派生集组或验证集组中。根据其颈动脉超声记录,衍生装置组的受试者进一步分为不稳定的斑块组或稳定的斑块组。 174例和1720例来自衍生装置组分别分别分析到不稳定的斑块组和稳定的斑块组。颈动脉不稳定斑块的独立危险因素是:雄性(或1.966,95%CI 1.406-2.749),年龄(50-59或6.012,95%CI 1.410-25.629; 60-69或13.915,95%CI 3.381-57.267;≥70,或31.267,95%CI 7.472-130.83),已婚(或1.780,95%CI 1.186-2.672),LDL-C(或2.015,95%CI 1.443-2.814)和HDL-C (或2.130,95%CI 1.360-3338)。产生预测评分系统,从0到10。这个预测评分系统的截止值为6.5。导流和验证集组的AUC值分别为0.738和0.737。对于那些具有卒中风险很大的人,我们开发了一种新模式,可以识别那些有更高机会的人有不稳定的颈动脉斑块。当个人的预测模型得分超过6.5时,具有颈动脉不稳定斑块的概率很高,并且应相应地进行颈动脉超声。该模型可能有助于初级预防行程。

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