Department of Neurology, Brain Hospital Affiliated to Nanjing Medical University, 264# Guangzhou road, Nanjing, 210012, Jiangsu, China.
Department of Computer Science and Technology, Nanjing University, Nanjing, 210012, Jiangsu, China.
BMC Cardiovasc Disord. 2020 Apr 7;20(1):164. doi: 10.1186/s12872-020-01450-z.
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.
已经有一些模型被开发出来用于预测无症状颈动脉狭窄（ACS），然而这些模型并没有过多关注狭窄程度较低（<50%或颈动脉斑块，尤其是不稳定颈动脉斑块）的人群，这些人群可能从早期干预中获益。在这里，我们通过对高卒中风险人群进行系统筛查，建立了一个新的模型来预测不稳定颈动脉斑块。
参与中国国家卒中筛查与预防项目（CNSSPP）的社区居民被筛查卒中风险。共有 2841 名高卒中风险的个体被纳入本研究，其中 266 名（9.4%）被发现存在不稳定颈动脉斑块。本研究共纳入 19 个危险因素。将受试者随机分配到推导集组或验证集组。根据颈动脉超声检查记录，推导集组中的受试者进一步分为不稳定斑块组或稳定斑块组。
推导集组中 174 例和 1720 例受试者分别被归入不稳定斑块组和稳定斑块组。颈动脉不稳定斑块的独立危险因素为：男性（OR 1.966，95%CI 1.406-2.749），年龄较大（50-59 岁，OR 6.012，95%CI 1.410-25.629；60-69 岁，OR 13.915，95%CI 3.381-57.267；≥70 岁，OR 31.267，95%CI 7.472-130.83），已婚（OR 1.780，95%CI 1.186-2.672），LDL-C（OR 2.015，95%CI 1.443-2.814），HDL-C（OR 2.130，95%CI 1.360-3.338）。建立了一个预测评分系统，范围为 0 至 10。该预测评分系统的截断值为 6.5。推导集和验证集组的 AUC 值分别为 0.738 和 0.737。
对于高卒中风险的个体，我们建立了一个新的模型，可以识别出那些发生不稳定颈动脉斑块可能性较高的患者。当个体的预测模型评分超过 6.5 时，发生颈动脉不稳定斑块的概率较高，应进行相应的颈动脉超声检查。该模型有助于卒中的一级预防。
