某随访人群颈动脉硬化发生影响因素及风险预测能力研究

  目的  探讨不同性别人群颈动脉硬化发生影响因素，为疾病针对性预防提供参考。  方法  对金昌队列人群按年龄分层随机抽样后进行随访，采用巢式病例对照研究，分析不同性别人群颈动脉硬化发生的影响因素，并建立发病风险预测模型，以受试者工作特征曲线(receiver operator characteristic curve，ROC) 下面积评价模型拟合优度。  结果  该随访人群颈动脉硬化标化发病率为12.32%，男性发病率(13.65%)高于女性(11.29%)，差异有统计学意义(χ2=4.267，P < 0.001)。年龄、文化程度、收缩压升高、低密度脂蛋白升高是男性和女性发病的共同危险因素，空腹血糖升高(OR=2.556，95% CI:1.618~4.038)和甘油三酯升高(OR=1.535，95% CI:1.058~2.227)仅与男性发病有关；腹型肥胖(OR=1.414，95% CI:1.013~1.974)仅与女性发病有关。男性和女性预测模型曲线下面积分别为0.835(95% CI:0.815~0.856)、0.809(95% CI:0.788~0.831)，灵敏度分别为78.0%、78.9%，特异度分别为78.8%、73.1%，诊断一致率分别为91.3%、82.4%。  结论  影响男性和女性颈动脉硬化发生的危险因素不同，应分性别采取针对性防控措施，运用Logistic回归建立的发病风险预测模型有一定的指导价值。

Influence factors and predictive ability of a risk prediction model for carotid atherosclerosis in a follow-up population

  Objective  To explore factors influencing the incidence of carotid atherosclerosis in different genders so as to provide reference for the specific prevention of the disease.  Methods  A nested case-control study was conducted to analyze factors influencing the incidence of carotid atherosclerosis in Jinchang cohort population who were randomly selected through stratified sampling by age and followed up. A risk prediction model was established and the goodness of fit was evaluated by the area under the receiver operator characteristic curve (ROC).  Results  The standardized incidence of carotid atherosclerosis in this follow-up population was 12.32%, and the incidence rate of males (13.65%) was greater than that of females (11.29%). The difference was statistically significant (χ2=4.267, P < 0.001). Age, education, elevated systolic blood pressure, and elevated low-density lipoprotein cholesterol were common risk factors for carotid atherosclerosis in both men and women. Elevated fasting plasma glucose (OR=2.556, 95% CI: 1.618-4.038) and elevated triglyceride (OR=1.535, 95% CI: 1.058-2.227) were only associated with men. Abdominal obesity (OR=1.414, 95% CI: 1.013-1.974) was only associated with women. The area under ROC of male and female prediction models was 0.835 (95% CI: 0.815-0.856) and 0.809 (95% CI: 0.788-0.831), respectively. The sensitivity was 78.0% and 78.9%, the specificity was 78.8% and 73.1%, and the diagnostic coincidence rate was 91.3% and 82.4%, respectively.  Conclusions  There are different risk factors for carotid atherosclerosis in males and females, and targeted prevention and control measures should be taken according to gender. The risk prediction model established by Logistic regression had certain guiding value.