目的通过构建并验证中老年人群颈动脉斑块的预测模型及对应用价值探讨。方法回顾性收集2017—2019年于上海交通大学医学院附属仁济医院内分泌科门诊完成颈动脉超声检查患者的临床资料,性别不限,年龄≥45岁。共纳入1416例样本,训练集993...目的通过构建并验证中老年人群颈动脉斑块的预测模型及对应用价值探讨。方法回顾性收集2017—2019年于上海交通大学医学院附属仁济医院内分泌科门诊完成颈动脉超声检查患者的临床资料,性别不限,年龄≥45岁。共纳入1416例样本,训练集993例,验证集423例。按照7∶3的比例随机分为训练集和验证集,在训练集中比较颈动脉斑块组与非颈动脉斑块组各临床指标差异,并将特征指标变量采用多因素Logistic回归分析确定颈动脉斑块发生的独立危险素,依次构建中老年人群颈动脉斑块发生风险的可视化列线图模型。通过校准曲线和受试者操作特征曲线(receiver operator characteristic curve,ROC曲线)验证模型的区分度、一致性和准确性,最后采用决策曲线分析法确定模型的临床实用性,并通过外部验证进行评估。结果最终本研究纳入1416例患者,有483例(34.11%)有颈动脉斑块。多因素Logistic回归分析结果显示,年龄、收缩压、γ-谷氨酰转肽酶、糖化血红蛋白是颈动脉斑块发生的危险因素,而相较于男性,女性是颈动脉斑块发生的保护因素。依此构建可视化列线图模型,训练集ROC曲线下面积(area under the curve,AUC)为0.75(95%CI:0.72~0.78),验证集ROC曲线的AUC为0.71(95%CI:0.66~0.76)。训练集与验证集校准曲线Hosmer-Lemeshow拟合优度检验显示P值均>0.05(训练集P=0.7501,验证集P=0.9872)。决策曲线结果显示预测模型在训练集和验证集的阈值概率分别为5%~98%和1%~81%。结论基于指标(性别、年龄、收缩压、谷氨酰转肽酶、糖化血红蛋白),成功建立了中老年人群颈动脉斑块发生风险的预测模型,该模型预测效能较好,可用于社区或者农村等偏远地区居民普查,有助于颈动脉斑块的早期识别,进而改善预后。展开更多
目的:应用超声造影技术定量及半定量比较糖尿病患者与非糖尿病患者颈动脉斑块内新生血管。方法:回顾性分析2020年7月~2021年11月淄博市中心医院收治的63例颈动脉斑块患者。根据空腹血糖将其分为糖尿病组和非糖尿病组。所有患者均予以超...目的:应用超声造影技术定量及半定量比较糖尿病患者与非糖尿病患者颈动脉斑块内新生血管。方法:回顾性分析2020年7月~2021年11月淄博市中心医院收治的63例颈动脉斑块患者。根据空腹血糖将其分为糖尿病组和非糖尿病组。所有患者均予以超声检查,采用超声造影技术对颈动脉斑块新生血管进行半定量及定量分析,对比两组之间的差异。结果:糖尿病组与非糖尿病组之间血糖差异有统计学意义(P 0.05)。糖尿病组颈动脉斑块内新生血管增强评分、AUC (P/L)和EI (P/L)高于非糖尿病组(P 0.05)。结论:糖尿病患者的颈动脉斑块含有更多的新生血管,斑块更易损,更易发生心脑血管事件。Objective: To quantitatively and semi-quantitatively compare new vessels within carotid atherosclerotic plaques between diabetic and non-diabetic patients using contrast-enhanced ultrasound (CEUS) technique. Methods: A retrospective analysis was performed on 63 patients with carotid plaques admitted to Zibo Central Hospital from July 2020 to November 2021. The patients were divided into diabetic and non-diabetic groups based on fasting blood glucose levels. All patients underwent ultrasound examination, and CEUS was used for semi-quantitative and quantitative analysis of new vessels within carotid atherosclerotic plaques, and the differences between the two groups were compared. Results: There was a statistically significant difference in fasting blood glucose levels between the diabetic and non-diabetic groups (P 0.05). The diabetic group had higher scores for new vessel enhancement within carotid plaques, AUC (P/L), and EI (P/L) compared to the non-diabetic group (P 0.05). Conclusion: Carotid atherosclerotic plaques in diabetic patients contain more new vessels, are more vulnerable, and are more prone to cardiovascular and cerebrovascular events.展开更多
文摘目的通过构建并验证中老年人群颈动脉斑块的预测模型及对应用价值探讨。方法回顾性收集2017—2019年于上海交通大学医学院附属仁济医院内分泌科门诊完成颈动脉超声检查患者的临床资料,性别不限,年龄≥45岁。共纳入1416例样本,训练集993例,验证集423例。按照7∶3的比例随机分为训练集和验证集,在训练集中比较颈动脉斑块组与非颈动脉斑块组各临床指标差异,并将特征指标变量采用多因素Logistic回归分析确定颈动脉斑块发生的独立危险素,依次构建中老年人群颈动脉斑块发生风险的可视化列线图模型。通过校准曲线和受试者操作特征曲线(receiver operator characteristic curve,ROC曲线)验证模型的区分度、一致性和准确性,最后采用决策曲线分析法确定模型的临床实用性,并通过外部验证进行评估。结果最终本研究纳入1416例患者,有483例(34.11%)有颈动脉斑块。多因素Logistic回归分析结果显示,年龄、收缩压、γ-谷氨酰转肽酶、糖化血红蛋白是颈动脉斑块发生的危险因素,而相较于男性,女性是颈动脉斑块发生的保护因素。依此构建可视化列线图模型,训练集ROC曲线下面积(area under the curve,AUC)为0.75(95%CI:0.72~0.78),验证集ROC曲线的AUC为0.71(95%CI:0.66~0.76)。训练集与验证集校准曲线Hosmer-Lemeshow拟合优度检验显示P值均>0.05(训练集P=0.7501,验证集P=0.9872)。决策曲线结果显示预测模型在训练集和验证集的阈值概率分别为5%~98%和1%~81%。结论基于指标(性别、年龄、收缩压、谷氨酰转肽酶、糖化血红蛋白),成功建立了中老年人群颈动脉斑块发生风险的预测模型,该模型预测效能较好,可用于社区或者农村等偏远地区居民普查,有助于颈动脉斑块的早期识别,进而改善预后。
文摘目的:应用超声造影技术定量及半定量比较糖尿病患者与非糖尿病患者颈动脉斑块内新生血管。方法:回顾性分析2020年7月~2021年11月淄博市中心医院收治的63例颈动脉斑块患者。根据空腹血糖将其分为糖尿病组和非糖尿病组。所有患者均予以超声检查,采用超声造影技术对颈动脉斑块新生血管进行半定量及定量分析,对比两组之间的差异。结果:糖尿病组与非糖尿病组之间血糖差异有统计学意义(P 0.05)。糖尿病组颈动脉斑块内新生血管增强评分、AUC (P/L)和EI (P/L)高于非糖尿病组(P 0.05)。结论:糖尿病患者的颈动脉斑块含有更多的新生血管,斑块更易损,更易发生心脑血管事件。Objective: To quantitatively and semi-quantitatively compare new vessels within carotid atherosclerotic plaques between diabetic and non-diabetic patients using contrast-enhanced ultrasound (CEUS) technique. Methods: A retrospective analysis was performed on 63 patients with carotid plaques admitted to Zibo Central Hospital from July 2020 to November 2021. The patients were divided into diabetic and non-diabetic groups based on fasting blood glucose levels. All patients underwent ultrasound examination, and CEUS was used for semi-quantitative and quantitative analysis of new vessels within carotid atherosclerotic plaques, and the differences between the two groups were compared. Results: There was a statistically significant difference in fasting blood glucose levels between the diabetic and non-diabetic groups (P 0.05). The diabetic group had higher scores for new vessel enhancement within carotid plaques, AUC (P/L), and EI (P/L) compared to the non-diabetic group (P 0.05). Conclusion: Carotid atherosclerotic plaques in diabetic patients contain more new vessels, are more vulnerable, and are more prone to cardiovascular and cerebrovascular events.
