Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Room Str 6.131, Universiteitsweg 100, PO Box 85500, 3508 GA Utrecht, the Netherlands.
Radiology. 2010 Nov;257(2):549-59. doi: 10.1148/radiol.10100054. Epub 2010 Sep 28.
To predict cardiovascular disease (CVD) in a clinical care population by using prevalent subclinical ancillary aortic findings detected on chest computed tomographic (CT) images.
The study was approved by the medical ethics committee of the primary participating facility and the institutional review boards of all other participating centers. From a total of 6975 patients who underwent diagnostic contrast material-enhanced chest CT for noncardiovascular indications, a representative sample population of 817 patients plus 347 patients who experienced a cardiovascular event during a mean follow-up period of 17 months were assigned visual scores for ancillary aortic abnormalities--on a scale of 0-8 for calcifications, a scale of 0-4 for plaques, a scale of 0-4 for irregularities, and a scale of 0-1 for elongation. Four Cox proportional hazard models incorporating different sum scores for the aortic abnormalities plus age, sex, and chest CT indication were compared for discrimination and calibration. The prediction model that performed best was chosen and externally validated.
Each aortic abnormality was highly predictive, and all models performed well (c index range, 0.70-0.72; goodness-of-fit P value range, .45-.76). The prediction model incorporating the sum score for aortic calcifications was chosen owing to its good performance (c index, 0.72; goodness-of-fit P = .47) and its applicability to nonenhanced CT scanning. Validation of this model in an external data set also revealed good performance (c index, 0.71; goodness-of-fit P = .25; sensitivity, 46%; specificity, 76%).
A derived prediction model incorporating ancillary aortic findings detected on routine diagnostic CT images complements established risk scores and may help to identify patients at high risk for CVD. Timely application of preventative measures may ultimately reduce the number or severity of CVD events.
利用胸部 CT 图像上检测到的普遍亚临床辅助性主动脉异常来预测临床护理人群中的心血管疾病（CVD）。
该研究得到了主要参与机构的医学伦理委员会以及所有其他参与中心的机构审查委员会的批准。在总共 6975 名因非心血管原因接受诊断性对比增强胸部 CT 检查的患者中，选择了一个代表性的样本人群，其中 817 名患者加上 347 名在平均 17 个月的随访期间发生心血管事件的患者，对辅助性主动脉异常进行了视觉评分——钙化评分为 0-8 分，斑块评分为 0-4 分，不规则评分为 0-4 分，伸长评分为 0-1 分。比较了纳入主动脉异常的不同总分加上年龄、性别和胸部 CT 适应证的四个 Cox 比例风险模型的判别能力和校准能力。选择表现最佳的预测模型并进行外部验证。
每种主动脉异常均具有高度预测性，所有模型的表现均良好（c 指数范围为 0.70-0.72；拟合优度 P 值范围为 0.45-0.76）。选择了纳入主动脉钙化总分的预测模型，因为它具有良好的性能（c 指数为 0.72；拟合优度 P=0.47），并且适用于非增强 CT 扫描。在外部数据集验证该模型时，也发现了良好的性能（c 指数为 0.71；拟合优度 P=0.25；敏感性为 46%；特异性为 76%）。
纳入常规诊断 CT 图像上检测到的辅助性主动脉发现的衍生预测模型补充了现有的风险评分，可能有助于识别 CVD 风险较高的患者。及时采取预防措施最终可能会减少 CVD 事件的数量或严重程度。
