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Incidental imaging findings from routine chest CT used to identify subjects at high risk of future cardiovascular events

机译:常规胸部CT附带的影像学发现可用于识别将来发生心血管事件的高风险受试者

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摘要

Purpose: To investigate the contribution of incidental findings at chest computed tomography (CT) in the detection of subjects at high risk for cardiovascular disease (CVD) by deriving and validating a CT-based prediction rule. Materials and Methods: This retrospective study was approved by the ethical review board of the primary participating facility, and informed consent was waived. The derivation cohort comprised 10 410 patients who underwent diagnostic chest CT for noncardiovascular indications. During a mean follow-up of 3.7 years (maximum, 7.0 years), 1148 CVD events (cases) were identified. By using a case-cohort approach, CT scans from the cases and from an approximately 10% random sample of the baseline cohort (n = 1366) were graded visually for several cardiovascular findings. Multivariable Cox proportional hazards analysis with backward elimination technique was used to derive the best-fitting parsimonious prediction model. External validation (discrimination, calibration, and risk stratification) was performed in a separate validation cohort (n = 1653). Results: The final model included patient age and sex, CT indication, left anterior descending coronary artery calcifications, mitral valve calcifications, descending aorta calcifications, and cardiac diameter. The model demonstrated good discriminative value, with a C statistic of 0.71 (95% confidence interval: 0.68, 0.74) and a good overall calibration, as assessed in the validation cohort. This imagingbased model allows accurate stratification of individuals into clinically relevant risk categories. Conclusion: Structured reporting of incidental CT findings can mediate accurate stratification of individuals into clinically relevant risk categories and subsequently allow those at higher risk of future CVD events to be distinguished.
机译:目的:通过推导并验证基于CT的预测规则,研究胸部计算机断层扫描(CT)的偶然发现对检测高心血管疾病(CVD)受试者的贡献。资料和方法:这项回顾性研究获得了主要参与机构的伦理审查委员会的批准,并放弃了知情同意。该派生队列包括10410名接受了非心血管适应症诊断性胸部CT检查的患者。在平均3.7年(最长7.0年)的平均随访期间,确定了1148个CVD事件(病例)。通过使用病例队列方法,对病例和大约10%的基线队列随机样本(n = 1366)的CT扫描进行了视觉分级,以获取多个心血管检查结果。采用后向消除技术进行多变量Cox比例风险分析,得出最适合的简约预测模型。外部验证(区分,校准和风险分层)在单独的验证队列中进行(n = 1653)。结果:最终模型包括患者的年龄和性别,CT适应症,左前降支冠状动脉钙化,二尖瓣钙化,主动脉降钙化和心脏直径。该模型表现出良好的判别力,其C统计量为0.71(95%置信区间:0.68,0.74),并且在验证队列中进行了很好的总体校准。这种基于影像的模型可以将个体准确地分为临床相关风险类别。结论:CT偶然发现的结构化报告可以将个体的准确分层介导为临床上相关的风险类别,并随后区分那些将来发生CVD事件的较高风险的人。

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