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 imaging-based 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.