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Published on: August 28, 2018
Cardiovascular disease: prediction with ancillary aortic findings on chest CT scans in routine practice
Martijn J A Gondrie1, Willem P T M Mali, Peter C Jacobs
1Julius 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. m.gondrie@umcutrecht.nl
Insights
Subclinical aortic findings on routine chest CT scans can predict cardiovascular disease (CVD). Aortic calcification scores from CT images help identify high-risk patients for early intervention.
Area of Science:
- Radiology
- Cardiology
- Preventive Medicine
Background:
- Cardiovascular disease (CVD) remains a leading cause of mortality.
- Early identification of at-risk individuals is crucial for effective prevention.
- Routine chest computed tomographic (CT) scans offer potential for incidental detection of CVD risk factors.
Purpose of the Study:
- To develop and validate a prediction model for cardiovascular disease (CVD).
- To utilize subclinical ancillary aortic findings from non-cardiac chest CT scans for CVD risk prediction.
- To assess the utility of aortic abnormalities detected on CT in clinical care populations.
Main Methods:
- A cohort of 6975 patients undergoing chest CT for non-cardiac reasons was analyzed.
- A subset of 817 patients and 347 with cardiovascular events were scored for aortic calcifications, plaques, irregularities, and elongation.
- Cox proportional hazard models were used to evaluate prediction performance, with the best model externally validated.
Main Results:
- Ancillary aortic abnormalities detected on chest CT were highly predictive of CVD.
- A prediction model based on aortic calcification scores demonstrated good performance (c-index 0.72) and applicability to non-enhanced CT.
- External validation confirmed the model's good performance (c-index 0.71) with 46% sensitivity and 76% specificity.
Conclusions:
- A prediction model using ancillary aortic findings from routine CT scans can supplement existing CVD risk scores.
- This approach aids in identifying patients at high risk for cardiovascular disease.
- Early identification facilitates timely preventive measures, potentially reducing CVD event severity or incidence.
Purpose:
To predict cardiovascular disease (CVD) in a clinical care population by using prevalent subclinical ancillary aortic findings detected on chest computed tomographic (CT) images.
Materials And Methods:
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.
Results:
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%).
Conclusion:
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.
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