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Aortic and Cardiac Structure From Routine CT Predict Cardiovascular Risk Beyond PREVENT and Coronary Calcium
Daniel W Oo1, Matthias Jung1, Leonard Nürnberg2
1Cardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Insights
Computed tomography (CT)-derived cardiac and aortic radiomics accurately predict major adverse cardiac events (MACE) beyond traditional risk scores. This imaging analysis identifies high-risk individuals who may benefit from enhanced cardiovascular disease prevention strategies.
Area of Science:
- Cardiology
- Radiology
- Medical Imaging Analysis
Background:
- Accurate cardiovascular disease (CVD) risk assessment is crucial for prevention, but current risk scores lack precision.
- Routine imaging, such as computed tomography (CT), offers potential for opportunistic CVD risk prediction.
Purpose of the Study:
- To evaluate if CT-derived cardiac and aortic structural features can predict major adverse cardiac events (MACE) beyond established risk assessment tools.
- To assess the added value of radiomics in CVD risk stratification.
Main Methods:
- A least absolute shrinkage and selection operator (LASSO) model was developed using radiomics features from 13,437 lung cancer screening CT scans to predict cardiovascular mortality.
- The radiomics score was compared against the PREVENT tool and coronary artery calcium (CAC) score in an independent cohort.
- Discrimination was assessed using Harrel's C-index, and MACE rates were analyzed in high-risk groups defined by the PREVENT or radiomics scores.
Main Results:
- The radiomics score demonstrated superior discrimination for MACE compared to the PREVENT score (C-index 0.66 vs 0.61).
- The radiomics score was complementary to CAC, improving overall MACE prediction (combined C-index 0.69 vs 0.66 for CAC alone).
- Individuals identified as high-risk by the radiomics score (but not PREVENT) exhibited a 3.6-fold higher MACE incidence; key predictive features included aortic surface-to-volume ratio and cardiac chamber dimensions.
Conclusions:
- CT-derived cardiac and aortic radiomics effectively identify high-risk individuals missed by conventional clinical scores.
- This imaging-based approach enhances risk stratification, particularly when combined with CAC scoring.
- Intensified primary prevention strategies may be beneficial for high-risk patients identified through radiomics analysis.
Background:
Cardiovascular disease prevention relies on accurate risk assessment; however, existing scores are imprecise. Routine imaging may be opportunistically used to predict risk.
Objectives:
The authors tested whether computed tomography (CT)-derived cardiac and aortic structure predicts major adverse cardiac events (MACE) beyond standard-of-care scores.
Methods:
The authors developed a least absolute shrinkage and selection operator model to predict cardiovascular mortality using "radiomics" features describing cardiac and aortic structure from 13,437 lung cancer screening CTs from the NLST (National Lung Screening Trial). They compared this score to the PREVENT (Predicting Risk of Cardiovascular Disease Events) tool and the coronary artery calcium (CAC) score in patients with routine chest CT and no prior MACE from Mass General Brigham. They calculated discrimination using Harrel's C-index and MACE rates in high-risk groups by the PREVENT score (≥7.5% risk) or the radiomics score (≥3.0% in men, ≥1.5% in women).
Results:
In external testing (n = 14,577, mean age 61.1 ± 8.6 years, 47.5% male), 6.2% had incident MACE over a median of 5.7 years of follow-up. The radiomics score had higher discrimination for MACE than PREVENT (C-index 0.66 [95% CI: 0.64-0.68] vs 0.61 [95% CI: 0.59-0.63]) and was complementary to CAC (combined C-index 0.69 [95% CI: 0.67-0.71] vs CAC alone 0.66 [95% CI: 0.65-0.68]). High-risk patients by the radiomics score but not PREVENT had 3.6-fold higher MACE incidence than low-risk patients by both scores (23.1 [95% CI: 16.7-30.2] vs 6.5 [95% CI: 5.5-7.5] MACE per 1,000 person-years). Aortic surface-to-volume ratio, left ventricular volume, and left atrial short-axis length were among the most predictive features of MACE.
Conclusions:
CT-derived structural cardiac and aortic radiomics identified high-risk patients missed by clinical scores and further stratified risk among CAC risk groups. High-risk patients may benefit from intensified primary prevention.
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