Related Experiment Video
Updated: Jun 26, 2025
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Enhancing cardiovascular risk prediction through AI-enabled calcium-omics
Ammar Hoori1, Sadeer Al-Kindi2,3, Tao Hu1
1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
Insights
Novel calcium-omics features improve prediction of major adverse cardiovascular events (MACE) beyond the standard Agatston score. This advanced analysis identifies more high-risk patients for targeted therapies.
Area of Science:
- Cardiovascular Imaging
- Radiomics
- Predictive Analytics
Background:
- The Agatston score is a standard but limited predictor of major adverse cardiovascular events (MACE).
- It does not capture detailed calcification features crucial for understanding coronary artery disease pathophysiology.
- Individualized risk assessment requires analysis of calcification characteristics like number, distribution, and density.
Purpose of the Study:
- To develop and validate a novel risk prediction model using detailed coronary artery calcification features (calcium-omics).
- To compare the performance of the calcium-omics model against the traditional Agatston score for MACE prediction.
- To identify specific calcium-omics features that are significant determinants of MACE risk.
Main Methods:
- Utilized hand-crafted calcification features (calcium-omics) from 2457 CT calcium score (CTCS) images.
- Employed Cox time-to-event modeling, elastic net, and synthetic sampling for imbalanced data.
- Developed and validated a calcium-omics predictive model using an 80/20 training/testing split.
Main Results:
- Calcium-omics features, including number of calcifications, LAD mass, and diffusivity, were key risk determinants.
- Dense calcifications (>1000 HU) were associated with reduced MACE risk.
- The calcium-omics model achieved superior performance (C-index 80.5%/71.6%, 2-year AUC 82.4%/74.8%) compared to Agatston, identifying 13.2% more MACE cases.
Conclusions:
- Calcium-omics offers improved risk prediction for MACE compared to the conventional Agatston score.
- This novel approach enhances identification of patients requiring intensive cardiovascular follow-up and treatment.
- Further multi-institutional studies are warranted to validate and implement calcium-omics in clinical practice.
Abstract:
Whole-heart coronary calcium Agatston score is a well-established predictor of major adverse cardiovascular events (MACE), but it does not account for individual calcification features related to the pathophysiology of the disease (e.g., multiple-vessel disease, spread of the disease along the vessel, stable calcifications, numbers of lesions, and density). We used novel, hand-crafted calcification features (calcium-omics); Cox time-to-event modeling; elastic net; and up and down synthetic sampling methods for imbalanced data, to assess MACE risk. We used 2457 CT calcium score (CTCS) images enriched for MACE events from our large no-cost CLARIFY program (ClinicalTrials.gov Identifier: NCT04075162). Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were especially important determinants of increased risk, with dense calcification (> 1000HU, stable calcifications) associated with reduced risk Our calcium-omics model with (training/testing, 80/20) gave C-index (80.5%/71.6%) and 2-year AUC (82.4%/74.8%). Although the C-index is notoriously impervious to model improvements, calcium-omics compared favorably to Agatston and gave a significant difference (P < 0.001). The calcium-omics model identified 73.5% of MACE cases in the high-risk group, a 13.2% improvement as compared to Agatston, suggesting that calcium-omics could be used to better identity candidates for intensive follow-up and therapies. The categorical net-reclassification index was NRI = 0.153. Our findings from this exploratory study suggest the utility of calcium-omics in improved risk prediction. These promising results will pave the way for more extensive, multi-institutional studies of calcium-omics.
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