Measurement and Application of Incidentally Detected Coronary Calcium: JACC Review Topic of the Week

Predictive value of tibial and coronary artery calcification scores for cardiac and lower extremity events

OBJECTIVE

The extent of calcification in tibial and coronary arteries predicts major ischemic events. Tibial calcification predicts ischemic events in a manner independent of the degree of associated atherosclerotic occlusive disease while coronary calcification can improve risk stratification in patients at intermediate risk for cardiovascular disease. We thus assessed the relationship between tibial and coronary calcification, the influence of cardiovascular risk factors on their extent, and their individual predictive value on major adverse limb and cardiac events (MALE and MACE).

METHODS

We retrospectively reviewed images and collected demographic and cardiovascular risk factor data of patients who underwent CT scans of the lower extremities and chest. Calcification in the tibial and coronary arteries were identified and scored using a semi-automated method. A spearman correlation coefficient was calculated using tibial and coronary artery calcification scores (TAC and CAC). Ordinal logistic regression and multiple linear regression were performed using cardiovascular and demographic factors as covariates. Log-rank test and Kaplan-Meier estimate were completed to assess MACE and MALE free probability.

RESULTS

A total of 101 patients were identified who met inclusion criteria. There was a strong correlation (r=0.76) between CAC and TAC scores. Severe CAC (defined as > 400) and severe TAC (defined as > 500) scores were both associated with advanced age, male sex, a history of smoking, and diabetes. Renal disease was associated with a high TAC score but not CAC. An increasing TAC score was associated with increased MACE (p<0.0001) and MALE (p=0.039). However, increasing CAC was only associated with increased MACE (p=0.0036).

CONCLUSION

We provide data suggesting that TAC and CAC share similar risk factors and are highly correlated. Interestingly, TAC predicts both MACE and MALE, while CAC is best at predicting coronary, but not lower extremity events. Further studies are needed to understand the contributions of arterial calcification to myocardial and lower extremity ischemia.

Artificial Intelligence in Ischemic Heart Disease Prevention

PURPOSE OF REVIEW

This review discusses the transformative potential of artificial intelligence (AI) in ischemic heart disease (IHD) prevention. It explores advancements of AI in predictive modeling, biomarker discovery, and cardiovascular imaging. Finally, considerations for clinical integration of AI into preventive cardiology workflows are reviewed.

RECENT FINDINGS

AI-driven tools, including machine learning (ML) models, have greatly enhanced IHD risk prediction by integrating multimodal data from clinical sources, patient-generated inputs, biomarkers, and imaging. Applications in these various data sources have demonstrated superior diagnostic accuracy compared to traditional methods. However, ensuring algorithm fairness, mitigating biases, enhancing explainability, and addressing ethical concerns remain critical for successful deployment. Emerging technologies like federated learning and explainable AI are fostering more robust, scalable, and equitable adoption. AI holds promise in reshaping preventive cardiology workflows, offering more precise risk assessment and personalized care. Addressing barriers related to equity, transparency, and stakeholder engagement is key for seamless clinical integration and sustainable, lasting improvements in cardiovascular care.

Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?

PURPOSE OF REVIEW

This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology.

RECENT FINDINGS

AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.