New Advances in the Treatment of Severe Coronary Artery Calcifications

Artificial intelligence in coronary artery calcium measurement: Barriers and solutions for implementation into daily practice

Coronary artery calcification (CAC) measurement is a valuable predictor of cardiovascular risk. However, its measurement can be time-consuming and complex, thus driving the desire for artificial intelligence (AI)-based approaches. The aim of this review is to explore the current status of CAC volume measurement using AI-based systems for the automated prediction of cardiovascular events. We also make proposals for the implementation of these systems into clinical practice. Research to date on applying AI to CAC scoring has shown the potential for automation and risk stratification, and, overall, efficacy and a high level of agreement with categorisation by trained clinicians have been demonstrated. However, research in this field has not been uniform or directed. One contributing factor may be a lack of integration and communication between computer scientists and cardiologists. Clinicians, institutions, and organisations should work together towards applying this technology to improve processes, preserve healthcare resources, and improve patient outcomes.

Association between Serum 25-Hydroxyvitamin D and Abdominal Aortic Calcification: A Large Cross-Sectional Study

In the American population, the relationship between the standardized serum 25-hydroxyvitamin D (25(OH)D) concentration and the risk of abdominal aortic calcification (AAC) is unclear. The purpose of our study was to investigate the relationship between serum 25(OH)D concentration and AAC risk. Participants from the National Health and Nutrition Examination Survey (NHANES) between 2013 and 2014 were analyzed cross sectionally. An analysis of the relationship between serum 25(OH)D concentration and incident AAC and severe AAC (SAAC) was based on the restricted cubic spline (RCS) and multivariable logistic regression model. In addition, generalized additive models with smooth functions were used to evaluate the relationship between serum 25(OH)D concentration and the degree of AAC. Finally, a subgroup analysis was conducted. There were a total of 3,040 individuals in our study. The serum 25(OH)D concentration was divided into quartiles (Q1: 9.37-50.5 nmol/L; Q2: 50.6-67.2 nmol/L; Q3: 67.3-85.8 nmol/L; and Q4: 85.9-318.0 nmol/L); the lowest quartile served as the reference group (Q1). After adjusting for known confounding variables, compared with the lowest quartile (Q1) of serum 25(OH)D concentration, the odds ratios with 95% confidence intervals for AAC and SAAC across the quartiles (Q2, Q3, and Q4) were (1.042 (0.812, 1.338), 0.863 (0.668, 1.115), and 1.022 (0.787, 1.327)) and (1.48 (0.87, 2.52), 1.70 (1.01, 2.92), and 2.13 (1.19, 3.86)), respectively. As shown by the RCS plot, the serum 25(OH)D concentration was associated with the risk of AAC/SAAC in a U-shaped pattern (P for nonlinearity <0.05). In addition, the degree of AAC decreased at first and then increased as the serum 25(OH)D concentration increased. In conclusion, a U-shaped relationship existed between serum 25(OH)D concentration and the risk of AAC and SAAC. Consequently, the risk of AAC and SAAC may be mitigated with regular monitoring and vitamin D supplementation.

Atherogenic index of plasma for non-diabetic, coronary artery disease patients after percutaneous coronary intervention: a prospective study of the long-term outcomes in China

Background

Non-diabetic coronary artery disease (CAD) patients are thought to encounter metabolic dysfunction and while these changes may be imperceptible to the patient they probably influence outcomes. At present, there is no system to support patients sensing these subtle changes, nor is there an established model for prognoses. The Atherogenic Index of Plasma (AIP) index has already proven useful for atherosclerosis although further research is needed, especially for those without hyperglycemia.

Methods

This is a prospective study of 5538 non-diabetic CAD patients who had received percutaneous coronary intervention (PCI). Participants were assigned to one of three groups according to their AIP index. High AIP index cases were then compared to low index patients according to major adverse cardiac events (MACE). Restricted cubic spline (RCS) analysis was also conducted to investigate interrelations between AIP index levels and hazard ratios (HR) for MACEs.

Results

Patients with a high AIP index encountered metabolic dysfunction compared to those with a low AIP index i.e., higher Body Mass Index (BMI), Total Cholesterol (TC), Triglycerides (TG), and uric acid as well as lower HDL-C. Each of the aforementioned interrelations were significant with p values of less than 0.001. There was also a significant increase in the number of MACEs in the high AIP index group compared to the low AIP index group (HR: 1.37, 95% CI 1.04–1.81; p = 0.025). A J-shaped RCS curve highlighted a change in the HR after the 0.18 juncture (HR per SD: 1.20, 95% CI 0.96–1.50). Further subgroup analysis supported the main findings, all with HRs greater than one.

Conclusion

The AIP index could be used in prognostics for non-diabetic CAD patients 2 years after PCI. The relationship between hazard ratio and the AIP index appears to be J-shaped. Although, further multi-center studies designed for non-diabetic patients with potential metabolic dysfunction should be conducted to determine the value of the AIP index.