Coronary Artery Calcium Scoring in Asymptomatic Patients

Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics

BACKGROUND

Timely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted increased amounts of attention. Developing a model for predicting the incidence of MACE utilizing machine learning (ML) integrating clinical and PVAT features may facilitate targeted preventive interventions and improve patient outcomes.

METHODS

From January 2017 to December 2019, we analyzed a cohort of 1077 individuals who underwent coronary CT scanning at our facility. Clinical features were collected alongside imaging features, such as coronary artery calcium (CAC) scores and perivascular adipose tissue (PVAT) characteristics. Logistic regression (LR), Framingham Risk Score, and ML algorithms were employed for MACE prediction.

RESULTS

We screened seven critical features to improve the practicability of the model. MACE patients tended to be older, smokers, and hypertensive. Imaging biomarkers such as CAC scores and PVAT characteristics differed significantly between patients with and without a 3-year MACE risk in a population that did not exhibit disparities in laboratory results. The ensemble model, which leverages multiple ML algorithms, demonstrated superior predictive performance compared with the other models. Finally, the ensemble model was used for risk stratification prediction to explore its clinical application value.

CONCLUSIONS

The developed ensemble model effectively predicted MACE incidence based on clinical and imaging features, highlighting the potential of ML algorithms in cardiovascular risk prediction and personalized medicine. Early identification of high-risk patients may facilitate targeted preventive interventions and improve patient outcomes.

Cardiac Troponin Serum Concentration Measurement Is Useful Not Only in the Diagnosis of Acute Cardiovascular Events

Cardiac troponin serum concentration is the primary marker used for the diagnosis of acute coronary syndrome. Moreover, the measurement of cardiac troponin concentration is important for risk stratification in patients with pulmonary embolism. The cardiac troponin level is also a general marker of myocardial damage, regardless of etiology. The purpose of this study is to conduct a literature review and present the most important information regarding the current state of knowledge on the cardiac troponin serum concentration in patients with chronic cardiovascular disease (CVD), as well as on the relationships between cardiac troponin serum concentration and features of subclinical cardiovascular dysfunction. According to research conducted to date, patients with CVDs, such as chronic coronary syndrome, chronic lower extremities' ischemia, and cerebrovascular disease, are characterized by higher cardiac troponin concentrations than people without a CVD. Moreover, the literature data indicate that the concentration of cardiac troponin is correlated with markers of subclinical dysfunction of the cardiovascular system, such as the intima-media thickness, pulse wave velocity, ankle-brachial index, coronary artery calcium index (the Agatston score), and flow-mediated dilation. However, further research is needed in various patient subpopulations and in different clinical contexts.