Coronary Artery Calcium Score Predicts Major Adverse Cardiovascular Events in Stable Chest Pain.

Background Coronary artery calcium (CAC) has prognostic value for major adverse cardiovascular events (MACE) in asymptomatic individuals, whereas its role in symptomatic patients is less clear. Purpose To assess the prognostic value of CAC scoring for MACE in participants with stable chest pain initially referred for invasive coronary angiography (ICA). Materials and Methods This prespecified subgroup analysis from the Diagnostic Imaging Strategies for Patients With Stable Chest Pain and Intermediate Risk of Coronary Artery Disease (DISCHARGE) trial, conducted between October 2015 and April 2019 across 26 centers in 16 countries, focused on adult patients with stable chest pain referred for ICA. Participants were randomly assigned to undergo either ICA or coronary CT. CAC scores from noncontrast CT scans were categorized into low, intermediate, and high groups based on scores of 0, 1-399, and 400 or higher, respectively. The end point of the study was the occurrence of MACE (myocardial infarction, stroke, and cardiovascular death) over a median 3.5-year follow-up, analyzed using Cox proportional hazard regression tests. Results The study involved 1749 participants (mean age, 60 years ± 10 [SD]; 992 female). The prevalence of obstructive coronary artery disease (CAD) at CT angiography rose from 4.1% (95% CI: 2.8, 5.8) in the CAC score 0 group to 76.1% (95% CI: 70.3, 81.2) in the CAC score 400 or higher group. Revascularization rates increased from 1.7% to 46.2% across the same groups (P < .001). The CAC score 0 group had a lower MACE risk (0.5%; HR, 0.08 [95% CI: 0.02, 0.30]; P < .001), as did the 1-399 CAC score group (1.9%; HR, 0.27 [95% CI: 0.13, 0.59]; P = .001), compared with the 400 or higher CAC score group (6.8%). No significant difference in MACE between sexes was observed (P = .68). Conclusion In participants with stable chest pain initially referred for ICA, a CAC score of 0 showed very low risk of MACE, and higher CAC scores showed increasing risk of obstructive CAD, revascularization, and MACE at follow-up. Clinical trial registration no. NCT02400229 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Hanneman and Gulsin in this issue.

Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies.

The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging plays an important role in diagnosis and management of cardiomyopathies and provides useful prognostic information. Most molecular factors exert their functions by interacting with other cellular components, thus many diseases reflect perturbations of intracellular networks. Indeed, complex diseases and traits such as cardiomyopathies are caused by perturbations of biological networks. The network medicine approach, by integrating systems biology, aims to identify pathological interacting genes and proteins, revolutionizing the way to know cardiomyopathies and shifting the understanding of their pathogenic phenomena from a reductionist to a holistic approach. In addition, artificial intelligence tools, applied to morphological and functional imaging, could allow imaging scans to be automatically analyzed to extract new parameters and features for cardiomyopathy evaluation. The aim of this review is to discuss the tools of network medicine in cardiomyopathies that could reveal new candidate genes and artificial intelligence imaging-based features with the aim to translate into clinical practice as diagnostic, prognostic, and predictive biomarkers and shed new light on the clinical setting of cardiomyopathies. The integration and elaboration of clinical habits, molecular big data, and imaging into machine learning models could provide better disease phenotyping, outcome prediction, and novel drug targets, thus opening a new scenario for the implementation of precision medicine for cardiomyopathies.

Myocardial iron overload assessed by magnetic resonance imaging (MRI)T2* in multi-transfused patients with thalassemia and acquired anemias.

BACKGROUND: Cardiac complications secondary to iron overload remain a significant matter in patients with transfusion dependent anemias. PATIENTS AND METHODS: To evaluate cardiac siderosis, Magnetic resonance imaging T2* (MRI T2*) was performed in 3 cohorts of transfusion dependent patients: 99 with thalassemia major (TM), 20 with thalassemia intermedia (TI), and 10 with acquired anemias (AA). Serum ferritin was measured and all patients underwent echocardiographic evaluation. RESULTS: In TM patients cardiac T2* pathologic values (below 20 ms) were found in 37 patients. Serum ferritin was negatively associated with age (r=-0.32, p=0.001) and weakly with T2* values (r=-0.19, p=0.057). A positive correlation was found between T2* and LVEF (r=0.27, p=0.006). Out of 37 patients with T2*<20 ms, 18 (48%) had serum ferritin values<1000 ng/ml. In TI cohort, 3 patients had cardiac T2* pathologic values. In AA cohort, pathologic T2* values were found in 2 patients, who received 234 and 199 PRBC units, respectively, and were both on chelation therapy (in one patient ferritin value was 399 ng/ml). T2* values were negatively associated, but not significantly, with the number of PRBC transfused (r=-0.53, p=0.07). CONCLUSION: In our experience, 37% of TM patients had a myocardial iron overload assessed by MRI T2*; this value is higher than in TI patients. Serum ferritin measurement was a poor predictor of myocardial siderosis. In patients with AA, more than 200 PRBC units transfused were required to induce cardiac hemosiderosis, in spite of chelation therapy and, in one patient, of normal ferritin values.