Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has several limitations, such as visual assessment of stenosis severity, which has significant interobserver variability. This motivates to development of a lesion classification system that can support specialists in their clinical procedures. Although deep learning classification methods are well‐developed in other areas of medical imaging, ICA image classification is still at an early stage. One of the most important reasons is the lack of available and high‐quality open‐access datasets. In this paper, we reported a new annotated ICA images dataset, CADICA, to provide the research community with a comprehensive and rigorous dataset of coronary angiography consisting of a set of acquired patient videos and associated disease‐related metadata. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity, by computer scientists to create computer‐aided diagnostic systems to help in such assessment, and to validate existing methods for CAD detection. In addition, baseline classification methods are proposed and analysed, validating the functionality of CADICA with deep learning‐based methods and giving the scientific community a starting point to improve CAD detection.

Invasive Coronary Angiography (ICA) images are considered the gold standard for assessing the state of the coronary arteries. Deep learning classification methods are widely used and well-developed in different areas where medical imaging evaluation has an essential impact due to the development of computer-aided diagnosis systems that can support physicians in their clinical procedures. In this paper, a new performance analysis of deep learning methods for binary ICA classification with different lesion degrees is reported. To reach this goal, an annotated dataset of ICA images containing the ground truth, the location of lesions, and seven possible severity degrees ranging between 0% and 100% was employed. The ICA images were divided into “lesion” or “non-lesion” patches. We aim to study how binary classification performance is affected by the different lesion degrees considered in the positive class. Therefore, five Convolutional Neural Network architectures – DenseNet-201, MobileNet-V2, NasNet-Mobile, ResNet-18, and ResNet-50 – were trained with different input images where different lesion degree ranges were gradually incorporated until considering the seven lesion degrees. Besides, four types of experiments with and without data augmentation were designed, whose F-measure and Area Under Curve (AUC) were computed. Reported results achieved an F-measure and AUC of 92.7% and 98.1%, respectively. However, lesion classification is highly affected by the degree of the lesion intended to be classified, with 15% less accuracy when < 99% lesion patches are present.

Background/Objectives: Coronary artery disease (CAD) and type-2 diabetes mellitus (T2DM) are characterized by chronic low-grade inflammation. However, measuring cytokines typically involves invasive blood sampling, which can be problematic for CAD patients. This study aimed to assess ophthalmological parameters and tear cytokines in patients with CAD, comparing those with comorbid T2DM to those without to understand their inflammatory profiles. Methods: One hundred subjects with suspected chronic or acute CAD were initially included in this single-center cross-sectional study after clinical stabilization. Seventy-two patients with confirmed CAD were divided into two groups: 32 patients with T2DM and 40 patients without T2DM. A total of 144 eyes were examined, and tear fluid samples were collected to determine cytokine concentrations. Ophthalmological parameters and tear concentrations of cytokines were analyzed, controlling for age, sex, and other cardiovascular risk factors. Results: Patients with CAD and T2DM exhibited decreased ophthalmological parameters and increased cytokine concentrations in comparison to those without T2DM. Significant inverse correlations between ophthalmological parameters and cytokine concentrations were observed. Following adjustment, a full logistic regression model for distinguishing patients with CAD and comorbid T2DM included macular cube volume, mean macular thickness, interleukin (IL)-4, IL-5, IL-6, IL-8, IL-9, IL-13, granulocyte colony-stimulating factor (G-CSF), CCL3, CCL4, and CCL11/eotaxin-1, demonstrating excellent discriminatory power (Area Under the Curve = 0.95, 95% Confidence Interval = 0.91–0.99; p < 0.001). Subsequently, IL-5 (Odds Ratio = 1.68, 95% CI = 1.26–2.24; p < 0.001), G-CSF (OR = 1.06, 95% CI = 1.02–1.11; p < 0.01), and CCL11/eotaxin-1 (OR = 1.56, 95% CI = 1.19–2.05; p = 0.001) emerged as the most distinguishing variables in a reduced model (AUC = 0.89, 95% CI = 0.84–0.95; p < 0.001). Conclusions: Differences in ophthalmological variables, mainly in cytokine concentrations, suggest distinct pathophysiological mechanisms in patients with CAD based on the presence of T2DM. These findings demonstrate that the inflammatory profile can be readily detected through tear sample cytokines, proving valuable for establishing more accurate prognoses and monitoring in cardiometabolic disorders.

Aims: The optimal management of older patients with acute coronary syndrome (ACS) remains unclear. The most effective evaluation methods for frailty and malnutrition are yet to be delineated, despite being proposed as predictors of adverse outcomes. Consequently, this study aims to assess the significance of these factors in predicting in-hospital complications within this population. Methods and results: In this single-centre, intrahospital case–control study, 217 older patients (aged >70 years) with ACS were categorized into two groups based on the occurrence of net adverse clinical events (NACE) during hospitalization: the NACE (n = 59) and non-NACE (n = 158) groups. Demographic and clinical variables, including assessments of frailty using the FRAIL scale (FS), dependency using the Barthel index, and nutritional status using both the prognostic nutritional index (PNI) and geriatric nutritional risk index, were collected. Regression models and receiver operating characteristics curves were constructed to predict NACE. Patients in the NACE group exhibited higher frailty (P < 0.001), increased dependency (P < 0.001), and poorer nutritional status (P < 0.05) compared with those in the non-NACE group. The FS [odds ratios (OR) 95% confidence interval (CI) = 2.03 (1.39–2.95), P < 0.001] and PNI [OR (95% CI) = 0.64 (0.41–0.99), P < 0.05] scores emerged as independent predictors of NACE in a multivariable model including age, sex, and traditional cardiovascular risk factors. This model demonstrated a high discriminatory power [area under the curve (95% CI) = 0.834 (0.768–0.899), P < 0.001] for NACE. Conclusion: Frailty and nutritional status could significantly contribute to the stratification and identification of older adults with ACS who are at risk of developing in-hospital complications.

The coronavirus disease of 2019 (COVID-19) has been a cause of significant morbidity and mortality worldwide. Among the short- and long-term consequences of COVID-19, myocarditis is a disease to be taken into consideration. Myocarditis, in general, is related to a poor prognosis. However, the epidemiology and prognosis of myocarditis related to COVID-19 are currently unknown. While vaccination against COVID-19 is of great benefit at a public health level, the risk of myocarditis should be considered in the context of the global benefits of vaccination. In this narrative review, we will summarize the etiopathogenic bases, the epidemiology, the clinical manifestations, the course, diagnosis, prognosis, and the treatment of myocarditis related to SARS-CoV-2, as well as myocarditis secondary to mRNA vaccines.

Pulmonary vein (PV) isolation is a well-established rhythm control therapy in atrial fibrillation (AF). Currently, there is no consensus on which ablation technique to use for the first procedure, cryoballoon (CB) or radiofrequency (RF). A retrospective cohort study was conducted on 1055 patients who underwent a first ablation, to assess both techniques based on the need for reablation. Patients with CB (n = 557) and RF (n = 498) ablations were clinically characterized and the need for reablation during a 30-month follow-up was used as the primary endpoint. Independent variables were analyzed to identify potential predictors. The need for reablation was significantly lower in the CB group than in the RF group (hazard ratio = 0.45 and 95% confident interval = 0.32–0.61; p < 0.001); in both paroxysmal and persistent AF, using a full-adjusted regression Cox model by age, sex, smoking, hypertension, diabetes mellitus, dyslipidemia, severe obstructive sleep apnea, dilated left atrium, persistent AF and early recurrence. RF ablation, dilated left atrium, persistent AF and early recurrence were identified as independent predictors of reablation. In addition, the CB-redo subgroup had a lower PV reconnection than the RF-redo subgroup. In conclusion, CB ablation suggests a reduction in the need for reablation and lower PV reconnection during the follow-up than RF ablation.