Minerva cardiology and angiology最新文献

Background: The aim of this study is to investigate the association between inflammation-related markers in COVID-19 infection and ST-segment elevation myocardial infarction (STEMI).

Methods: We conducted an observational, single-center, retrospective study between January 2020 and November 2022. A total of 149 patients aged between 34 and 90 years, 28.2% (N.=42) female and 71.8% (N.=107) male, were included in the study. Systemic immune-inflammation index (SII), systemic inflammation-response indexes (SIRI), platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) were calculated for each patient. The patients were divided into two groups based on their presence or absence of a confirmed SARS-CoV-2 infection.

Results: During the in-hospital follow-up, mortality occurred in 12% (N.=20) of patients. Among the COVID-19 (+) and STEMI group, the mortality rate was 24.3% (N.=10), while it was 5.6% (N.=6) in the COVID-19 (-) and STEMI group (P=0.001). In multivariate logistic regression analysis, SII ([HR] = 7.198 [1.423-36.411], P=0.017) and PLR ([HR] = 5.762 [1.783-18.619], P=0.003) remained significant risk factor for mortality.

Conclusions: The SII, SIRI, NLR, and PLR are relatively new, simple, and effective inflammation-related markers that determine mortality risk in STEMI patients.

Contrast associated acute kidney injury (CA-AKI) is a major complication of contrast media (CM) exposure following percutaneous coronary intervention (PCI), associated with high rates of morbidity and mortality in both early and late phases. During the past years, several CA-AKI prevention strategies based on CM sparing have been proposed, which differ significantly in terms of methodological features and efficacy. In this review, we propose a new classification of these techniques based on their dependency on operators' management. Following, we summarize current evidence on the effectiveness in terms of CA-AKI reduction of each one of the currently available operator-dependent and -independent CM minimization strategies.

Large-scale clinical outcome trials have demonstrated significant reductions in cardiovascular (CV) and renal outcomes with sodium-glucose cotransporter-2 inhibitors (SGLT2i). These benefits are sustained in patients with a range of left ventricular ejection fractions (LVEF), irrespective of diabetes status, and in a variety of clinical settings, prompting incorporation into clinical practice guidelines for patients with chronic kidney disease (CKD), heart failure (HF), and atherosclerotic cardiovascular disease (ASCVD). The clinical benefits are mediated by an interplay of cardio-metabolic-renal mechanisms, and they have a favorable safety profile. We provide a review of the proposed mechanisms of cardiorenal protection and the evidence supporting the clinical benefits of SGLT2i in CKD, acute and chronic HF treatment and prevention , and ASCVD, highlighting the uses of SGLT2i in clinical practice guidelines.

Cardiac contractility modulation (CCM) signals are non-excitatory signals that are applied during the myocyte's absolute refractory period. These signals have been demonstrated to have an inotropic effect without increasing myocardial oxygen consumption. This has been observed in both preclinical animal studies and randomized clinical trials. CCM influences the expression of various genes that are abnormally expressed in heart failure: it reverses fetal myocyte gene programming associated with heart failure and regulates the expression of genes associated with calcium cycling and myocardial contractile machinery. Clinical investigations have primarily focused on patients with heart failure and normal QRS duration where CCM has demonstrated its safety and effectiveness in reducing heart failure-related hospitalizations, as well as improving symptoms, functional capacity, and overall quality of life. Currently, for individuals experiencing symptomatic heart failure with an ejection fraction ranging from 25% to 45% and a QRS duration of less than 130 ms, who are not suitable candidates for cardiac resynchronization therapy, CCM offers a viable treatment option. Even though promising results in specific HF subgroups have been published, further studies are needed to understand the role of CCM in tailored treatment for heart failure. Moreover, the role of multimodality imaging in lead placement and prognostic stratification in CCM patients should be further investigated. This review aims to summarize the main pathophysiological evidence related to the use of CCM and to highlight its role as a possible additional weapon in tailored treatment for specific subgroups of patients with heart failure.

Background: Complications of arrhythmia often occur in patients with acute myocardial infarction (AMI). This study mainly explored the expression and diagnostic significance of long non-coding RNA CYTOR (lncRNA CYTOR) in patients with AMI with arrhythmia, and analyzed the effects of CYTOR on inflammation and oxidative stress responses of cardiomyocytes.

Methods: CYTOR expression in serum samples from 119 cases of AMI with arrhythmia and 119 healthy subjects was determined by qRT-PCR. Receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic function of serum CYTOR in AMI with arrhythmia. AMI cell models were constructed by hypoxia/reoxygenation treatment. The pathological function of CYTOR in AMI was determined by the detection of inflammatory factors and oxidative stress indicators.

Results: Serum CYTOR was upregulated in patients with AMI with arrhythmia, which has a certain ability to distinguish patients from healthy individuals (P<0.001, AUC=0.8963). The levels of interleukin-1beta (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and malondialdehyde (MDA) were increased in the AMI cell model, while superoxide dismutase (SOD) levels were decreased (P<0.001), which was alleviated by silencing CYTOR.

Conclusions: Overexpression of CYTOR may aggravate the condition of AMI patients with arrhythmia, which promotes oxidative stress injury and inflammatory response of cardiomyocytes. CYTOR can be a reference factor for diagnostic biomarkers of AMI with arrhythmia.

Background: Atrial fibrillation (AF) is the most common atrial arrhythmia after transcatheter aortic valve implantation (TAVI) and is associated with high mortality. Although some clinical and echocardiographic variables have been defined to predict new-onset atrial fibrillation (NOAF), electrophysiologic (EP) parameters have not been identified yet. We aimed to investigate the impact of atrial refractoriness on NOAF after TAVI.

Methods: Seventy-nine consecutive patients who underwent TAVI were enrolled in this trial. All patients undergoing TAVI were screened for AF.

Results: Fifteen (19%) had AF during the follow-up period. Patients with NOAF were older and had a higher BMI and STS. Left atrial diameter (LAD) was higher, left ventricular ejection fraction (LVEF) was lower, and preprocedural LVEDP was higher in patients with NOAF. As electrophysiologic (EP) parameters, atrial effective refractory periods (AERP) (in high right atrium [AERPHRA], in right posterolateral atrium [AERPRPL], and in distal coronary sinus [AERPDCS]) were lower, difference between atrial effective refractory periods (AERPDISP) and PA intervals were higher in patients with AF than those without AF. The only independent parameter that influenced the development of AF after TAVI was AERPDISP. The Receiver Operating Characteristic (ROC) analysis showed that an AERPDISP>46 msec significantly separated those with AF and those without AF with a sensitivity of 85% and a specificity of 97%.

Conclusions: The current study demonstrates that the only independent variable predicting NOAF is AERPDISP. Therefore, increased AERPDISP values may help predict patients with high risk for NOAF and needing specific therapies.

Background: Acute myocardial infarction (AMI) is a major cause of death in cardiovascular patients. SOCS3's protective role in cardiac I/R-I is being explored, and miRNAs, particularly miRNA-148a-3p, are suspected to target SOCS3. To elucidate the role of miRNA-148a-3p targeting lipid metabolism gene SOCS3 in cardiac ischemia-reperfusion injury (I/R-I) in rats.

Methods: Derived mRNA expression data GSE59867 from GEO, identified 558 lipid metabolism genes from KEGG and GSEA, and screened for differentially expressed genes in acute myocardial infarction (AMI). Predicted miRNA-148a-3p targeting SOCS3 using TargetScanHuman, validated binding via luciferase assay and 3'UTR mutation. Established a rat I/R-I model to assess miRNA-148a-3p and SOCS3 expression, and investigated SOCS3 regulation by miRNA-148a-3p overexpression. Analyzed expression of NF-κB p65, IL-1β, and TNF-α-related proteins, and evaluated cardiac hemodynamics post-SOCS3 regulation by miRNA-148a-3p.

Results: In GSE59867, TSPO, SOCS3, LRP1, PLB1, CYP1B1, PPARG, ACSL1, and CYP27A1 were identified as differentially expressed lipid metabolism genes in AMI. The results of immune infiltration showed a close relationship between the differential lipid metabolism genes and the infiltration of immune cells such as macrophages and monocytes. The random forest algorithm identified SOCS3 as the key gene. The luciferase reporter gene demonstrated the participation of miRNA-148a-3p in the regulation of SOCS3 by binding to its 3'UTR. In vivo experiments revealed low expression of miRNA-148a-3p in myocardial I/R, while SOCS3 was highly expressed. Elevated miRNA-148a-3p expression led to a decrease in SOCS3, NF-κB p65, IL-1β, and TNF-α levels during cardiac I/R-I. Overexpression of miRNA-148a-3p enhanced the cardiac performance in rats experiencing cardiac I/R-I.

Conclusions: Overexpression of miRNA-148a-3p regulates NF-κB signaling pathway by targeting lipid metabolism gene SOCS3, reduces inflammatory response, and then reduces cardiac I/R-I in rats.

The chest X-ray (CXR) has a wide range of clinical indications in the field of cardiology, from the assessment of acute pathology to disease surveillance and screening. Despite many technological advancements, CXR interpretation error rates have remained constant for decades. The application of machine learning has the potential to substantially improve clinical workflow efficiency, pathology detection accuracy, error rates and clinical decision making in cardiology. To date, machine learning has been developed to improve image processing, facilitate pathology detection, optimize the clinical workflow, and facilitate risk stratification. This review explores the current and potential future applications of machine learning for chest radiography to facilitate clinical decision making in cardiology. It maps the current state of the science and considers additional potential use cases from the perspective of clinicians and technologists actively engaged in the development and deployment of deep learning driven clinical decision support systems.

Background: We explored the link between sST2 and NT-proBNP levels and postoperative atrial fibrillation (POAF) incidence in non-cardiac surgery patients in this study.

Methods: The research involved 302 participants over 40 years old who underwent medium and/or high-risk non-cardiac surgeries. These patients were divided into two groups: those who developed POAF and those who did not.

Results: The study cohort consisted of a total of 302 patients, with 14 (4.6%) experiencing POAF. POAF was more common in patients with previous heart failure, a high Left Atrial Volume Index (LAVI), and elevated ASA and RCRI scores (all P<0.05). LAVI, sST2, NT-proBNP, and RCRI scores were found to be independent predictors of POAF in patients undergoing non-cardiac surgeries (all P<0.05). The area under the curve (AUC) for sST2 and NT-proBNP in predicting POAF was 0.707 (95% CI 0.544-0.869; P=0.009) and 0.727 (95% CI 0.598-0.857; P=0.004), respectively. Combined elevation of sST2 and NT-proBNP increased the likelihood of developing POAF by approximately 8.5 times (OR: 8.65, CI 95% 1.06-35.3, P=0.044).

Conclusions: sST2 and NT-proBNP are valuable predictors of POAF in patients undergoing non-cardiac surgery. Identifying these predictors can help in recognizing high-risk patient groups for POAF.