Reviews in cardiovascular medicine最新文献

Background: Obstructive sleep apnea (OSA) is a severe condition associated with numerous cardiovascular complications, including heart failure. The complex biological and morphological relationship between OSA and atherosclerotic cardiovascular disease (ASCVD) poses challenges in predicting adverse cardiovascular outcomes. While artificial intelligence (AI) has shown potential for predicting cardiovascular disease (CVD) and stroke risks in other conditions, there is a lack of detailed, bias-free, and compressed AI models for ASCVD and stroke risk stratification in OSA patients. This study aimed to address this gap by proposing three hypotheses: (i) a strong relationship exists between OSA and ASCVD/stroke, (ii) deep learning (DL) can stratify ASCVD/stroke risk in OSA patients using surrogate carotid imaging, and (iii) including OSA risk as a covariate with cardiovascular risk factors can improve CVD risk stratification.

Methods: The study employed the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) search strategy, yielding 191 studies that link OSA with coronary, carotid, and aortic atherosclerotic vascular diseases. This research investigated the link between OSA and CVD, explored DL solutions for OSA detection, and examined the role of DL in utilizing carotid surrogate biomarkers by saving costs. Lastly, we benchmark our strategy against previous studies.

Results: (i) This study found that CVD and OSA are indirectly or directly related. (ii) DL models demonstrated significant potential in improving OSA detection and proved effective in CVD risk stratification using carotid ultrasound as a biomarker. (iii) Additionally, DL was shown to be useful for CVD risk stratification in OSA patients; (iv) There are important AI attributes such as AI-bias, AI-explainability, AI-pruning, and AI-cloud, which play an important role in CVD risk for OSA patients.

Conclusions: DL provides a powerful tool for CVD risk stratification in OSA patients. These results can promote several recommendations for developing unique, bias-free, and explainable AI algorithms for predicting ASCVD and stroke risks in patients with OSA.

Background: The serum C-reactive protein-to-albumin ratio (CAR) has been identified as an adverse prognostic indicator in a variety of diseases. Nevertheless, there have been not been any studies reporting a relationship between CAR and the prognosis of chronic heart failure (CHF). This study was designed to evaluate the association between CAR and all-cause mortality in CHF patients with different ejection fractions.

Methods: A total of 1221 heart failure (HF) patients were enrolled at the First Affiliated Hospital of Kunming Medical University due to acute exacerbation of chronic HF from January 2017 to October 2021. The main outcome was all-cause mortality. After collecting baseline characteristics and laboratory results from all patients, we classified all participants into four groups based on CAR quartile (G1-G4). Kaplan-Meier survival curves and multivariate Cox proportional hazard models were employed to investigate the association between CAR and all-cause mortality in the patients. Furthermore, receiver operating characteristic (ROC) curves were constructed for CARs, and the area under the curve (AUC) was calculated.

Results: After excluding ineligible patients, we ultimately included 1196 patients with CHF. The mean age was 66.38 ± 12.521 years, and 62% were male. According to the Kaplan‒Meier analysis, with different ejection fractions, the risk of all-cause mortality was always highest for G4 (CAR >63.27) and lowest for G1 (CAR ≤7.67). Cox multivariate regression analyses indicated that the CAR was an independent predictor of all-cause mortality in all HF patients and in patients with different HF subtypes. According to the ROC curves, the AUC for the CAR was 0.732 (p < 0.001), with a sensitivity of 66.2% and the specificity of 72.7%. CAR had a greater predictive value for all-cause mortality than did C-reactive protein (CRP).

Conclusions: An elevated serum CAR was independently associated with an increased risk of all-cause death, regardless of heart failure subtype.

Coronary atherosclerosis (or coronary heart disease [CHD]) is a common cardiovascular disease that seriously damages human health. Percutaneous coronary stent implantation represents the primary treatment option for severe CHD in clinical practice; meanwhile, dual antiplatelet therapy (DAPT) is widely used to reduce the risk of postoperative thrombosis. Although the mechanisms of action of the two most commonly used antiplatelet drugs, aspirin and clopidogrel, remain unclear, clinical studies have shown that some patients are susceptible to stent thrombosis-antiplatelet resistance (high on-treatment platelet reactivity [HTPR])-despite using these drugs. Therefore, screening for HTPR and formulating personalized antiplatelet therapies is necessary. Ticagrelor, indobufen, and rivaroxaban are the most common and safe antiplatelet drugs used in clinical practice, with broad application prospects. This review summarizes the mechanisms of action of existing antiplatelet drugs, reasons for personalized treatment, screening of antiplatelet reactions, and development of novel antiplatelet drugs.

With advances in therapies to reduce cardiovascular events and improvements in coronary imaging, an increasing number of clinical trials have demonstrated that treatments to reduce cardiovascular events in coronary artery disease are associated with favorable effects on atherosclerotic plaque size and characteristics. It has been observed that various drugs may induce plaque regression and enhance plaque stability after plaque formation. Numerous clinical trials have been conducted to verify the occurrence of plaque stabilization and regression and their beneficial effects on cardiovascular events. Using invasive imaging techniques such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT), researchers have been able to gather evidence supporting the existence of coronary plaque stabilization and regression. In this review, we explore the possible mechanisms of plaque stabilization and regression, summarize the imaging features of plaque stabilization and regression, and assemble the evidence from clinical studies that have used different features as observational endpoints.

Cardiovascular disease (CVD) is a leading cause of death in women and risk of development is greatly increased following menopause. Menopause occurs over several years and is associated with hormonal changes, including a reduction in estradiol and an increase in follicle-stimulating hormone. This hormonal shift may result in an increased risk of developing abdominal adiposity, insulin resistance, dyslipidemia, vascular dysfunction, hypertension, type 2 diabetes mellitus (T2DM), metabolic dysfunction-associated fatty liver disease (MAFLD), and metabolic syndrome (MetS). Furthermore, with the onset of menopause, there is an increase in oxidative stress that is associated with impaired vascular function, inflammation, and thrombosis, further increasing the risk of CVD development. Despite the harmful consequences of the menopause transition being well known, women in premenopausal, perimenopausal, and postmenopausal stages are unlikely to be enrolled in research studies. Therefore, investigations on the prevention and treatment of cardiovascular and metabolic disease in middle-aged women are still relatively limited. Whilst lifestyle interventions are associated with reduced CVD risk in this population sample, the evidence still remains inconclusive. Therefore, it is important to explore the effectiveness of early intervention and potential therapeutic approaches to maintain cellular redox balance, preserve endothelium, and reduce inflammation. Glycine, N-acetylcysteine, and L-theanine are amino acids with potential antioxidant and anti-inflammatory activity and are identified as therapeutic interventions in the management of age-related and metabolic diseases. The benefits of the intake of these amino acids for improving factors associated with cardiovascular health are discussed in this review. Future studies using these amino acids are warranted to investigate their effect on maintaining the vascular health and cardiovascular outcomes of postmenopausal women.

Introduction: Coronary atherosclerosis serves as the primary pathological etiology underlying coronary artery disease (CAD). Thyroid hormones show potential as risk factors, aside from the main standard modifiable cardiovascular risk factors (SMuRFs). This research seeks to elucidate the link between thyroid activity and coronary atherosclerosis.

Methods: Single nucleotide polymorphisms (SNPs) linked to hypothyroidism (N = 213,990), Graves' disease (GD) (N = 190,034), other hyperthyroidism types (N = 190,799), thyroid-stimulating hormone (TSH) (N = 271,040), free thyroxine (FT4) (N = 119,120), and coronary atherosclerosis (N = 360,950) were retrieved from the IEU OpenGWAS, Finngen R9, and ThyroidOmics Consortium databases. Following the application of strict criteria to eliminate linkage disequilibrium, palindromic sequences, and heterozygous alleles, a bidirectional Mendelian Randomization (MR) analysis was conducted between the thyroid gland and coronary atherosclerosis using inverse variance weighting (IVW), weighted median (WM), and MR-Egger techniques. For sensitivity analysis, Cochran's Q test, leave-one-out method, and MR-Egger regression analysis were employed.

Results: The forward MR analysis indicates that genetic predispositions such as hypothyroidism (OR = 1.07; 95% CI 1.01-1.12; IVW-p = 0.021), Graves' disease (OR = 1.04; 95% CI 1.01-1.07; IVW-p = 0.002), and other forms of hyperthyroidism (OR = 1.05; 95% CI 1.01-1.10; IVW-p = 0.021) elevate the likelihood of developing coronary atherosclerosis. Additionally, no discernible evidence of a causality between FT4 or TSH, and coronary atherosclerosis (IVW-p > 0.05) was found. Coronary atherosclerosis is not related to increased risk of five thyroid function phenotypes in reverse MR analysis. The sensitivity analysis provided relatively reliable evidence to reinforce the validity of our findings.

Conclusions: Our findings are an investigation of the causality between thyroid function and coronary atherosclerosis. This study pinpointed potential heart disease risks linked to coronary atherosclerosis and offered additional understanding for defining SMuRFs in CAD.

Background: This study aimed to develop and evaluate the detection and classification performance of different deep learning models on carotid plaque ultrasound images to achieve efficient and precise ultrasound screening for carotid atherosclerotic plaques.

Methods: This study collected 5611 carotid ultrasound images from 3683 patients from four hospitals between September 17, 2020, and December 17, 2022. By cropping redundant information from the images and annotating them using professional physicians, the dataset was divided into a training set (3927 images) and a test set (1684 images). Four deep learning models, You Only Look Once Version 7 (YOLO V7) and Faster Region-Based Convolutional Neural Network (Faster RCNN) were employed for image detection and classification to distinguish between vulnerable and stable carotid plaques. Model performance was evaluated using accuracy, sensitivity, specificity, F1 score, and area under curve (AUC), with p < 0.05 indicating a statistically significant difference.

Results: We constructed and compared deep learning models based on different network architectures. In the test set, the Faster RCNN (ResNet 50) model exhibited the best classification performance (accuracy (ACC) = 0.88, sensitivity (SEN) = 0.94, specificity (SPE) = 0.71, AUC = 0.91), significantly outperforming the other models. The results suggest that deep learning technology has significant potential for application in detecting and classifying carotid plaque ultrasound images.

Conclusions: The Faster RCNN (ResNet 50) model demonstrated high accuracy and reliability in classifying carotid atherosclerotic plaques, with diagnostic capabilities approaching that of intermediate-level physicians. It has the potential to enhance the diagnostic abilities of primary-level ultrasound physicians and assist in formulating more effective strategies for preventing ischemic stroke.

Background: The impact of seasonal patterns on the mortality and morbidity of surgical patients with cardiovascular diseases has gained increasing attention in recent years. However, whether this seasonal variation extends to cardiovascular surgery outcomes remains unknown. This study sought to evaluate the effects of seasonal variation on the short-term outcomes of patients undergoing off-pump coronary artery bypass grafting (OPCABG).

Methods: This study identified all patients undergoing elective OPCABG at a single cardiovascular center between January 2020 and December 2020. Patients were divided into four groups according to the season of their surgery. The primary outcome was the composite incidence of mortality and morbidity during hospitalization. Secondary outcomes included chest tube drainage (CTD) within 24 h, total CTD, chest drainage duration, mechanical ventilation duration, and postoperative length of stay (LOS) in the intensive care unit (ICU) and hospital.

Results: Winter and spring surgeries were associated with higher composite incidence of mortality and morbidities (26.8% and 18.0%) compared to summer (15.7%) and autumn (11.1%) surgeries (p < 0.05). Spring surgery had the highest median CTD within 24 hours after surgery (640 mL), whereas it also exhibited the lowest total CTD (730 mL) (p < 0.05). Chest drainage duration was longer in spring and summer than in autumn and winter (p < 0.05). While no significant differences were observed in mechanical ventilation duration and hospital stay among the four seasons, the LOS in the ICU was longer in summer than in autumn (88 h vs. 51 h, p < 0.05).

Conclusions: The OPCABG outcomes might exhibit seasonal patterns in patients with coronary heart disease.

Background: Despite significant reductions in in-stent restenosis (ISR) incidence with the adoption of drug-eluting stents (DES) over bare metal stents (BMS), ISR remains an unresolved issue in the DES era. The risk factors associated with DES-ISR have not been thoroughly analyzed. This meta-analysis aims to identify the key factors and quantify their impact on DES-ISR.

Methods: We conducted comprehensive literature searches in PubMed, EMBASE, Cochrane, and Web of Science up to 28 February 2023, to identify studies reporting risk factors for DES-ISR. Meta-analysis was performed on risk factors reported in two or more studies to determine their overall effect sizes.

Results: From 4357 articles screened, 17 studies were included in our analysis, evaluating twenty-four risk factors for DES-ISR through meta-analysis. The pooled incidence of DES-ISR was approximately 13%, and significant associations were found with seven risk factors. Ranked risk factors included diabetes mellitus (odds ratio [OR]: 1.46; 95% confidence interval [CI]: 1.14-1.87), stent length (OR: 1.026; 95% CI: 1.003-1.050), number of stents (OR: 1.62; 95% CI: 1.11-2.37), involvement of the left anterior descending artery (OR: 1.56; 95% CI: 1.25-1.94), lesion length (OR: 1.016; 95% CI: 1.008-1.024), medical history of myocardial infarction (OR: 1.79; 95% CI: 1.12-2.86) and previous percutaneous coronary intervention (OR: 1.97; 95% CI: 1.53-2.55). Conversely, a higher left ventricular ejection fraction was identified as a protective factor (OR: 0.985; 95% CI: 0.972-0.997).

Conclusions: Despite advancements in stent technology, the incidence of ISR remains a significant clinical challenge. Our findings indicate that patient characteristics, lesion specifics, stent types, and procedural factors all contribute to DES-ISR development. Proactive strategies for early identification and management of these risk factors are essential to minimize the risk of ISR following DES interventions.

The prospero registration: CRD42023427398, https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=427398.

Cardiomyopathies, historically regarded as rare, are increasingly recognized due to advances in imaging diagnostics and heightened clinical focus. These conditions, characterized by structural and functional abnormalities of the myocardium, pose significant challenges in both chronic and acute patient management. A thorough understanding of the hemodynamic properties, specifically the pressure-volume relationships, is essential. These relationships provide insights into cardiac function, including ventricular compliance, contractility, and overall cardiovascular performance. Despite their potential utility, pressure-volume curves are underutilized in clinical settings due to the invasive nature of traditional measurement techniques. Recognizing the dynamic nature of cardiomyopathies, with possible transitions between phenotypes, underscores the importance of continuous monitoring and adaptive therapeutic strategies. Enhanced hemodynamic evaluation can facilitate tailored treatment, potentially improving outcomes for patients with these complex cardiac conditions.