Cardiovascular diagnosis and therapy最新文献

Background: Hypereosinophilic syndrome (HES) represents a group of disorders with eosinophil-mediated end-organ damage. Eosinophilic myocarditis (EM) represents cardiac involvement in HES. Data are limited regarding this rare condition. To better understand contemporary clinical characteristics of EM in HES, we reviewed demographics, cardiac imaging, and outcomes of this condition at our center.

Methods: We performed a cross-sectional study of all patients aged >18 years with diagnosis of EM in HES at our center between September 1986 and January 2023. Relevant clinical data, including clinical presentation, medical history, medication use, comorbidities, imaging findings, and outcomes, were collected and analyzed.

Results: Of 1,664 patients identified with hypereosinophilia (HE), 36 cases of clinically diagnosed HES were identified. Of the 36 patients diagnosed with HES, 11 patients (30.6%) were diagnosed with EM. Of these, six patients underwent endomyocardial biopsy (EMB). The mean age was 57±12 years and 63.6% were female. Asthma was the most common comorbidity (54.5%). Patients with EM had significantly more dyspnea (63.6%), fatigue (54.5%), and neuropathy (36.4%) compared to those without cardiac involvement. Echocardiography was performed in all patients and cardiac magnetic resonance (CMR) imaging was performed in eight patients. Left ventricular (LV) thrombus was detected more frequently by CMR (5/8, 62.5%) compared to echocardiography (3/10, 30%). Subendocardial pattern of late gadolinium enhancement (LGE) was observed in the majority of patients on CMR (6/7, 85.7%). Steroids were utilized in 90.9% of cases, and aspirin in all patients. Compared to HES patients without cardiac involvement, thromboembolic events occurred significantly more frequently (63.6% vs. 24.0%, P=0.02).

Conclusions: In a 37-year cohort of HES-associated EM, echocardiography was the first-line imaging modality, while CMR was an essential but still under-utilized imaging modality. Patients with EM had significantly more thromboembolic events compared to HES without cardiac involvement.

Tetralogy of Fallot (TOF) is a condition that often leads to long-term enlargement of the aortic root in after surgery. The aortic dilation is believed to be caused by histological abnormalities of the aortic media and the hemodynamic characteristics of increased aortic flow, compared to pulmonary flow. Severe cyanosis, severe right ventricular outflow tract (RVOT) obstruction, older age at repair, a larger aortic size at the time of repair, and a history of an aortopulmonary shunt parameters related to long-standing volume overload of the aortic root were the reported risk factors. Right aortic arch, male sex, and the association of chromosome 22q11 deletion were also reported to be risk factors. The enlargement of the aortic root can cause aortic regurgitation (AR), leading to left ventricular dysfunction and an increased risk of aortic dissection, necessitating surgical intervention. The outcomes of aortic valve repair for AR have improved, leading to an increasing trend of choosing this approach, particularly in younger patients who would otherwise require mechanical valve replacement, thereby avoiding the need for anticoagulation therapy. The indications and timing of prophylactic aortic root replacement in adult patients with congenital heart disease have not been described, and the current consensus recommends surgical intervention for an ascending aorta with a diameter of ≥55 mm. In this review article, we focus on valve-sparing root replacement (VSRR) in TOF.

As the population of adults with congenital heart disease (ACHD) grows, there also grows an expanded need for non-invasive surveillance methods to guide management and intervention. A multimodal imaging approach layers complementary insights from echocardiography, computed tomography (CT), magnetic resonance imaging (MRI), and other modalities into a clinician's view of patient physiology. Merely applying strategies from acquired adult cardiac disease would be inadequate and potentially misleading. As data amasses in this small but growing population, investigators in the field of ACHD have discovered population-specific imaging biomarkers that identify deterioration and pivotal time points where intervention may reduce morbidity and mortality. Moreover, due to the variety of physiologies and the modest number of ACHD patients relative to that of adults with acquired heart disease, multicenter registries will be key in advancing research. The integration of well-defined imaging variables into these databases can help identify important biomarkers. Emerging technologies like computational fluid dynamics (CFD) and artificial intelligence (AI) are also primed to enhance imaging capabilities and clinical workflows, though require careful adaption as ACHD patients are not meaningfully represented in the training data for these technologies. Ultimately, a multimodal imaging approach is essential for optimizing care for ACHD patients, enabling personalized medicine where interventions can be performed before clinical deterioration occurs.

Background: About 30% of ischemic strokes do not have a clear cause, which is called cryptogenic stroke (CS). Increasing evidence suggests a potential link between CS and right-to-left shunt (RLS). RLS may lead to CS via paradoxical embolic mechanism. Hence, current study aims to explore the correlation between different RLS indexes and the occurrence of CS and its clinical diagnostic value in CS.

Methods: A total of 117 patients diagnosed with CS from October 2020 to June 2024 were randomly collected, and 93 patients with only headache and dizziness were randomly collected as the control group. All patients underwent agitated saline contrast echocardiography (ASCE) and the semi-quantitative classification, type and duration of RLS were analyzed. Spearman correlation analysis was used to analyze the correlation between RLS grade and type and the occurrence of CS, and the correlation between RLS duration and RLS grade and type. The efficacy of different RLS grades, types and durations in the diagnosis of CS were analyzed by receiver operating characteristic (ROC) curve.

Results: The included population ranged in age from 20-73 years, with 90 males and 120 females. There was no significant difference in basic data (e.g., gender, smoking history, drinking history, and the number of people with hypertension and diabetes) and serum biological indicators [triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL) and low-density lipoprotein (LDL)] between the CS group and the control group (all P>0.05). The proportion of RLS (77.78%) in the CS group was significantly higher than that in the control group (35.48%) (P<0.001). Spearman correlation analysis showed that RLS grade (r=0.569) and type (r=0.346) were significantly correlated with the occurrence of CS (both P<0.001). In addition, RLS duration was significantly correlated with RLS type (r=0.902, P<0.001), but not with RLS size (P>0.05). ROC curve analysis showed that RLS grade had the highest area under the curve (AUC) in CS diagnosis, which was 0.807 [95% confidence interval (CI): 0.748-0.866], the diagnostic sensitivity was 68.4%, and the specificity was 87.1%. In addition, the diagnostic AUC of RLS type and RLS duration in CS were similar, at 0.700 (95% CI: 0.626-0.773) and 0.707 (95% CI: 0.634-0.780), respectively.

Conclusions: RLS grade and RLS type are significantly correlated with the occurrence of CS. As an auxiliary means of CS diagnosis, RLS grade can effectively reduce the misdiagnosis rate of CS, which is of great clinical significance for early detection of CS risk.

Background: Cardiovascular diseases (CVDs) continue to be the world's greatest cause of death. To evaluate heart function and diagnose coronary artery disease (CAD), myocardial perfusion imaging (MPI) has become essential. Artificial intelligence (AI) methods have been incorporated into diagnostic methods such as MPI to improve patient outcomes in recent years. This study aims to employ a novel approach to examine how parameters/criteria and performance metrics affect the prioritization of selected MPI techniques and AI tools in CAD diagnosis. Identifying the most effective method in these two interconnected areas will increase the CAD diagnosis rate.

Methods: The study includes an in-depth investigation of popular convolutional neural network (CNN) models, including InceptionV3, VGG16, ResNet50, and DenseNet121, in addition to widely used machine learning (ML) models, including random forests (RF), K-nearest neighbor (KNN), support vector machine (SVM), and Naïve Bayes (NB). In addition, it includes the evaluation of nuclear MPI techniques, including positron emission tomography (PET) and single photon emission computed tomography (SPECT), with the non-nuclear MPI technique of cardiovascular magnetic resonance imaging (CMR). Various performance metrics were used to evaluate AI tools. They are F1-score, recall, specificity, precision, accuracy, and area under the receiver operating characteristic curve (AUC-ROC). For MPI techniques, the evaluation criteria include specificity, sensitivity, radiation dose, cost of scan, and study duration. The analysis was evaluated and compared using the fuzzy-based preference ranking organization method for enrichment evaluation (PROMETHEE), the multi-criteria decision-making method (MDCM).

Results: According to the study's findings, considering selected performance metrics or criteria, RF is the most efficient AI tool for SPECT MPI in the diagnosis of CAD with a net flow (Φ^net ) of 0.3778, and it's revealed that CMR is the most efficient MPI technique for CAD diagnosis with a net flow of 0.3666. By expanding this study, more comprehensive evaluations can be made in the diagnosis of CAD.

Conclusions: It was concluded that CMR outperformed the nuclear MPI techniques. SPECT, as the least advantageous technique, remained below average on other criteria except for the cost of the scan. Integrating the RF algorithm, which stands out as the most effective AI tool in diagnosing CAD, with SPECT MPI may contribute to SPECT becoming a superior alternative.