Cardiology最新文献

Introduction: Stable coronary artery disease (CAD) is a leading cause of cardiac morbidity and mortality worldwide, with elevated native T1 value linked to major adverse cardiovascular events. However, predictors of elevated native T1 value in stable CAD still need to be studied. This study aimed to identify clinical and imaging predictors of elevated native T1 values in CAD patients.

Methods: A total of 316 consecutive stable CAD patients (median age 58 years, 91.8% male) undergoing coronary computed tomography angiography and cardiovascular magnetic resonance native T1 mapping were included, along with 30 age- and sex-matched healthy controls. Patients were divided into normal and elevated native T1 groups based on the normal global native T1 reference; logistic regressions were used to identify predictors.

Results: Patients with elevated native T1 values were more likely to be aged ≥60 years, abstain from alcohol, have abnormal electrocardiogram findings, multivessel disease, noncalcified plaques, greater degrees of stenosis, stenosis ≥50% in two or three coronary arteries, and computed tomography-derived fractional flow reserve ≤0.8 (p < 0.05). Multivariate logistic regression identified age ≥60 years (odd ratio [OR]: 2.23, 95% confidence interval [95% CI]: 1.15-4.30, p = 0.018), stenosis ≥50% in two (OR: 13.27, 95% CI: 3.38-56.94, p < 0.001) or three coronary arteries (OR: 114.19, 95% CI: 20.53-276.59, p < 0.001), and left anterior descending FFR ≤0.8 (OR: 2.69, 95% CI: 1.16-6.29, p = 0.021) as independent risk factors, whereas alcohol consumption (OR: 0.47, 95% CI: 0.25-0.88, p = 0.019) was a predictor of normal native T1 values, with strong predictive performance (area under the curve = 0.832, Brier score = 0.142).

Conclusion: Our findings could help clinicians make individualized diagnosis and treatment of stable CAD patients, which also provide a foundation for predicting prognosis. This research has been registered in the National Medical Research Registration and Filing Information System, numbered MR-32-24-030226.

Introduction: Effective prediction of in-stent restenosis and revascularization after coronary stent implantation and interventions targeting risk factors that may lead to these events are crucial for their prevention and management.

Methods: Based on a C5.0 decision tree approach, data from 2,326 patients from two centers were included. We comprehensively analyzed 34 risk factors that may affect in-stent restenosis and revascularization after stent implantation and conducted predictions and risk factor analyses for in-stent restenosis and revascularization following coronary stent implantation.

Results: The accuracy of predicting in-stent restenosis following coronary stent implantation with a median follow-up period of 30 months was as follows: area under the curve (AUC) in the training set, 0.996; AUC in the internal validation set, 0.988; and AUC in the external validation set, 0.889, with an f1 value of 0.95, a sensitivity of 99.16%, and a specificity of 91.72%. Additionally, the accuracy of revascularization prediction was as follows: AUC in the training set, 0.984; AUC in the internal validation set, 0.956; and AUC in the external validation set, 0.876, with an f1 value of 0.84, a sensitivity of 96.43%, and a specificity of 25%. We also conducted a risk factor analysis.

Conclusion: We successfully constructed a predictive and risk factor analysis model for in-stent restenosis and revascularization following coronary stent implantation. This model may be helpful for clinical decision-making.

Introduction: Hypertrophic cardiomyopathy (HCM) patients may be at risk for major adverse cardiovascular events (MACEs), making risk stratification essential for implementing interventions in high-risk individuals. Deep transfer learning (DTL) and radiomics have made significant advances in the medical field; however, to date, no studies have combined echocardiography in HCM patients with DTL and radiomics to develop predictive models for identifying individuals at risk for MACE.

Methods: This study is a retrospective analysis that included 210 HCM patients, with a mean follow-up time of 29.44 ± 16.21 months. Among the patients, 59 experienced MACE and 151 non-MACE. The patients were randomly divided into training and validation sets in an 8:2 ratio. We collected chest parasternal left ventricular long-axis and short-axis images, with the left ventricular myocardial region defined as the region of interest. Radiomic features were extracted using the PyRadiomics software package, and DTL features were obtained through the pre-trained Resnet50 model. These radiomic and DTL features were then combined, and feature selection was conducted using the least absolute shrinkage and selection operator. The selected features were used to construct the DTL-RAD predictive model with machine learning algorithms. The model's diagnostic performance was evaluated using the receiver operating characteristic curve and decision curve analysis (DCA). Finally, we compared the prediction performance of the DTL-RAD model with those of models built using only radiomic features or only DTL features.

Results: The diagnostic performance of the DTL-RAD model in both the training and validation sets was excellent, with AUC values of 0.936 and 0.918, specificity values of 0.852 and 0.767, and sensitivity values of 0.892 and 0.929, respectively. It significantly outperformed models that used only radiomics or DTL features. Furthermore, the DCA demonstrated that the DTL-RAD model exhibited superior clinical applicability and effectiveness, surpassing the performance of other models.

Conclusion: The DTL-RAD model demonstrated exceptional performance in identifying HCM patients at risk of MACE, accurately detecting high-risk individuals among HCM patients at an early stage. This provides a basis for precise clinical intervention, effectively reducing the incidence of MACE in HCM patients.

Introduction: Blood biomarkers of myocardial damage or stress are routinely used to guide the diagnosis and management of both acute and chronic cardiac conditions. The circulating levels of these proteins are directly influenced by source tissue expression levels, and understanding where and to what degree they are expressed throughout the body can yield insights into their properties as biomarkers. Thus, in this descriptive study, we sought to comprehensively map the expression of twelve clinically established or emerging cardiac biomarkers across a broad spectrum of human tissues and comparatively assess expression characteristics that could impact diagnostic performance.

Methods: Existing genome-wide mRNA sequencing data originating from 16,357 cardiac and noncardiac tissue specimens harvested from 946 donors were used to quantify the expression levels of genes coding for the twelve proteins of interest (cTnT, cTnI, H-FABP, proANP, proBNP, Mb, CKTOTAL, CK-MB, LDHTOTAL, LDH-1, sST2, and cMyBP-C). Cardiac abundance, atrioventricular heterogeneity, and cardiac enrichment were subsequently assessed and compared between genes, both in the total pool of specimens and subsets of specimens grouped by donor sex and age.

Results: When considering the entirety of our analyses, the spatial expression characteristics of cMyBP-C, one of the emerging biomarkers we investigated, compared favorably to those of established biomarkers such as the troponins, suggesting that it may be a viable supplement to markers currently in clinical use. However, several other emerging biomarkers we assessed, including sST2 and H-FABP, displayed high expression in numerous noncardiac tissues that could serve as diagnostic confounds and limit their clinical value. In addition, we also observed differences between the expression profiles of closely related established biomarkers that have often been used interchangeably, including cTnI and cTnT, and proANP and proBNP, that could explain recent reports of discordant blood measures. Finally, we observed notable age and sex-related differences in the expression of proANP and proBNP within cardiac tissue specifically that support calls for the use of tiered diagnostic cutoffs.

Conclusion: Our findings provide insights into the potential utility of several notable emerging cardiac biomarkers and new information that could mechanistically explain previously reported phenomena or highlight possible diagnostic advantages, disadvantages, or use-caveats regarding others currently measured in clinical care.

Introduction: The outcome of patients with hypertrophic cardiomyopathy (HCM) largely depends on the presence of ventricular arrhythmias. T2 mapping is a well-documented cardiovascular magnetic resonance (CMR) sequence for edema evaluation and quantification. Our aim was to evaluate whether T2 mapping values might predict ventricular arrythmias and clinical outcome in patients with HCM.

Methods: Consecutive patients (n = 48, mean age 50 ± 18 years) with HCM and healthy volunteers (n = 21, mean age 44 ± 5 years) underwent a CMR scan (3 Tesla scanner). T2 mapping values were presented using a 16-segment AHA model and divided into 3 tertiles. Late gadolinium enhancement (LGE) was calculated as percentage of left ventricular mass. Ventricular tachycardia (VT) was recorded by 24-h Holter monitoring or during exercise stress test. The clinical endpoint was defined as the presence of VT, admission due to arrhythmias, and implantable cardioverter defibrillator insertion.

Results: The clinical endpoint was documented in 69%, 62%, and 50% of the third, second, and first T2 mapping tertiles, respectively (p for trend = 0.03). A multivariate analysis including age, gender, LGE, and T2 mapping demonstrated that T2 mapping was an independent predictor of the clinical endpoint as a continuous variable (OR 1.56; 1.03-2.38, p = 0.04) and the third T2 tertile (OR 22.80; 1.40-361.60, p = 0.03), respectively.

Conclusion: Elevated T2 mapping values were found to be an independent predictor of the clinical endpoint in HCM patients. These results highlight the potential role of edema in HCM arrhythmias.

Introduction: Intracoronary acetylcholine provocative testing is the gold standard method for the assessment of epicardial and/or microvascular spasm, with the latter diagnosed when there are acetylcholine (ACh)-induced chest pain and ischaemic electrocardiogram changes in the absence of epicardial spasm. While epicardial spasm can be visualised, microvascular spasm cannot and remains a presumed diagnosis.

Methods: This article describes a hydrodynamic model developed to calculate the epicardial and microvascular resistances for both pre- and post-ACh administration. The model is based on the concept of two resistances (epicardial and microvascular) located in a series arrangement. The epicardial resistance is obtained as a hydraulic resistance, accounting for the friction resistance between the coronary blood flow and the arterial walls. The microvascular resistance is calculated by subtracting the epicardial resistance from the ratio of the pressure and flow measured using coronary guidewire-based techniques.

Conclusion: This novel methodology provides key insights into the physiological characteristics of epicardial and microvascular spasm during ACh provocation testing. Further clinical validation is required to explore the clinical utility of this methodology.

Introduction: Transition of care (ToC) is a critical time that requires effective management, especially for patients with cardiovascular disease, who have complex health needs. Pharmacists can play an integral role in improving medication safety and care coordination at ToC. This review determined the types of pharmacist-led multidisciplinary ToC services and associated outcomes in patients with cardiovascular disease.

Methods: A systematic literature search of four databases; PubMed, Embase, CINAHL, and Scopus, was undertaken from inception until June 2023. Abstracts and full text were screened against eligibility criteria. Extracted data included study characteristics, ToC service descriptions, primary and secondary outcomes, limitations, and key findings. Study findings were synthesised narratively. The types of in-patient and post-discharge activities and their effect on patient outcomes were critiqued and presented.

Results: Of the 1822 studies identified, 37 were included in the final review. The most common primary outcome reported was 30-day all cause readmissions (n=14). Pharmacist-led ToC services incorporated a diverse combination of inpatient and post-discharge activities. The most common in-patient activities included medication history and reconciliation, patient medication education, and medication review and optimisation. The most common post-discharge activities were medication review and optimisation, adherence assessments, and medication education. Multifaceted ToC services that involved inpatient and post-discharge activities demonstrated statistically significant changes in readmission rates and other patient reported outcomes.

Conclusion: A variety of multifaced approaches to pharmacist-led ToC services have been evaluated within many studies, reporting favourable effects on outcomes for patients with cardiovascular disease. Tailoring ToC services to specific patient populations and settings, targeting activities to high-risk patients, and a multidisciplinary approach, are important to optimise ToC and patient outcomes.