Functional Evaluation of Intermediate Coronary Lesions with Integrated Computed Tomography Angiography and Invasive Angiography in Patients with Stable Coronary Artery Disease

Accurate diagnosis of severe coronary stenosis based on resting magnetocardiography: a prospective, single-center, cross-sectional analysis

OBJECTIVE

To evaluate the role of resting magnetocardiography in identifying severe coronary artery stenosis in patients with suspected coronary artery disease.

METHODS

A total of 513 patients with angina symptoms were included and divided into two groups based on the extent of coronary artery disease determined by angiography: the non-severe coronary stenosis group (< 70% stenosis) and the severe coronary stenosis group (≥ 70% stenosis). The diagnostic model was constructed using magnetic field map (MFM) parameters, either individually or in combination with clinical indicators. The performance of the models was evaluated using receiver operating characteristic curves, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Calibration plots and decision curve analysis were performed to investigate the clinical utility and performance of the models, respectively.

RESULTS

In the severe coronary stenosis group, QR_MCTDd, S_MDp, and TT_MAC50 were significantly higher than those in the non-severe coronary stenosis group (10.46 ± 10.66 vs. 5.11 ± 6.07, P < 0.001; 7.2 ± 8.64 vs. 4.68 ± 6.95, P = 0.003; 0.32 ± 57.29 vs. 0.26 ± 57.29, P < 0.001). While, QR_MVamp, R_MA, and T_MA in the severe coronary stenosis group were lower (0.23 ± 0.16 vs. 0.28 ± 0.16, P < 0.001; 55.06 ± 48.68 vs. 59.24 ± 53.01, P < 0.001; 51.67 ± 39.32 vs. 60.45 ± 51.33, P < 0.001). Seven MFM parameters were integrated into the model, resulting in an area under the curve of 0.810 (95% CI: 0.765-0.855). The sensitivity, specificity, PPV, NPV, and accuracy were 71.7%, 80.4%, 93.3%, 42.8%, and 73.5%; respectively. The combined model exhibited an area under the curve of 0.845 (95% CI: 0.798-0.892). The sensitivity, specificity, PPV, NPV, and accuracy were 84.3%, 73.8%, 92.6%, 54.6%, and 82.1%; respectively. Calibration curves demonstrated excellent agreement between the nomogram prediction and actual observation. The decision curve analysis showed that the combined model provided greater net benefit compared to the magnetocardiography model.

CONCLUSIONS

The novel quantitative MFM parameters, whether used individually or in combination with clinical indicators, have been shown to effectively predict the risk of severe coronary stenosis in patients presenting with angina-like symptoms. Magnetocardiography, an emerging non-invasive diagnostic tool, warrants further exploration for its potential in diagnosing coronary heart disease.

Conquer coronary artery perforation with magic hands

Coronary artery perforation (CAP) poses a significant challenge for interventional cardiologists. Management of CAP depends on the location and severity of the perforation. The conventional method for addressing the perforation of large vessels involves the placement of a covered stent, while the perforation of distal and collateral vessels is typically managed using coils, autologous skin, subcutaneous fat, microspheres, gelatin sponge, thrombin or other substances. However, the above techniques have certain limitations and are not applicable in all scenarios. Our team has developed a range of innovative strategies for effectively managing CAP. This article provides an insightful review of the various tips and tricks for the treatment of CAP.

Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography

Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning-based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR-TB and classify crucial subtypes, including rifampicin-resistant tuberculosis, multidrug-resistant tuberculosis, and extensively drug-resistant tuberculosis. Moreover, chest lesions were manually annotated to endow the model with robust power to assist radiologists in image interpretation and the Circos revealed the relationship between chest abnormalities and specific types of DR-TB. Finally, DeepTB achieved an area under the curve (AUC) up to 0.930 for thoracic abnormality detection and 0.943 for DR-TB diagnosis. Notably, the system demonstrated instructive value in DR-TB subtype classification with AUCs ranging from 0.880 to 0.928. Meanwhile, class activation maps were generated to express a human-understandable visual concept. Together, showing a prominent performance, DeepTB would be impactful in clinical decision-making for DR-TB.

Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images

This study aimed to design an end-to-end deep learning model for estimating the value of fractional flow reserve (FFR) using angiography images to classify left anterior descending (LAD) branch angiography images with average stenosis between 50 and 70% into two categories: FFR > 80 and FFR ≤ 80. In this study 3625 images were extracted from 41 patients' angiography films. Nine pre-trained convolutional neural networks (CNN), including DenseNet121, InceptionResNetV2, VGG16, VGG19, ResNet50V2, Xception, MobileNetV3Large, DenseNet201, and DenseNet169, were used to extract the features of images. DenseNet169 indicated higher performance compared to other networks. AUC, Accuracy, Sensitivity, Specificity, Precision, and F1-score of the proposed DenseNet169 network were 0.81, 0.81, 0.86, 0.75, 0.82, and 0.84, respectively. The deep learning-based method proposed in this study can non-invasively and consistently estimate FFR from angiographic images, offering significant clinical potential for diagnosing and treating coronary artery disease by combining anatomical and physiological parameters.

Exosomes and their derivatives as biomarkers and therapeutic delivery agents for cardiovascular diseases: Situations and challenges

Microvesicles known as exosomes have a diameter of 40 to 160 nm and are derived from small endosomal membranes. Exosomes have attracted increasing attention over the past ten years in part because they are functional vehicles that can deliver a variety of lipids, proteins, and nucleic acids to the target cells they encounter. Because of this function, exosomes may be used for the diagnosis, prognosis and treatment of many diseases. All throughout the world, cardiovascular diseases (CVDs) continue to be a significant cause of death. Because exosomes are mediators of communication between cells, which contribute to many physiological and pathological aspects, they may aid in improving CVD therapies as biomarkers for diagnosing and predicting CVDs. Many studies demonstrated that exosomes are associated with CVDs, such as coronary artery disease, heart failure, cardiomyopathy and atrial fibrillation. Exosomes participate in the progression or inhibition of these diseases mainly through the contents they deliver. However, the application of exosomes in diferent CVDs is not very mature. So further research is needed in this field.

Predicting cutaneous malignant melanoma patients' survival using deep learning: a retrospective cohort study

BACKGROUND

Cutaneous malignant melanoma (CMM) has the worst prognosis among skin cancers, especially metastatic CMM. Predicting its prognosis accurately could direct clinical decisions.

METHODS

The Surveillance, Epidemiology, and End Results database was screened to collect CMM patients' data. According to diagnosed time, patients were subdivided into three cohorts, train cohort (diagnosed between 2010 and 2013), validation cohort (diagnosed in 2014), and test cohort (diagnosed in 2015). Train cohort was used to train deep learning survival model for cutaneous malignant melanoma (DeepCMM). DeepCMM was then evaluated in train cohort and validation cohort internally, and validated in test cohort externally.

RESULTS

DeepCMM showed 0.8270 (95% CI, confidence interval, CI 0.8260-0.8280) as area under the receiver operating characteristic curve (AUC) in train cohort, 0.8274 (95% CI 0.8286-0.8298) AUC in validation cohort, and 0.8303 (95% CI 0.8289-0.8316) AUC in test cohort. Then DeepCMM was packaged into a Windows 64-bit software for doctors to use.

CONCLUSION

Deep learning survival model for cutaneous malignant melanoma (DeepCMM) can offer a reliable prediction on cutaneous malignant melanoma patients' overall survival.

Cardiovascular implications of inflammatory bowel disease: An updated review

Emerging data highlights the heightened risk of atherosclerotic cardiovascular diseases (ASCVD) in patients with chronic inflammatory disorders, particularly those afflicted with inflammatory bowel disease (IBD). This review delves into the epidemiological connections between IBD and ASCVD, elucidating potential underlying mechanisms. Furthermore, it discusses the impact of current IBD treatments on cardiovascular risk. Additionally, the cardiovascular adverse effects of novel small molecule drugs used in moderate-to-severe IBD are investigated, drawing parallels with observations in patients with rheumatoid arthritis. This article aims to comprehensively evaluate the existing evidence supporting these associations. To achieve this, we conducted a meticulous search of PubMed, spanning from inception to August 2023, using a carefully selected set of keywords. The search encompassed topics related to IBD, such as Crohn's disease and ulcerative colitis, as well as ASCVD, including coronary artery disease, cardiovascular disease, atrial fibrillation, heart failure, conduction abnormalities, heart blocks, and premature coronary artery disease. This review encompasses various types of literature, including retrospective and prospective cohort studies, clinical trials, meta-analyses, and relevant guidelines, with the objective of providing a comprehensive overview of this critical intersection of inflammatory bowel disease and cardiovascular health.

Application progress of nursing intervention in cardiac surgery

As a stressor, cardiac surgery affects the physiology and psychology of patients, as well as their postoperative recovery. Patients tend to worry about cognitive deficiency, pain, discomfort, the risk of death, sleep, complications, and other factors, resulting in stress and anxiety. Moreover, serious adverse events, such as circulatory and respiratory dysfunction and infection, tend to occur after cardiac surgery and increase the economic burden on patients. Therefore, appropriate nursing interventions should be selected to strengthen patients’ cognitive levels, compliance, and postoperative practices to accelerate their recovery, reduce complications, and shorten hospital stays so as to contribute to patients’ lives and health.

Complete Percutaneous Coronary Revascularization in Acute Coronary Syndromes With Multivessel Coronary Disease: A Systematic Review

Multivessel disease (MVD) affects approximately 50% of patients with acute coronary syndromes (ACS) and is significantly burdened by poor outcomes and high mortality. It represents a clinical challenge in patient management and decision making and subtends an evolving research area related to the pathophysiology of unstable plaques and local or systemic inflammation. The benefits of complete revascularization are established in hemodynamically stable ACS patients with MVD, and guidelines provide some reference points to inform clinical practice, based on an evidence level that is solid for ST-segment elevation myocardial infarction and less robust for non-ST-segment elevation myocardial infarction and cardiogenic shock. However, several areas of uncertainty remain, such as the optimal timing for complete revascularization or the best guiding strategy for intermediate stenoses. We performed a systematic review of current evidence in the field of percutaneous revascularization in ACS and MVD, also including future perspectives from ongoing trials that will directly compare different timing strategies and investigate the role of invasive and noninvasive guidance techniques. (Complete percutaneous coronary revascularization in patients with acute myocardial infarction and multivessel disease; CRD42022383123).

Accuracy and Reproducibility of Coronary Angiography-Derived Fractional Flow Reserve in the Assessment of Coronary Lesion Severity

Purpose

Coronary angiography-derived fractional flow reserve (caFFR) is a novel computational flow dynamics (CFD)-derived assessment of coronary vessel flow with good diagnostic performance. Herein, we performed a retrospective study to evaluate the reproducibility of caFFR findings between observers and investigate the diagnostic performance of caFFR for coronary stenosis defined as FFR ≤0.80, especially in the grey zone (0.75≤caFFR ≤0.80).

Patients and Methods

A total of 150 patients (167 coronary vessels) underwent caFFR (with FlashAngio used for calculation of flow variables) and subsequent invasive fractional flow reserve (FFR) measurements. Outcomes, including reproducibility, were compared for vessels in and outside the grey zone.

Results

The correlation of caFFR findings was good between the two laboratories (r = 0.723, p<0.001). The AUC of ROC were both high for caFFR-CoreLab1 and caFFR-CoreLab2 (0.975 and 0.883). The diagnostic accuracy, sensitivity, specificity, and negative and positive predictive values were not significantly different between the two laboratories (p>0.05). caFFR had a strong correlation with measures to FFR (r=0.911, p<0.001). There was no systematic difference between caFFR and FFR on Bland-Altman analysis in and outside the grey zone. There was no difference in diagnostic accuracy between the grey and non-grey zones in the prediction of FFR ≤0.80 (p=0.09).

Conclusion

The inter-observer reproducibility for caFFR was high, and the diagnostic accuracy of caFFR was good compared to that of FFR.