BACKGROUND

Myocardial bridging (MB) is a congenital coronary anomaly characterized by an epicardial coronary artery taking an intramuscular course, causing systolic compression of the tunneled segment. In comparison to coronary artery disease (CAD), myocardial bridges have been uncommonly associated with acute coronary syndromes and sudden cardiac death.Evidence of accelerated atherosclerotic plaque formation proximal to the bridged segment may increase the risk for future adverse cardiac events in these patients.

This Single–Center Retrospective Study included 323 adult in-patients who underwent coronary angiography for suspected myocardial ischemia in 2022. Clinical information and in-hospital outcomes were obtained by review of medical records.

Myocardial bridging was observed in 31 out of 323 patients (9.60%), with the majority in the mid-left anterior descending artery (87.10%). MB was more prevalent in females (56.62%), and these patients were younger than patients with obstructive CAD (56.9 versus 63.6 years). Chronic Coronary Syndrome was more prevalent in the MB group (82.62%). The coronary segment proximal to the area with MB showed the concurrent presence of obstructive CAD in 16.12% and non-obstructive CAD in 29.03% of cases. In-hospital mortality occurred in 4.44% of the studied population. However, there were no mortalities in the MB group.

Among patients admitted for suspected myocardial ischemia, 9.6% had MB. These patients were younger and, more often, female. Obstructive and non-obstructive CAD were noted in bridged vessels. Although patients with obstructive CAD have a higher risk of experiencing in-hospital death and cardiac complications, evidence of increased atherosclerotic plaque formation in bridged vessels has important implications for future adverse cardiac events and repeat hospitalizations in the MB population. Aggressive risk factor modification, emphasis on long-term follow-up, and the establishment of clinical practice guidelines are therefore necessary for patients with MB.

BACKGROUND OF THE STUDY

Coronary artery disease (CAD) poses a significant global health challenge. In the Philippines, despite increased availability of coronary artery bypass grafting (CABG), cost remains a barrier to access. This research evaluates key factors such as all-cause death, cardiovascular death, repeat revascularization and quality of life post-CABG under the Philippine Health Z Benefit Package (PZBP). Its findings provide critical insights for shaping clinical practices, policymaking and advocating for broader implementation of the PZBP to improve healthcare access and quality of life for post-CABG patients.

This retrospective cross-sectional study explores the outcomes of post-coronary artery bypass grafting (CABG) patients enrolled under the PZBP. The study, conducted at Perpetual Succour Hospital in Cebu City from December 2018 to September 2023, included patients diagnosed with CAD based on ACC/AHA 2021 guidelines. Using a complete enumeration strategy, the research employed descriptive statistics for demographic and clinical profiling, and measured quality of life using the World Health Organization Quality of Life Brief Version (WHOQOL-BREF) tool.

The study analyzed 29 patients who underwent CABG through the PZBP, presenting detailed demographic and clinical data. Most patients (41.4%) fell within the 51-60 age range, with males comprising 75.9%. Diabetes mellitus type II was prevalent (58.6%) and 86.2% had three-vessel diseases. The quality of life assessed with WHOQOL-BREF yielded positive scores across physical, mental, social and environmental domains, indicating favorable outcomes. Notably, no cardiovascular deaths, all-cause deaths, or repeat revascularizations were reported during the study, highlighting the effectiveness of CABG under PZBP.

The study on post-CABG patients under the PZBP shows promising outcomes with no all-cause death, cardiovascular death, or repeat revascularization. This indicates the program's effectiveness in delivering accessible, high-quality healthcare, enhancing long-term survival rates and overall well-being. However, addressing underutilization is crucial, highlighting the importance of raising awareness and utilization to further improve post-CABG patients' outcomes and quality of life.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD), now known as Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD), is linked to cardiovascular disease. This renaming emphasizes the role of metabolic problems. Coronary artery calcification (CAC) reflects early coronary artery disease, but data on the MAFLD-CAC link is limited. OBJECTIVE: To demonstrate the association between metabolic dysfunction-associated fatty liver disease (MAFLD) based on its criteria and coronary artery calcification, as measured by CT CAC score. METHODS: This single-center retrospective study involved adult Filipino patients who underwent CT CAC scoring between January 2021 and January 2023. Clinical and laboratory data were obtained via review of electronic records. RESULTS: This study involved 147 patients with an average age of 62 years, primarily females (57.14%), and mostly falling into the Obese-Class I category (31.29%). The most common comorbidities were hypertension (95.24%), dyslipidemia (62.59%), and diabetes mellitus (38.1%). In terms of CAC scores using the CT Agatston method, majority (30.61%) had low calcium buildup (Stage 2 with scores between 1-99). Approximately 26.53% had higher liver fat content with liver HU below 40, while 73.47% had lower liver fat content with HU equal to or greater than 40. Furthermore, 25.17% of patients with fatty livers and other risk factors were diagnosed with MAFLD, while 74.83% were not. The p-value indicated a significant difference in proportions, suggesting a lower proportion of MAFLD among those who had undergone CT CAC scoring. However, the Pearson Chi-Square statistic (4.051) and the p-value (0.256) indicated no statistically significant association between MAFLD and CT CAC. CONCLUSION The study found a notably lower proportion of MAFLD diagnoses in patients who underwent CT CAC scoring. Additionally, there was no statistically significant link between MAFLD and CT CAC.
Cardiovascular Diseases ; Coronary Artery Disease ; Fatty Liver, Alcoholic

Background: Worldwide, coronary artery disease (CAD) is a leading cause of mortality and morbidity and remains to be a top health priority in many countries. A non-invasive imaging modality for diagnosis of CAD such as single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI) is usually requested by cardiologists as it displays radiotracer distribution in the heart reflecting myocardial perfusion. The interpretation of SPECT-MPI is done visually by a nuclear medicine physician and is largely dependent on his clinical experience and showing significant inter-observer variability. Objective: The aim of the study is to apply a deep learning approach in the classification of SPECT-MPI for perfusion abnormalities using convolutional neural networks (CNN). Methods: A publicly available anonymized SPECT-MPI from a machine learning repository (https://www.kaggle.com/ selcankaplan/spect-mpi) was used in this study involving 192 patients who underwent stress-test-rest Tc99m MPI. An exploratory approach of CNN hyperparameter selection to search for optimum neural network model was utilized with particular focus on various dropouts (0.2, 0.5, 0.7), batch sizes (8, 16, 32, 64), and number of dense nodes (32, 64, 128, 256). The base CNN model was also compared with the commonly used pre-trained CNNs in medical images such as VGG16, InceptionV3, DenseNet121 and ResNet50. All simulations experiments were performed in Kaggle using TensorFlow 2.6.0., Keras 2.6.0, and Python language 3.7.10. Results: The best performing base CNN model with parameters consisting of 0.7 dropout, batch size 8, and 32 dense nodes generated the highest normalized Matthews Correlation Coefficient at 0.909 and obtained 93.75% accuracy, 96.00% sensitivity, 96.00% precision, and 96.00% F1-score. It also obtained higher classification performance as compared to the pre-trained architectures. Conclusions The results suggest that deep learning approaches through the use of CNN models can be deployed by nuclear medicine physicians in their clinical practice to further augment their decision skills in the interpretation of SPECT-MPI tests. These CNN models can also be used as a dependable and valid second opinion that can aid physicians as a decision-support tool as well as serve as teaching or learning materials for the less-experienced physicians particularly those still in their training career. These highlights the clinical utility of deep learning approaches through CNN models in the practice of nuclear cardiology.
Coronary Artery Disease ; Deep Learning

BACKGROUND: Cardiac Amyloidosis is a disorder of protein misfolding and metabolism in which insoluble fibrils are deposited in the myocardial extracellular matrix causing organ dysfunction and eventually death. It can exhibit cardiac signs and symptoms, or it can be identified through screening in patients who exhibit extracardiac symptoms of amyloidosis. As there were no clear clinical signs of cardiac amyloidosis and a biopsy is required to show amyloid deposition, the condition has been historically challenging to diagnose. Thus, a high index of suspicion based on the clinical presentation and the outcomes of the preliminary testing arecrucial to determine the approach to diagnosis. CASE SUMMARY: We outline a case of 75-year-old Filipino male who was admitted due to progressive exertional dyspnea. Cardiac Amyloidosis was considered due to evaluation findings of heart failure with preserved ejection fraction with restrictive type of cardiomyopathy. This was subsequently confirmed through extracardiac fat pad biopsy, echocardiographic strain analysis and Technetium (99mTc) Pyrophosphate (PYP) single photon emission computed tomography scan (SPECT). CONCLUSION This case report discussed the red flags of clinical manifestations of cardiac amyloidosis and highlighted the use of non-invasive diagnostic modalities to diagnose the disease. Cardiac amyloidosis remains a rare entity and with emerging therapies that have the potential to improve patient outcomes, early diagnosis is really important. Having high index of suspicion based on signs and symptoms can lead to early detection and an increased number of patients being referred for treatment.
Human ; Male ; Aged: 65-79 yrs old ; Coronary Artery Disease

Humans ; Coronary Artery Disease/surgery* ; Percutaneous Coronary Intervention ; Heart ; Treatment Outcome ; Coronary Angiography

Humans ; Retrospective Studies ; Cardiopulmonary Bypass/adverse effects* ; Coronary Artery Bypass/adverse effects* ; Coronary Artery Disease/surgery* ; Acute Kidney Injury/etiology* ; Postoperative Complications ; Risk Factors ; Treatment Outcome

BACKGROUND: We have recently developed a new Coronary Artery Tree description and Lesion EvaluaTion (CatLet) angiographic scoring system. Our preliminary studies have demonstrated its superiority over the the Synergy between percutaneous coronary intervention (PCI) with Taxus and Cardiac Surgery (SYNTAX) score with respect to outcome predictions for acute myocardial infarction (AMI) patients. The current study hypothesized that the residual CatLet (rCatLet) score predicts clinical outcomes for AMI patients and that a combination with the three clinical variables (CVs)-age, creatinine, and ejection fraction, will enhance its predicting values. METHODS: The rCatLet score was calculated retrospectively in 308 consecutively enrolled patients with AMI. Primary endpoint, major adverse cardiac or cerebrovascular events (MACCE) including all-cause mortality, non-fatal AMI, transient ischemic attack/stroke, and ischemia-driven repeat revascularization, was stratified according to rCatLet score tertiles: rCatLet_low ≤3, rCatLet_mid 4-11, and rCatLet_top ≥12, respectively. Cross-validation confirmed a reasonably good agreement between the observed and predicted risks. RESULTS: Of 308 patients analyzed, the rates of MACCE, all-cause death, and cardiac death were 20.8%, 18.2%, and 15.3%, respectively. Kaplan-Meier curves for all endpoints showed increasing outcome events with the increasing tertiles of the rCatLet score, with P values <0.001 on trend test. For MACCE, all-cause death, and cardiac death, the area under the curves (AUCs) of the rCatLet score were 0.70 (95% confidence intervals [CI]: 0.63-0.78), 0.69 (95% CI: 0.61-0.77), and 0.71 (95% CI: 0.63-0.79), respectively; the AUCs of the CVs-adjusted rCatLet score models were 0.83 (95% CI: 0.78-0.89), 0.87 (95% CI: 0.82-0.92), and 0.89 (95% CI: 0.84-0.94), respectively. The performance of CVs-adjusted rCatLet score was significantly better than the stand-alone rCatLet score in terms of outcome predictions. CONCLUSION: The rCatLet score has a predicting value for clinical outcomes for AMI patients and the incorporation of the three CVs into the rCatLet score will enhance its predicting ability. TRIAL REGISTRATION http://www.chictr.org.cn , ChiCTR-POC-17013536.
Humans ; Coronary Artery Disease/complications* ; Death ; Myocardial Infarction/etiology* ; Percutaneous Coronary Intervention ; Retrospective Studies ; Risk Assessment ; Risk Factors ; Treatment Outcome

Humans ; Intra-Abdominal Fat/diagnostic imaging* ; Coronary Artery Disease ; Body Mass Index

BACKGROUND: Several studies have reported that polygenic risk scores (PRSs) can enhance risk prediction of coronary artery disease (CAD) in European populations. However, research on this topic is far from sufficient in non-European countries, including China. We aimed to evaluate the potential of PRS for predicting CAD for primary prevention in the Chinese population. METHODS: Participants with genome-wide genotypic data from the China Kadoorie Biobank were divided into training ( n = 28,490) and testing sets ( n = 72,150). Ten previously developed PRSs were evaluated, and new ones were developed using clumping and thresholding or LDpred method. The PRS showing the strongest association with CAD in the training set was selected to further evaluate its effects on improving the traditional CAD risk-prediction model in the testing set. Genetic risk was computed by summing the product of the weights and allele dosages across genome-wide single-nucleotide polymorphisms. Prediction of the 10-year first CAD events was assessed using hazard ratios (HRs) and measures of model discrimination, calibration, and net reclassification improvement (NRI). Hard CAD (nonfatal I21-I23 and fatal I20-I25) and soft CAD (all fatal or nonfatal I20-I25) were analyzed separately. RESULTS: In the testing set, 1214 hard and 7201 soft CAD cases were documented during a mean follow-up of 11.2 years. The HR per standard deviation of the optimal PRS was 1.26 (95% CI:1.19-1.33) for hard CAD. Based on a traditional CAD risk prediction model containing only non-laboratory-based information, the addition of PRS for hard CAD increased Harrell's C index by 0.001 (-0.001 to 0.003) in women and 0.003 (0.001 to 0.005) in men. Among the different high-risk thresholds ranging from 1% to 10%, the highest categorical NRI was 3.2% (95% CI: 0.4-6.0%) at a high-risk threshold of 10.0% in women. The association of the PRS with soft CAD was much weaker than with hard CAD, leading to minimal or no improvement in the soft CAD model. CONCLUSIONS In this Chinese population sample, the current PRSs minimally changed risk discrimination and offered little improvement in risk stratification for soft CAD. Therefore, this may not be suitable for promoting genetic screening in the general Chinese population to improve CAD risk prediction.
Male ; Humans ; Female ; Coronary Artery Disease/genetics* ; Biological Specimen Banks ; East Asian People ; Risk Assessment/methods* ; Genetic Predisposition to Disease/genetics* ; Risk Factors ; Genome-Wide Association Study