Detection Of Coronary Artery Disease Research Articles

Enhancing Coronary Artery Disease Detection with a Hybrid Machine Learning Approach: Integrating K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) Algorithms

Recent studies have identified coronary artery disease (CAD) as a leading cause of death globally. Early detection of CAD is crucial for reducing mortality rates. However, accurately predicting CAD poses challenges, particularly in treating patients effectively before a heart attack occurs due to the complexity of data and relationships in traditional methodologies. This research has successfully developed a machine learning model for CAD prediction by combining K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) Classifier techniques. The model, trained and tested on a dataset of 918 samples (508 with cardiac issues and 410 healthy cases), achieved an accuracy of 82% for KNN, 84.3% for SVM, and 88.7% for the hybrid model after rigorous training and testing.

Value of Dynamic Computed Tomography Myocardial Perfusion in CAD: A Systematic Review and Meta-analysis.

Dynamic stress computed tomography (CT) perfusion is a non-invasive method for quantifying myocardial ischemia by assessing myocardial blood flow (MBF). In this meta-analysis, we evaluated the diagnostic accuracy of dynamic CT perfusion for the detection of significant coronary artery disease (CAD) across various CT scanners, obese patients, and its prognostic value. We systematically searched PubMed, Embase, Web of Science, and Cochrane library for published studies evaluating the accuracy of CT myocardial perfusion in diagnosing functional significant ischemia by invasive fractional flow reserve. The diagnostic performance of dynamic CT perfusion in detecting ischemia was evaluated using a summary receiver operating characteristic (sROC) curve. A total of 23 studies underwent meta- analysis. In myocardial region without ischemia, MBF was measured at 1.44 ml/min/g (95% confidence interval [CI]: 1.13-1.75), while in region with ischemia, it was 0.94 ml/min/g (95% CI: 0.80-1.08) (p<0.001). On the patient-based analysis, the area under the sROC curve of CT-MBF was 0.93, with a sensitivity of 0.84 and specificity of 0.88. Differences in CT type (dual source vs. single source), and body mass index (BMI) did not significantly affect the diagnostic performance. The pooled hazard ratio of dynamic CT perfusion for predicting adverse events was 4.98 (95%CI: 2.08-11.93, p=<0.001, I2=61%, p for heterogeneity = 0.07). Dynamic CT perfusion has high diagnostic performance in the quantitative assessment of ischemia and detection of functional myocardial ischemia as defined by invasive FFR, and may be useful in risk stratification of CAD patients.

The Role of the Anaesthesiologist in Liver Transplantation - Preoperative Evaluation

Preoperative evaluation prior to listing for orthotopic liver transplantation (LT) requires a careful multidisciplinary approach with specialized teams including surgeons, hepatologists and anesthesiologists in order to improve short- and long-term clinical outcomes. Due to inadequate supply of donor organs and changing demographics, patients listed for LT have become older, sicker and share more comorbidities. As cardiovascular events are the leading cause for early mortality precise evaluation of risk factors is mandatory. This review focuses on the detection and management of coronary artery disease, cirrhotic cardiomyopathy, portopulmonary hypertension and hepatopulmonary syndrome in patients awaiting LT. Further insights are being given into scoring systems, patients with Acute-on-chronic-liver-failure (ACLF), frailty, NASH cirrhosis and into psychologic evaluation of patients with substance abuse.

Study of Correlation of TMT Results with Angiogram Findings in Coronary Artery Disease Predictability in Patients with Angina Equivalents in DAE Hospital

Introduction Coronary artery disease is leading cause of mortality and morbidity worldwide. The early detection and treatment of CAD is vital to prevent complications with non invasive tests like stress tests Exercise treadmill testing (ETT) is a wellvalidated, inexpensive test that can be used for the evaluation of patients with suspected coronary artery diseases (CAD). In addition to identifying patients who have exercise-induced ECG changes suggestive of ischemia, ETT also provides important data on exercise capacity, blood pressure, and heart rate response to exercise, symptoms, and the presence of exercise-induced arrhythmias. In a person at high risk for coronary heart disease (e.g., advanced age, multiple coronary risk factors), an abnormal TMT is very predictive of the presence of coronary heart disease (&gt; 90% accurate). In the present study ,we studied the patients with changing risk factor profiles along with increasing incidence of CAD in younger population (even as young as 30-35 years), in females versus males by TMT and also the correlation of TMT results with angiogram findings in the predictability of CAD

Deep learning-based detection of coronary artery disease from electrocardiogram (ECG) images

Deep learning-based detection of coronary artery disease from electrocardiogram (ECG) images

Hybrid classification of XGBoost-based ADAM optimization for coronary artery disease diagnosis

A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19%. This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy.

Combining anatomical and biochemical markers in the detection and risk stratification of coronary artery disease.

We aimed to test the hypothesis if combining coronary artery calcium score (Ca-score) as a quantitative anatomical marker of coronary atherosclerosis with high-sensitive cardiac troponin as a quantitative biochemical marker of myocardial injury provided incremental value in the detection of functional relevant CAD (fCAD) and risk stratification. Consecutive patients undergoing myocardial perfusion SPECT (MPS) without prior CAD were enrolled. The diagnosis of fCAD was based on the presence of ischemia on MPS and coronary angiography- fCAD was centrally adjudicated in the diagnostic and prognostic domain. Diagnostic accuracy was evaluated using the area under receiver-operating characteristic curve. The composite of cardiovascular death and non-fatal acute myocardial infarction (AMI) within 730 days were the primary prognostic endpoints.Among 1715 patients eligible for the diagnostic analysis, 399 patients had fCAD. The combination of Ca-Score and hs-cTnT had good diagnostic accuracy for the diagnosis of fCAD, AUC 0.79 (95 % CI 0.77-0.81), but no incremental value compared to the Ca-score alone (AUC 0.79 (95%CI 0.77-0.81, p=0.965). Similar results were observed using hs-cTnI (AUC 0.80, 95%CI 0.77-0.82) instead of hs-cTnT.Among 1709 patients (99.7%) with available follow-up, 59 patients (3.5%) suffered the composite primary prognostic endpoint (nonfatal AMI n=34, CV death n=28).Both, Ca-score and hs-cTnT had independent prognostic value. Increased risk was restricted to patients with elevation in both markers. The combination of the Ca-score with hs-cTnT increases the prognostic accuracy for future events defining fCAD, but does not provide incremental value versus the Ca-Score alone for the diagnosis of fCAD.

Acute coronary syndrome patterns in the Young: risk factor profile and in-hospital outcomes in a tertiary referral hospital in Kenya

IntroductionAcute coronary syndrome (ACS) accounts for coronary artery disease (CAD) –related morbidity and mortality. There has been growing concern about the rising incidence of ACS among young individuals globally both in developed and developing countries, including Sub-Saharan Africa. This group’s phenotypic characteristics; risk factors and clinical outcomes are not well described. contextual and regional studies are necessary to understand the magnitude of ACS among young Individuals and help highlight challenges and opportunities for improved ACS outcomes in the region. The study aimed to describe the demographic and clinical characteristics of young individuals hospitalized with ACS and report on in-hospital outcomes.MethodologyThis single-center retrospective study was conducted at the Aga Khan University Hospital, Nairobi. Medical records of all young individuals hospitalized with ACS from 30th June 2020 to 1st May 2023 were reviewed. We defined young individuals as 50 years or below. Categorical variables were reported as frequencies and proportions, and compared with Pearson chi- square or Fisher’s exact tests. Continuous variables were reported as means or medians and compared with independent t-tests or Mann-Whitney U tests. P- value < 0.05 was considered statistically significant.ResultsAmong 506 patients hospitalized with ACS, (n = 138,27.2%) were aged 50 years and below. The study population was male (n = 107, 79.9%) and African(n = 82,61.2%) predominant with a median age of 46.5 years (IQR 41.0–50.0). Hypertension (n = 101,75.4%) was noted in most study participants. More than half of the cohort were smokers (n = 69,51.5%) having a family history of premature ASCVD(n = 70,52.2%) and were on lipid-lowering therapy(n = 68,50.7%) prior to presentation. ST-segment–elevation myocardial infarction (STEMI) was the most common clinical manifestation of ACS (n = 77, 57.5%). Of the significant coronary artery disease (n = 75,56.0%), the majority of the individuals had single vessel disease (n = 60, 80%) with a predilection of left anterior deciding artery(n = 47,62.6%). The Main cause of ACS was atherosclerosis (n = 41,54.6%). The mean left ventricular ejection fraction was 46.0 (± 12.4). The in-hospital mortality was (n = 2, 1.5%).ConclusionThis study highlights that young individuals contribute to a relatively large proportion of patients presenting with ACS at our center. The most common presentation was STEMI. The principal cause was atherosclerosis. The findings of this study highlight the importance of developing systems of care that enable the early detection of CAD. Traditional cardiovascular risk factors were prevalent and modifiable, thus targets of intervention.

Smartphone-Based Recognition of Heart Failure by Means of Microelectromechanical Sensors

BackgroundHeart failure (HF) is the leading cause of hospitalization in individuals over 65 years of age. Identifying noninvasive methods to detect HF may address the epidemic of HF. Seismocardiography which measures cardiac vibrations transmitted to the chest wall has recently emerged as a promising technology to detect HF. ObjectivesIn this multicenter study, the authors examined whether seismocardiography using commercially available smartphones can differentiate control subjects from patients with stage C HF. MethodsBoth inpatients and outpatients with HF were enrolled from Finland and the United States. Inpatients with HF were assessed within 2 days of admission, and outpatients were assessed in the ambulatory setting. In a prespecified pooled data analysis, algorithms were derived using logistic regression and then validated using a bootstrap aggregation method. ResultsA total of 217 participants with HF (174 inpatients and 172 outpatients) and 786 control subjects from cardiovascular clinics were enrolled. The mean age of participants with acute HF was 64 ± 13 years, 64.9% were male, left ventricular ejection fraction was 39 ± 15%, and median N-terminal pro–B-type natriuretic peptide was 5,778 ng/L (Q1-Q3: 1,933-6,703). The majority (74%) of participants with HF had reduced EF, and 38% had atrial fibrillation. Across both HF cohorts, the algorithms had an area under the receiver operating characteristic curve of 0.95 with a sensitivity of 85%, specificity of 90%, and accuracy of 89% for the detection of HF, with a decision threshold of 0.5. The positive and negative likelihood ratios were 8.50 and 0.17, respectively. The accuracy of the algorithms was not significantly different in subgroups based on age, sex, body mass index, and atrial fibrillation. ConclusionsSmartphone-based assessment of cardiac function using seismocardiography is feasible and differentiates patients with HF from control subjects with high diagnostic accuracy. (Recognition of Heart Failure With Micro Electro-mechanical Sensors FI [NCT04444583]; Recognition of Heart Failure With Micro Electro-mechanical Sensors [NCT04378179]; Detection of Coronary Artery Disease With Micro Electro-mechanical Sensors [NCT04290091])

High-sensitivity cardiac troponin T in detecting obstructive coronary artery disease in hemodialysis patients listed for kidney transplantation.

Cardiovascular diseases are the leading cause of morbidity and mortality in patients with end-stage renal disease. This study aimed to assess the prognostic value of high-sensitivity cardiac troponin T (hs-cTnT) in identifying patients with obstructive coronary artery disease (CAD) among patients on hemodialysis listed for kidney transplantation. The study prospectively enrolled consecutive adult hemodialysis patients listed for kidney transplantation. They underwent laboratory tests and a standardized set of imaging and functional tests, including coronary angiography, according to patient characteristics. The study included 100 consecutive patients (72 men)at a median age of 56.5 years. Ultimately, 48% of the patients were diagnosed with obstructive CAD. Age and plasma hs-cTnT levels predicted the diagnosis of obstructive CAD (OR, 1.13; 95% CI, 1.08-1.20; P < 0.001 and OR, 1.03; 95% CI, 1.01-1.05; P = 0.001, respectively). The calculated cut-off value for age was 53 years, which showed sensitivity of 87.5% and specificity of 76.9% for obstructive CAD diagnosis. The calculated value for hs-cTnT was 0.067 ng/ml, which showed sensitivity of 61.4% and specificity of 82.2% for the detection of obstructive CAD. In patients aged >52 years, 79.2% were diagnosed with obstructive CAD. However, in the group of patients ≤52 years and with hs-cTnT >0.069 ng/ml, the incidence of obstructive CAD was significantly higher than in the group with hs-cTnT level ≤0.069 ng/ml. Baseline hs-cTnT level is a useful prognostic biomarker in the diagnosis of obstructive CAD in hemodialysis patients listed for kidney transplantation.

The relationship between Choroidal Vascular Index and non-invasive ultrasonographic atherosclerosis predictors

BackgroundThis study explores the intricate connections between choroidal vascular index (CVI) and non-invasive ultrasonographic atherosclerosis predictors, shedding light on the potential links between ocular vascular dynamics and systemic cardiovascular health. MethodsWe conducted a cross-sectional analysis of 81 participants, assessing CVI, intima-media thickness (IMT), extra-media thickness (EMT), and the PATIMA index. The presence of coronary artery disease (CAD) was also evaluated. Statistical methods included descriptive statistics, t-tests for group comparisons, Spearman correlation analysis, and receiver operating characteristic (ROC) curve analysis. ResultsOur findings revealed that patients with CAD had lower CVI values compared to those without CAD, underscoring a potential association between CVI and CAD. Significant negative correlations were observed between CVI and IMT, EMT, PATIMA, and CAD. ROC curve analysis identified optimal CVI cutoff values for hypertension and CAD detection, showcasing its potential as a diagnostic marker. DiscussionOur results align with existing literature on ocular vascular changes, supporting the notion that CVI may be a promising indicator of systemic vascular conditions. The study contributes to the broader understanding of the relationships between ocular and cardiovascular health, providing a foundation for future research and clinical applications. ConclusionThe study suggests that CVI holds clinical relevance as a non-invasive marker for identifying systemic conditions, offering insights into the fields of neurology, physical therapy, and rehabilitation. Addressing its limitations, this research encourages further investigation into the multifaceted connections between CVI and atherosclerosis predictors.

Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data.

The 2017 World Health Organization Fact Sheet highlights that coronary artery disease is the leading cause of death globally, responsible for approximately 30% of all deaths. In this context, machine learning (ML) technology is crucial in identifying coronary artery disease, thereby saving lives. ML algorithms can potentially analyze complex patterns and correlations within medical data, enabling early detection and accurate diagnosis of CAD. By leveraging ML technology, healthcare professionals can make informed decisions and implement timely interventions, ultimately leading to improved outcomes and potentially reducing the mortality rate associated with coronary artery disease. Machine learning algorithms create non-invasive, quick, accurate, and economical diagnoses. As a result, machine learning algorithms can be employed to supplement existing approaches or as a forerunner to them. This study shows how to use the CNN classifier and RNN based on the LSTM classifier in deep learning to attain targeted "risk" CAD categorization utilizing an evolving set of 450 cytokine biomarkers that could be used as suggestive solid predictive variables for treatment. The two used classifiers are based on these "45" different cytokine prediction characteristics. The best Area Under the Receiver Operating Characteristic curve (AUROC) score achieved is (0.98) for a confidence interval (CI) of 95; the classifier RNN-LSTM used "450" cytokine biomarkers had a great (AUROC) score of 0.99 with a confidence interval of 0.95 the percentage 95, the CNN model containing cytokines received the second best AUROC score (0.92). The RNN-LSTM classifier considerably beats the CNN classifier regarding AUROC scores, as evidenced by a p-value smaller than 7.48 obtained via an independent t-test. As large-scale initiatives to achieve early, rapid, reliable, inexpensive, and accessible individual identification of CAD risk gain traction, robust machine learning algorithms can now augment older methods such as angiography. Incorporating 65 new sensitive cytokine biomarkers can increase early detection even more. Investigating the novel involvement of cytokines in CAD could lead to better risk detection, disease mechanism discovery, and new therapy options.

Impact of cardiac size on SPECT myocardial perfusion imaging performance: Insights from the REFINE-SPECT registry.

Variation in diagnostic performance of SPECT myocardial perfusion imaging (MPI) has been observed, yet the impact of cardiac size has not been well characterized. We assessed whether low left ventricular volume influences SPECT MPI's ability to detect obstructive coronary artery disease (CAD), and its interaction with age and sex. A total of 2,066 patients without known CAD (67% male, 64.7 ± 11.2 years) across 9 institutions underwent SPECT MPI with solid-state scanners followed by coronary angiography as part of the REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT. Area under receiver-operating characteristic curve (AUC) analyses evaluated performance of quantitative and visual assessments according to cardiac size (end- diastolic volume [EDV]; < 20th vs. ≥ 20th population or sex-specific percentiles), age (<75 vs. ≥ 75 years), and sex. Significantly decreased performance was observed in patients with low EDV compared to those without (AUC: population 0.72 vs. 0.78, p = 0.03; sex-specific 0.72 vs. 0.79, p = 0.01) and elderly patients compared to younger patients (AUC 0.72 vs. 0.78, p = 0.03), whereas males and females demonstrated similar AUC (0.77 vs. 0.76, p = 0.67). The reduction in accuracy attributed to lower volumes was primarily observed in males (sex-specific threshold: EDV 0.69 vs. 0.79, p = 0.01). Accordingly, a significant decrease in AUC, sensitivity, specificity, and negative predictive value for quantitative and visual assessments was noted in patients with at least two characteristics of low EDV, elderly age, or male sex. Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients.

Detection of Coronary Artery Disease Based on Clinical Phonocardiogram and Multiscale Attention Convolutional Compression Network.

Heart sound is an important physiological signal that contains rich pathological information related with coronary stenosis. Thus, some machine learning methods are developed to detect coronary artery disease (CAD) based on phonocardiogram (PCG). However, current methods lack sufficient clinical dataset and fail to achieve efficient feature utilization. Besides, the methods require complex processing steps including empirical feature extraction and classifier design. To achieve efficient CAD detection, we propose the multiscale attention convolutional compression network (MACCN) based on clinical PCG dataset. Firstly, PCG dataset including 102 CAD subjects and 82 non-CAD subjects was established. Then, a multiscale convolution structure was developed to catch comprehensive heart sound features and a channel attention module was developed to enhance key features in multiscale attention convolutional block (MACB). Finally, a separate downsampling block was proposed to reduce feature losses. MACCN combining the blocks can automatically extract features without empirical and manual feature selection. It obtains good classification results with accuracy 93.43%, sensitivity 93.44%, precision 93.48%, and F1 score 93.42%. The study implies that MACCN performs effective PCG feature mining aiming for CAD detection. Further, it integrates feature extraction and classification and provides a simplified PCG processing case.

The combination of high-frequency QRS and ST-segment alterations during exercise stress tests enhanced the diagnostic efficacy for coronary artery disease.

High-frequency QRS (HF-QRS) manifests as a novel adjunct electrocardiographic marker with potential utility in coronary artery disease (CAD) detection. We hypothesize that HF-QRS analysis may be superior to conventional ST-segment analysis in detecting CAD, and the combination of these two analyses in the exercise stress test may enhance the diagnostic efficacy for CAD. The study incorporated a sample of 157 patients (mean age 62 9 years) referred for nonemergent angiography. Before angiography, patients underwent exercise stress testing utilizing an upright bicycle. High-resolution electrocardiogram (ECG) data were collected during the exercise test, facilitating both HF-QRS and conventional ST-segment analyses. The diagnostic efficacy of HF-QRS and ST-segment analysis were compared, utilizing angiographic outcomes as the gold standard. The study design integrated HF-QRS analysis and ST-segment analysis via sequential and concurrent testing protocols. In terms of CAD detection, HF-QRS analysis displayed superior sensitivity compared to conventional ST-segment analysis (63% vs. 37%, p = .002). The serial test significantly increased specificity from 79% to 97% (p = .002) compared to ST-deviation analysis alone. It showed a markedly low sensitivity of 26%. The parallel test significantly increased sensitivity from 37% to 77% (p < .001), while retaining a moderate level of specificity of 51%. The quantity of ECG leads exhibiting a positive HF-QRS response demonstrated a correlation with the severity of CAD (p < .001). HF-QRS analysis exhibited superior sensitivity in detecting angiographically confirmed CAD relative to conventional ST-segment analysis. Moreover, the combination of HF-QRS and ST-segment alterations during exercise stress test enhanced the diagnostic efficacy for CAD.

Predicting asymptomatic coronary artery disease in first-ever acute ischemic stroke patients: a cross-sectional study.

It is frequently observed that patients with first-ever acute ischemic stroke (AIS) have a common occurrence of asymptomatic coronary artery disease (CAD). This condition is associated with a poor prognosis. Early detection and recognition of asymptomatic CAD in first-ever AIS patients may optimize the clinical management and ultimately lead to improved outcomes. The aim of this study was to investigate the role of aortic arch plaque (AAP) detected through combined computed tomography angiography (CTA) as an early predictor of asymptomatic CAD in patients with first-ever AIS without prior diagnosis of CAD. A cross-sectional study was conducted at Xuanwu Hospital, Capital Medical University from January 2019 to December 2021, involving patients with first-ever AIS caused by large arterial atherosclerosis. Patients with a history of recognized cardiovascular disease, nonatherosclerotic arterial stenosis, atrial fibrillation related to cardioembolism, and complete carotid occlusions were excluded. The study utilized a combined coronary and cervicocephalic CTA to evaluate atherosclerosis in the cervicocephalic, aortic, and coronary arteries simultaneously. First-ever AIS patients without prior diagnosis of CAD were divided into 2 groups: 1 with asymptomatic CAD detected through CTA and the other without. Multivariate logistic regression was used to identify independent risk factors associated with the presence of asymptomatic CAD, including aortic arch, cervical and intracranial atherosclerotic characteristics (e.g., vascular stenosis, plaque thickness, extent, and complexity). Among 182 AIS patients, 84 (46.2%) had asymptomatic CAD. Increased aortic arch plaque (AAP) thickness per millimeter [adjusted odds ratio (aOR): 1.26; 95% confidence interval (CI): 1.08-1.47], presence of severe AAP (aOR: 4.24; 95% CI: 1.59-12.03), mixed AAP (aOR: 2.65; 95% CI: 1.09-6.62), and ulcerative AAP (aOR: 11.76; 95% CI: 2.05-222.84) raised the risk of asymptomatic CAD in stroke patients, independent of other factors. The combination of ulcerative AAP, diabetes mellitus, and smoking could predict asymptomatic CAD with an area under the receiver operating characteristic curve (AUC) of 0.71 (95% CI: 0.64-0.79; P<0.001). Ulcerative AAP was found to be more valuable than cervicocephalic atherosclerotic characteristics for predicting asymptomatic CAD in first-ever AIS patients. The presence of ulcerative AAP was associated with asymptomatic CAD in first-ever AIS patients. As an early systemic atherosclerosis feature, ulcerative AAP is probably a more valuable indicator than cervicocephalic atherosclerotic characteristics for predicting asymptomatic CAD in AIS patients.

Does the prevalence of subclinical coronary atherosclerosis increase in primary hyperparathyroidism; coronary flow reserve and plasma aterogenic index in patients with primary hyperparathyroidism?

Aims: The coronary flow reserve (CFR) is a sign of early-stage coronary artery disease (CAD). Plasma atherogenic index (PAI) is related to atherosclerosis and cardiovascular mortality. Therefore, our aim was to determine CFR and PAI in patients with primary hyperparathyroidism (PHPT) and investigate whether PAI can be used in the detection of early-stage CAD.&#x0D; Methods: The sample was comprised of 44 patients with PTHT and 33 healthy volunteers. We defined CFR as the ratio of the hyperemic diastolic peak velocity to the baseline diastolic peak velocity. PAI values were calculated with the formula of log 10 triglyceride (TRG)/high-density lipoprotein (HDL).&#x0D; Results: The comparison of the groups for PAI and CFR demonstrated that PAI levels were significantly higher while CFR levels were significantly lower in the PTHT patients (p

Enhancing pre-transplant cardiac assessment: Validation and utility of the CAD-LT score with CCTA in liver transplant candidates

BackgroundCoronary computed tomography angiography (CCTA) is increasingly being used in the preoperative workup for liver transplantation (LT). We sought to assess the utility of integrating CCTA with the novel CAD-LT (Coronary Artery Disease in Liver Transplantation) score and its impact on reducing the need for invasive coronary angiography prior to LT. MethodsWe conducted a retrospective cohort study of consecutive patients (age ≥ 18 years) who underwent CCTA for LT workup between 2011 and 2018 at the Victorian Liver Transplant Unit, Melbourne, Australia. CAD-LT scores, a traditional risk factor-based criteria, were calculated, and patients stratified as low-, intermediate- or high-risk. ResultsOverall, 229 patients underwent CCTA. The mean age was 66 ± 5 years (82% male) with a modest-to-high risk factor burden (diabetes, 53%; hypertension, 46%; current or former smoker, 62%). The mean CAD-LT score of our cohort was 12.4 ± 4.0. No patients were classified as low-risk, 49 patients (21.4%) were deemed intermediate-risk and 180 patients (78.6%) were deemed high-risk. A high CAD-LT score (≥ 9) showed high sensitivity (95.3% [95% CI 86–98%]) and modest specificity (27.8% [95% CI 21–35%]) for the detection of obstructive coronary artery disease on CCTA, with a negative predictive value of 94%. Following multidisciplinary discussions, only 41 patients (18%) of patients proceeded to ICA of which 27% received percutaneous coronary intervention. ConclusionsThe use of CCTA in patients deemed intermediate- to high-risk by the CAD-LT score has the potential to reduce the need for invasive coronary angiography in patients undergoing LT workup.

Partnering Research and Clinical Practice to Implement a Photon-Counting Detector CT Myocardial Perfusion Imaging Study

Implementation of research studies in clinical practice may encounter institutional, cultural, or resource barriers. Lack of nursing representation during the development of research protocols and decision-making, as well as financial constraints and competing priorities can impact translating research evidence into practice. At a large Midwestern teaching hospital, these barriers are addressed using professional nursing practice and a team-based approach to patient care and innovation, which in turn improves health outcomes for patients. The institution's commitment to nursing research in the radiology department aids in the implementation of innovative imaging technologies with the potential for improved detection and diagnosis of coronary artery disease. This article will outline radiology nursing involvement in the development of a dual-purpose research and clinical exam utilizing advanced computed tomography scanner technology. This article will also show how the institution works through common barriers to nursing research and how its efforts aid in the translation of evidence-based research into clinical practice.

Feasibility of myocardial blood flow quantification to detect flow-limited coronary artery disease with a one-day rest/stress continuous rapid imaging protocol on cardiac-dedicated cadmium zinc telluride single photon emission computed tomography

BackgroundIt is clinically needed to explore a more efficient imaging protocol for single photon emission computed tomography (SPECT) myocardial blood flow (MBF) quantitation derived from cadmium zinc telluride (CZT) SPECT camera for the routine clinical utilization. MethodsOne hundred and twenty patients with matched clinical characteristics and angiographic findings who completed one-day rest/stress SPECT imaging with either the intermittently sequential imaging (ISI) protocol (two dynamic and two electrocardiography (ECG)-gated scans) or the continuous rapid imaging (CRI) protocol (two dynamic/ECG-gated scans) were included. MBF quantitation adopted residual activity correction (RAC) to correct for rest residual activity (RRA) in the stress dynamic SPECT scan for the detection of flow-limited coronary artery disease. ResultsThe CRI protocol reduced about 6.2 times shorter than the ISI protocol (25.5 min vs 157.6 min), but slightly higher than the RRA (26.7% ± 3.6% vs 22.3% ± 4.9%). With RAC, both protocols demonstrated close stress MBF (2.18 ± 1.13 vs 2.05 ± 1.10, P > 0.05) and myocardial flow reserve (MFR) (2.42 ± 1.05 vs 2.48 ± 1.11, P > 0.05) to deliver comparable diagnostic performance (sensitivity = 82.1%–92.3%, specificity = 81.2%–91.2%). Myocardial perfusion and left ventricular function overall showed no significant difference (all P > 0.26). ConclusionOne-day rest/stress SPECT with the CRI protocol and rest RAC is feasible to warrant the diagnostic performance of MBF quantitation with a shortened examination time and enhanced patient comfort. Further evaluation on the impact of extracardiac activity to regional MBF and perfusion pattern is required. Additional evaluation is needed in a patient population that is typical of those referred for SPECT MPI, including those with known or suspected coronary microvascular disease.