ASSESSMENT OF LEFT ATRIAL FUNCTION IN PATIENTS WITHMITRAL VALVE DISEASES

ABSTRACTObjective:To observe and assess left atrial (LA) function by observing the differences in conventional Doppler echo parameters of left ventricular inflow, Left atrial appendage, among patients with mitral valve disease.Methods: Forty three mitral valve disease subjects appearing consecutively for echocardiogram (ECHO) test at the cardiology department in a tertiary care hospital were recruited into the study as per the pre-set inclusion and exclusion criteria. The data from the ECHO was pooled using Microsoft excel and analyzed using SPSS software by application of appropriate statistical tests.Results:Of the 43 objects, 39 had MS, 3 had MR and 2 of them were found to have both MS and MR. The major presenting symptom as observed in 33 subjects, was dyspnea. LA maximum volume was found to be 91±59 ml and minimum was 66±51 ml. Left atrial expansion index was 128±91. Left atrial active emptying fraction was 29±13 and passive emptying fraction was 31±15. No significant change in LA global strain among groups with MR and without MR was observed. Further, no significant difference was observed in left atrial indices like left atrial emptying fraction, left atrial passive emptying fraction, atrial fraction, Left atrial expansion index among   groups having MR and no MR.Conclusion:LA contractile, reservoir and conduit function was significantly reduced in mitral valve diseases due to increased hemodynamic load. No significant difference was noted in global LA strain irrespective of MS or MR.Key Words: Valvular disease; Left atria; Strain; Contractile functio

Lipid Profile Parameters and Coronary Artery Disease in Young Patients Undergoing Diagnostic Angiography

Introduction: It is vital to understand the association between lipid profile and the severity of coronary artery disease (CAD) in young patients with suspected CAD. The clinical presentation, lipid profile and severity of CAD may differ in patients who develop CAD at young age and those at older age. Friesinger (FR) index is an important tool to assess the extent and severity of coronary artery lesions.Methods: This study was a single center retrospective study involving patients below 40 years who underwent diagnostic coronary angiography. Demographic variables, lipid profile and FR index were estimated. Patients were divided into four groups based on the FR index scores of 0, 1–4, 5–10 and 11-15, respectively.Results: A total of 158 patients (Mean ± SD of age; 35.65 ± 3.81 years) were included in the study. Among demographic variables, gender (P = 0.03) and body mass index (BMI) (P < 0.001) were found to be associated with FR index. In addition, total cholesterol (P < 0.001), low density cholesterol (LDL) cholesterol (P < 0.001), non-high density cholesterol (non-HDL) (P < 0.001) and ratio of triglycerides (TG) /non-HDL cholesterol (P = 0.004) showed significant differences between the FR groups. Logistic regression analysis showed that only diabetes (P = 0.02) and BMI (P = 0.004) were significant predictors of the extent and severity of coronary artery lesions in terms of FR index.Conclusions: A strong direct relationship was observed between total cholesterol, LDL and non HDL cholesterol while a negative correlation with the TG/non HDL ratio. Diabetes and BMI also play a very significant role

USE OF TISSUE DOPPLER IMAGING TO DETECT RIGHT VENTRICULAR MYOCARDIAL DYSFUNCTION IN PATIENTS WITH CHRONIC OBSTRUCTIVE PULMONARY DISEASE

Objective: To determine the utility of tissue Doppler imaging (TDI) in detecting early right ventricle (RV) myocardial dysfunction, given its prognostic implications in patients with chronic obstructive pulmonary disease (COPD).Methods:  A prospective case-control study was carried out involving 36 COPD patients in an acute exacerbated state as cases and 34 healthy subjects serving as controls. Each subject underwent a baseline echocardiography using various methods ranging from m-mode and 2-dimensional measures for analyzing RV geometry to strain and strain rate using Tissue Doppler Imaging to measure RV deformation. The cases underwent a subsequent echocardiogram 1month later once the respiratory symptoms subsided.Results: A significant difference was observed in RV tissue annular velocities ( E', A', S) between cases and the controls at baseline. However no significant increase was observed in tissue annular velocities among cases during remission states from baseline. Peak systolic strain in COPD group was significantly reduced in comparison to controls, but not significantly increased during remission when compared to baseline in cases. FEV1/VC did not bear any significant correlation with RV strain. Tei index had a negative linear correlation with peak systolic strain of RV, which was statistically significant.Conclusion: TDI parameters revealed that RV dysfunction remains unaltered even in remission state of COPD, despite pulmonary arterial pressure normalizing. In light of it bearing a negative prognosis in COPD, RV dysfunction merits assessment in COPD patients, both in acute exacerbations as well as in remission. Â

Prevalence of Coronary Artery Disease and Its Risk Factors in Patients Undergoing Permanent Pacemaker Implantation

Introduction: Some pacemaker implanted patients have an atherosclerotic disease which promotes conduction system ischemia and conduction disturbances. The aim of this study was to determine prevalence of coronary artery disease (CAD) and its risk factors in patients undergoing permanent pacemaker implantation (PPI).Methods: This prospective, single-center hospital based study examined patients older than 40 years who required PPI. Presence of atherosclerotic risk factors and CAD was examined.Results: Of 258 patients undergoing PPI, CAD was present in 50 (19.37 %) patients. CAD was more common among middle age and elderly patients (P = 0.03). Patients older than or equal 76.5 years had specificity of 78.8% for an association with CAD. Multivariate analysis showed that age (odds ratio: 1.042; 95% confidence interval: 1.009–1.075; P = 0.01) and diabetes (odds ratio: 3.437; 95% confidence interval: 1.618–7.303; P = 0.001) had a statistically significant association with CAD. Of 169 patients with involvement of the atrioventricular (AV) node, 28 (16.6 %) had associated left anterior descending artery (LAD) involvement with P = 0.01, suggesting an association between LAD disease and chronic degenerative changes in the AV node.Conclusion: CAD was present in 19.4% of patients undergoing PPI. Age and diabetes had a strong association with CAD. LAD stenosis was significantly more prevalent in AV nodal/ infra-hisian disease compared with sinus nodal disease

Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques

Hypertrophic cardiomyopathy (HCM) is a genetic disorder that exhibits a wide spectrum of clinical presentations, including sudden death. Early diagnosis and intervention may avert the latter. Left ventricular hypertrophy on heart imaging is an important diagnostic criterion for HCM, and the most common imaging modality is heart ultrasound (US). The US is operator-dependent, and its interpretation is subject to human error and variability. We proposed an automated computer-aided diagnostic tool to discriminate HCM from healthy subjects on US images. We used a local directional pattern and the ResNet-50 pretrained network to classify heart US images acquired from 62 known HCM patients and 101 healthy subjects. Deep features were ranked using Student's t-test, and the most significant feature (SigFea) was identified. An integrated index derived from the simulation was defined as 100.log(10 )(SigFea /root 2) in each subject, and a diagnostic threshold value was empirically calculated as the mean of the minimum and maximum integrated indices among HCM and healthy subjects, respectively. An integrated index above a threshold of 0.5 separated HCM from healthy subjects with 100% accuracy in our test dataset

Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child's outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field

Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023)

Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources

Local Preserving Class Separation Framework to Identify Gestational Diabetes Mellitus Mother Using Ultrasound Fetal Cardiac Image

In the presence of gestational diabetes mellitus (GDM), the fetus is exposed to a hyperinsulinemia environment. This environment can cause a wide range of metabolic and fetal cardiac structural alterations. Fetal myocardial hypertrophy predominantly affecting the interventricular septum possesses a morphology of disarray similar to hypertrophic cardiomyopathy, and may be present in some GDM neonates after birth. Myocardial thickness may increase in GDM fetuses independent of glycemic control status and fetal weight. Fetal echocardiography performed on fetuses with GDM helps in assessing cardiac structure and function, and to diagnose myocardial hypertrophy. There are few studies in the literature which have established evidence for morphologic variation associated with cardiac hypertrophy among fetuses of GDM mothers. In this study, fetal ultrasound images of normal, pregestational diabetes mellitus (preGDM) and GDM mothers were used to develop a computer aided diagnostic (CAD) tool. We proposed a new method called local preserving class separation (LPCS) framework to preserve the geometrical configuration of normal and preGDM/GDM subjects. The generated shearlet based texture features under LPCS framework showed promising results compared with deep learning algorithms. The proposed method achieved a maximum accuracy of 98.15% using a support vector machine (SVM) classifier. Hence, this paradigm can be helpful to physicians in detecting fetal myocardial hypertrophy in preGDM/GDM mothers

Automated detection of chronic kidney disease using image fusion and graph embedding techniques with ultrasound images

Chronic Kidney disease (CKD) is a progressive disease affecting more than twenty million individuals in the United States. Disease progression is often characterized by complications such as cardiovascular diseases, anemia, hyperlipidemia and metabolic bone diseases etc., Based on estimated GFR values, the disease is categorized in 5 stages which significantly influence patient outcome. Cardiovascular ultrasound (US) (echocardiography) imagery demonstrate significant hemodynamic alterations that are secondary to CKD in the form of volume/ pressure overload. As the CKD pathology directly impacts cardiovascular disease, the US imaging shows structural and hemodynamic adaptation. Hence, the development of a computer-aided diagnosis (CAD) model to predict CKD would be desirable, and can potentially improve treatment. Several prior studies have utilized kidney features for quantitative analysis. In this paper, acquisition of the four-chamber heart US image is employed to predict CKD stage. The method combines image and feature fusion techniques under a graph embedding framework to characterize heart chamber properties. Moreover, a support vector machine is incorporated to classify heart US images. The proposed method achieved 100 % accuracy for a two-class system, and 99.09 % accuracy for a multi-class categorization scenario. Hence, our proposed CAD tool is deployable in both clinic and hospital settings for computer-aided screening of CKD