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.

Prevalence of metabolic syndrome and its association with atrial arrhythmias in patients with implanted permanent pacemaker for cardiac conduction abnormality.

The objective of the study was to find the prevalence of metabolic syndrome along with identifying the atrial arrhythmias, QTC interval, and coronary artery disease among these patients during follow-ups. Among 171 subjects who were implanted with permanent pacemakers, metabolic syndrome was present in 90 (52.6 %). Prevalence of Arrhythmias was 49 (28.7 %), atrial tachycardia (AT)/atrial fibrillation (AF) was seen in 29 (17 %) patients. Our study showed that there is a strong association between metabolic syndrome and atrial arrhythmias. Metabolic syndrome, age, coronary artery disease and Systolic blood pressure were good independent predictors of atrial arrhythmias among patients with pacemaker implantation.

Association of ABO blood groups with the severity of coronary artery disease in southern India population: A prospective cross-sectional study.

This study aimed to find an association between ABO blood groups with presence and severity of Coronary artery disease (CAD) among Indian population. 1500 patients undergoing elective coronary angiogram (CAG) at a tertiary care hospital in Karnataka were enrolled in the study. Baseline demographic data and the presence of cardiac comorbidities were documented. Data from baseline echocardiography and angiographic studies were compiled. The incidence of CAD was higher among patients with blood group A. Blood group A also showed a higher incidence of acute coronary syndrome (ACS), left ventricular dysfunction, triple vessel disease, and severe CAD among the patients who underwent CAG.

Yoga as an adjuvant therapy in heart failure patients on optimal medical management analysed using echocardiographic parameters.

BACKGROUND: Heart failure (HF) is a chronic complex disease that prevails as a prime cause of concern for healthcare sectors worldwide, with a concordant objective to improve the long-term prognosis. Analysis of the available literature is evidence that yoga therapy and basic lifestyle modifications have considerably augmented heart failure patients' quality of life and enhanced left ventricular ejection fraction and NYHA functional class. AIM: Our study aims to establish the long-term outcomes of yoga therapy to validate the addition of yoga therapy as a complementary treatment in managing HF. METHODS: A prospective non-randomized study was conducted at a tertiary care center including seventy-five HF patients with NYHA class III or less who underwent coronary intervention, revascularization, or device therapy within the past six months to one year and continuing guideline-directed optimal medical therapy (GDMT). Thirty-five participants were part of the Interventional Group (IG), and forty were in the Non-Interventional Group (Non-IG). The IG received yoga therapy and GDMT, while the non-IG were only under standard GDMT. Echocardiographic parameters were compared at various follow-ups up to one year to see the impact of Yoga therapy on HF patients. RESULTS: A total of 75 heart failure patients, including 61 males and 14 females. The IG and non-IG had 35 subjects (31 males and 4 females) and 40 subjects (30 males and 10 females), respectively. Echocardiographic parameters were observed to compare the IG and Non-IG groups, and those did not demonstrate any significant difference between the two groups (p-value > 0.05). But, the echocardiographic parameters of the IG and non-IG from baseline to six months and one year showed a significant improvement (p-value of <0.05). The functional outcome (NYHA classes) was assessed after follow-up, and a p-value <0.05 showed a substantial improvement in the IG. CONCLUSION: Yoga therapy results in better prognosis, functional outcome, and left ventricular performance in HF patients with NYHA III or less. Hence this investigation has attempted to justify its importance as adjuvant/complimentary treatment for HF patients.

A comparative analysis of conventional and speckle-tracking strain echocardiographic findings in diabetic and non-diabetic kidney disease patients with normal ejection fraction.

This study aimed to compare the differences in echocardiographic and strain parameters in patients with diabetic kidney disease (DKD) and non-diabetic kidney disease (NDKD) in a cohort with pre-dialysis chronic kidney disease (CKD) and normal ejection fraction (EF). In this single-center prospective study, patients with CKD stages 3-5 and EF > 55% were included. We compared cardiac structure and function using conventional and speckle-tracking strain echocardiography among DKD and NDKD groups. Cardiovascular outcomes were assessed at the end of the study. Of the included 117 patients, 56 (47.9%) had DKD, and 61 (52.1%) had NDKD. Patients with DKD had higher ratios of early mitral inflow velocity and mitral annular early diastolic velocity (E/e') (11.9 ± 4.4 vs. 9.8 ± 3.5; p = 0.004), lower septal e' velocity (7.1 ± 2.5 vs. 8.2 ± 2.8; p = 0.031), lower lateral e' velocity (9.2 ± 2.9 vs. 10.4 ± 3.8; p = 0.045) and longer deceleration times (209.2 ± 41.5 vs. 189.1 ± 48.0; p = 0.017), compared to those with NDKD. Left ventricular mass index (LVMI), global longitudinal strain (GLS), early diastolic strain rate (SRE), and E/SRE were similar. At a median follow-up of 239 days, 3-P MACE (11.5% vs. 4.9%; p = 0.047) and 4-P MACE (28.6% vs. 11.5%; p = 0.020) were observed to be higher in the DKD group. Diastolic dysfunction was more common in patients with DKD, compared to those with NDKD, although both groups had similar LVMI and GLS. Those with DKD also had poorer cardiovascular outcomes. This highlights the importance of the assessment of diastolic function in CKD, particularly in those with diabetic CKD.

Evaluation of subclinical left ventricular systolic dysfunction using two-dimensional speckle-tracking echocardiography in patients with Child-Pugh A and B cirrhosis: A case-control study.

BACKGROUND: Although studies have proven that liver cirrhosis affects cardiac hemodynamics by means of circulatory overload, they present with definite cardiac functional alteration mostly with end-stage disease. There is limited data on relationship between progression of cirrhosis, cardiac mechanics and sub-clinical dysfunction. This study was done to assess ventricular myocardial mechanics using speckle tracking and deformation imaging among Child-Turcotte-Pugh (CTP) classification A and B cirrhosis. METHOD: Seventy patients with cirrhosis of Child-Pugh A/B class and sixty-two healthy subjects were prospectively evaluated by standard conventional echocardiography and deformation imaging with rotational echocardiography. Clinical stage of liver cirrhosis was assessed by model for end-stage liver disease (MELD) scores and CTP classification. RESULTS: Mean ages of patients with cirrhosis and controls were 55.64±14 years and 52.24±12 years, respectively. Though left ventricular (LV) dimensions (end diastolic dimension: 47.27±4.6 mm vs. 45.03±3.8 mm, p = 0.003; end systolic dimension: 30.33±4.9 mm vs. 28.40±2.91 mm, p = 0.006) and volumes (end diastolic volume: 82.08±22.53 mL vs. 68.18±15.75 mL, p = 0.001; end systolic volume: 28.60±8.42 mL vs. 22.18±7.48 mL, p = 0.001) were significantly higher in patients with cirrhosis, mean ejection fraction (EF) by Simpsons method was higher among controls (65.83±5.79% vs. 68.35±5.79%, p = 0.009). Left atrial volume was higher in cirrhosis group indicating presence of diastolic dysfunction (41.24±14.10 mL vs. 26.08±6.4 mL, p = 0.001). Global longitudinal strain as assessed by speckle tracking echocardiography did not show statistical significant difference between two groups (-22.35±4.08% vs. -21.80±2.54%, p = 0.348). Median value of torsion parameters in patients with cirrhosis did not differ compared to controls (torsion in degrees: 2.46 vs. 2.79, p = 0.268). CONCLUSION: Patients with Child-Pugh A and B stages of cirrhosis present with preserved longitudinal strain, normal torsion but with subtle diastolic dysfunction. Higher MELD score may correlate with increased longitudinal strain possibly due to hyperdynamic state.

Clinical utility of aortic isthmus Doppler in the prediction of perinatal outcomes.

BACKGROUND: Doppler studies of uteroplacental-fetal circulation have been proven useful in diagnosing fetal growth restriction, appropriately timing delivery, and improving perinatal morbidity and mortality. There has been an extensive search for the ideal means to identify fetuses between the compensatory and acidemic phase (ie, the "preacidemic phase"), and the aortic isthmus Doppler seems to show promise. OBJECTIVE: This study aimed to investigate: (1) the prevalence of abnormal aortic isthmus Dopplers in a cohort of small-for-gestational-age fetuses and their correlation with other conventional Doppler abnormalities, and (2) the predictive ability of abnormal aortic isthmus Dopplers with regard to short-term adverse neonatal outcomes. STUDY DESIGN: Fetuses diagnosed as small-for-gestational-age at ≥24 weeks' gestation were included. Management was as per the standard protocol. Aortic isthmus Doppler was performed within a week of delivery with other conventional Dopplers. The adverse perinatal outcomes studied were: requirement of neonatal resuscitation at birth, Apgar score at 5 minutes <7, cord blood pH <7, presence of bronchopulmonary dysplasia, hypoxic-ischemic encephalopathy, grade III/IV intraventricular hemorrhage, necrotizing enterocolitis, sepsis, neonatal intensive care unit stay longer than 14 days, and stillbirth or neonatal death. RESULTS: Among 121 small-for-gestational-age fetuses, 67 showed Doppler abnormalities in ≥1 vessels. The prevalence of abnormal aortic isthmus Doppler was 14.87%. Analysis was between group 1 with 103 normal aortic isthmus and group 2 with 18 abnormal aortic isthmus fetuses; 41 cases had some form of adverse perinatal outcome, the frequency of which was comparable between the groups. Abnormal aortic isthmus Doppler had a significant correlation with low cerebroplacental ratio, absent or reversed end-diastolic flow in the umbilical artery, and high pulsatility index in the ductus venosus. The positive likelihood ratio for predicting composite adverse perinatal outcome was 10.2 for absent or reversed end-diastolic flow in the umbilical artery, 9.6 for low cerebroplacental ratio, 2.28 for absent or retrograde flow in the aortic isthmus, and 2 for abnormal ductus venosus. CONCLUSION: Predelivery abnormal aortic isthmus Dopplers performed worse than other conventional Dopplers in predicting abnormal perinatal outcomes.

Cardiovascular complications and predictors of mortality in hospitalized patients with COVID-19: a cross-sectional study from the Indian subcontinent.

BACKGROUND: COVID-19 has spread rapidly across the world, producing significant morbidity and mortality. We investigated the cardiovascular complications and association of laboratory parameters with severity and mortality predictors in COVID-19 hospitalized patients. METHODS: Between May 2020 and June 2021, 730 COVID-19 patients were included in this retrospective observational study in the Coastal Karnataka region of South India. Acute coronary syndrome (ACS), myocarditis, arrhythmias, and all-cause mortality were reported as cardiovascular consequences. Neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), serum creatinine, D-dimer, troponin T, N-terminal pro-brain natriuretic peptide (NT-ProBNP), serum ferritin, and serum lactate dehydrogenase (LDH) were among the laboratory parameters measured. RESULTS: Most common electrocardiogram (ECG) changes were prolonged QTc interval (45.6%) followed by ST-T changes (40.7%) and sinus tachycardia (24.2%). 9.2% patients presented with ACS, with 38.8% having ST-elevation myocardial infarction (STEMI) and 61.2% having non-ST elevation myocardial infarction (NSTEMI). In non-survivors, NLR (p < 0.001) and PLR (p = 0.001) were significantly higher. Multivariable regression analysis showed that age (OR:1.019, 95% CI 1.003-1.034; p = 0.017), acute kidney injury (OR:3.562, 95% CI 1.737-7.301; p = 0.001), white blood cell count (WBC) (OR = 1.100, 95% CI 1.035-1.169; p = 0.002), platelet count (OR = 0.994, 95% CI 0.990-0.997; p = 0.001), PLR (OR = 1.002, 95% CI 1.000-1.004; p = 0.023) and severe COVID-19 (OR = 9.012, 95% CI 3.844-21.129; p = 0.001) were independent predictors of mortality in COVID-19 patients. CONCLUSIONS: Age, WBC count, neutrophil%, NLR, PLR, creatinine, D-dimer, ferritin, LDH, tachycardia, and lymphocytes% strongly correlated with the severity of the disease. Age, acute kidney injury, elevated WBC count, a greater PLR, low platelet count, and COVID-19 severity were independent predictors of mortality.

Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images.

Hypertension (HTN) is a major risk factor for cardiovascular diseases. At least 45% of deaths due to heart disease and 51% of deaths due to stroke are the result of hypertension. According to research on the prevalence and absolute burden of HTN in India, HTN positively correlated with age and was present in 20.6% of men and 20.9% of women. It was estimated that this trend will increase to 22.9% and 23.6% for men and women, respectively, by 2025. Controlling blood pressure is therefore important to lower both morbidity and mortality. Computer-aided diagnosis (CAD) is a noninvasive technique which can determine subtle myocardial structural changes at an early stage. In this work, we show how a multi-resolution analysis-based CAD system can be utilized for the detection of early HTN-induced left ventricular heart muscle changes with the help of ultrasound imaging. Firstly, features were extracted from the ultrasound imagery, and then the feature dimensions were reduced using a locality sensitive discriminant analysis (LSDA). The decision tree classifier with contourlet and shearlet transform features was later employed for improved performance and maximized accuracy using only two features. The developed model is applicable for the evaluation of cardiac structural alteration in HTN and can be used as a standalone tool in hospitals and polyclinics.

Novel Radiomics Features for Automated Detection of Cardiac Abnormality in Patients with Pacemaker.

Cardiac pacemakers are used in the treatment of patients with symptomatic bradycardia. The pacemaker paces the heart at the predetermined rate to maintain uninterrupted cardiac activity. Usually, pacemaker lead will be connected to the right atrium (RA) and right ventricle (RV) in dual-chamber pacemaker implantation and RV alone in single-chamber pacemaker implantation. This alters the route of proper conduction across the myocardial cells. The cell-to-cell conduction transmission in pacing delays the activation of selected intraventricular myocardial activation. Pacing-induced cardiomyopathy (PICM) is most commonly defined as a drop in left ventricle ejection fraction (LVEF) in the setting of chronic, high-burden right ventricle (RV) pacing. Currently, very few effective treatments are standard for PICM which rely on the detection of the RV pacing. Such treatments have primarily focused on upgrading to cardiac resynchronization therapy (CRT) when LVEF has dropped. However, the early and accurate detection of these stress factors is challenging. Cardiac desynchrony and interventricular desynchrony can be determined by various echocardiographic techniques, including M-mode, Doppler method, tissue Doppler method, and speckle tracking echocardiography which is subjective measures and shows a significant difference between RV and LV preejection period where the activation of LV is delayed considerably. Computer-aided diagnosis (CAD) is a noninvasive technique that can classify the ultrasound images of the heart in pacemaker-implanted patients and healthy patients with normal left ventricular systolic function and further detect the variations in pacemaker functions in its early stage using heart ultrasound images. Developing such a system requires a vast and diverse database to reach optimum performance. This paper proposes a novel CAD tool for the accurate detection of pacemaker variations using machine learning models of decision tree, SVM, random forest, and AdaBoost. The models have been used to extract radiomics features in terms of textures and then screened by their Relief-F scores for selection and ranking to be classified into nine groups consisting of up to 250 radiomics features. Ten best features were fed to the machine learning models. The R-wave dataset achieved a maximum test performance accuracy of 97.73% with four features in the random forest model. The T-wave dataset achieved a maximum test performance accuracy of 96.59% with three features in the SVM model. Our experimental results demonstrate the system's robustness, which can be developed as an early and accurate detection system for pacing-induced cardiomyopathy.

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·log10(SigFea/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.

Automated emotion recognition: Current trends and future perspectives.

BACKGROUND: Human emotions greatly affect the actions of a person. The automated emotion recognition has applications in multiple domains such as health care, e-learning, surveillance, etc. The development of computer-aided diagnosis (CAD) tools has led to the automated recognition of human emotions. OBJECTIVE: This review paper provides an insight into various methods employed using electroencephalogram (EEG), facial, and speech signals coupled with multi-modal emotion recognition techniques. In this work, we have reviewed most of the state-of-the-art papers published on this topic. METHOD: This study was carried out by considering the various emotion recognition (ER) models proposed between 2016 and 2021. The papers were analysed based on methods employed, classifier used and performance obtained. RESULTS: There is a significant rise in the application of deep learning techniques for ER. They have been widely applied for EEG, speech, facial expression, and multimodal features to develop an accurate ER model. CONCLUSION: Our study reveals that most of the proposed machine and deep learning-based systems have yielded good performances for automated ER in a controlled environment. However, there is a need to obtain high performance for ER even in an uncontrolled environment.

Persistent subclinical myocardial dysfunction among infants of diabetic mothers.

INTRODUCTION: Offspring of diabetic mothers have five times higher risk of cardiovascular abnormalities than in normal pregnancies. These cardiac anomalies involve fetal cardiac morphological and functional changes. Here, we investigate subclinical cardiovascular abnormalities, including structural and functional changes among infants of diabetic mothers. MATERIAL AND METHODS: A prospective cohort study was carried out from March 2017 to September 2020 at a South Indian tertiary care center. One hundred ninety-eight newborns delivered from non-diabetic mothers, well-controlled diabetic mothers (WCDM), and poorly controlled diabetic mothers (PCDM) were studied. Neonates and infantile echocardiographic imaging were performed at the first week of life, at 6-weeks, and 6-months of life. Cardiac structure and function were assessed using 2D, M-mode, Conventional Doppler, and Tissue Doppler imaging. RESULTS: Out of the total (198) infants, 66 (33.3%) were from the non-diabetic (control) mothers, 60 (30.3%) from the WCDM, and 72 (36.4%) were from the PCDM groups. In both WCDM and PCDM groups, the mean gestational ages at birth were shorter than the control group (37.19 ± 0.82, 36.02 ± 2.15, and 37.91 ± 1.33 weeks respectively). There was a high incidence of neonatal hypoglycemia (p-value <0.001), NICU stay >7 days (p-value <0.018), and persistent fetal transitional cardiac shunt (p-value <0.03) among poorly controlled DM group in comparison to others. Tissue deformation imaging showed a significant reduction in left ventricular global strain and strain rate in the neonatal heart from poorly controlled diabetic mothers. Myocardial wall thickness among neonates of diabetic mothers was higher compared to controls. LV TEI was higher in PCDM groups in comparison with WCDM and control groups; (0.59 ± 0.11, 0.46 ± 0.35, and 0.37 ± 0.12 respectively, p-value =0.01). Similarly, RV TEI was high in the PCDM (0.56 ± 0.09) group compared to the WCDM group (0.33 ± 0.12) and control group (0.28 ± 0.10). CONCLUSIONS: Infants of diabetic mothers are at higher risk of developing cardiac abnormalities, including structural and functional defects. The highest increase in interventricular septal thickening found in poorly controlled diabetic mothers' neonates will disrupt both ventricles' functions, followed by WCDM and the control group.

Role of Artificial Intelligence in COVID-19 Detection.

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization.

Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and-when chronic-calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods-machine versus deep learning-and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.

Prevalence and Patterns of Sleep-Disordered Breathing in Indian Heart Failure Population.

BACKGROUND: Sleep-disordered breathing (SDB) is a common yet a largely underdiagnosed entity in developing countries. It is one treatable condition that is known to adversely affect the mortality and morbidity in heart failure (HF). This study is one of the first attempts aimed at studying SDB in chronic HF patients from an Indian subcontinent. OBJECTIVES: The aim of this study was to study the prevalence, type, and characteristics of SDB in chronic HF patients and their association with HF severity and left ventricular (LV) systolic function and also to determine the relevance of SDB symptoms and screening questionnaires such as the Epworth Sleepiness Scale (ESS), Berlins questionnaire, and STOP-BANG score in predicting SDB in chronic HF patients. METHODS: We enrolled 103 chronic heart failure patients aged more than 18 years. Patients with a history of SDB and recent acute coronary syndrome within 3 months were excluded. Relevant clinical data, anthropometric measures, echocardiographic parameters, and sleep apnea questionnaires were collected, and all patients underwent the overnight type 3 sleep study. RESULTS: The overall prevalence of SDB in our study was high at 81.55% (84/103), with a predominant type of SDB being obstructive sleep apnea (59.2%). The occurrence of SDB was significantly associated with the male gender (p = 0.002) and higher body mass index (BMI) values (p = 0.01). SDB symptoms and questionnaires like ESS, STOP-BANG, and Berlins also did not have a significant association with the occurrence of SDB in HF patients. CONCLUSIONS: Our study showed a high prevalence of occult SDB predominantly OSA, in chronic HF patients. We advocate routine screening for occult SDB in HF patients.

A study of narrow QRS tachycardia with emphasis on the clinical features, ECG, electrophysiology/radiofrequency ablation.

Aim: Atrioventricular nodal reentrant tachycardia (AVNRT) and atrioventricular reentrant tachycardia (AVRT) are the most common forms of paroxysmal regular supraventricular tachycardias. Materials & methods: The clinical, ECG and electrophysiological findings of 121 patients with narrow QRS tachycardia were evaluated prospectively. Results: A total of 75 (62%) of the patients had AVNRT while 46 (38%) had AVRT. Pounding sensation in the neck (40 vs 15.2%; p = 0.004), presynope (26.7 vs 56.5%; p = 0.001) identifiable P wave after QRS complex (25.3 vs 73.9%; p = 0.001), pseudo r'/s waves (45.3 vs 4.3%; p = 0.001), limb leads ST-T changes (34.7 vs 60.9%; p = 0.004) were the significant changes observed. A total of 94.7% of AVNRT and 87% of AVRT could be diagnosed correctly considering both clinical and ECG criteria. Conclusion: Pounding sensation in the neck and presyncope along with ECG features like identifiable P wave after QRS complex, pseudo r'/s waves and limb lead ST-T changes very accurately differentiate AVNRT and AVRT.

Fetal echocardiography at term in diabetic pregnancies helps predict the adverse neonatal outcome - Results of a prospective observational study from South India.

There is sparse Indian data on whether fetal echocardiography among pregnant diabetics would be useful to predict adverse perinatal/neonatal outcome. OBJECTIVES: To study fetal cardiac changes in diabetic mothers and non-diabetic controls from 24 weeks gestation until the neonatal period; correlate them with maternal glycemic control; study their implications on adverse perinatal/neonatal outcome. METHODOLOGY: Prospective observational cohort study. Pregnant diabetics (17 overt, 66 gestational) recruited beyond 24 weeks, divided as well (39) and poorly (44) controlled, based on American Diabetes Association 2016 criteria. Controls were 102 healthy non-diabetic pregnancies. Fetal echocardiography performed at weeks 24-32, 32-36, >37, and between 4 and 7 days on neonates. The thickness of Interventricular septum (IVS), Right Ventricle (RV), and Left ventricle (LV) assessed with M mode. E/A ratio across Tricuspid/Mitral valves and Tei index determined. TDI(Tissue Doppler imaging) used to assess tissue annular velocities across IVS, RV, and LV. Maternal glycemic control and various perinatal/neonatal adverse outcomes were recorded. RESULTS: Significant myocardial hypertrophy seen among fetuses of diabetic mothers versus controls, most severe at term among the poorly controlled diabetics. Structural changes persisted in the neonate. At term, fetal myocardial dysfunction was evident among diabetic pregnancies only as poor annular systolic velocity across IVS, RV using TDI. However, Tissue Doppler changes could not predict adverse perinatal/neonatal outcome. Myocardial dysfunction could not be demonstrated in the neonates. Myocardial hypertrophy at term was a surrogate marker for suboptimal glycemic control, and it could predict important neonatal morbidities like hypoglycaemia, hyperbilirubinemia, prolonged NICU stays, and persistent foetal cardiac shunts. CONCLUSION: Our study shows a significant association between fetal myocardial hypertrophy and maternal glycemic control among GDM pregnancies. There also seems to be an association between fetal myocardial hypertrophy and some of the adverse perinatal events including hypoglycemia. However these newborns were not found to have clinically relevant cardiac comorbidities even though there was significant septal hypertrophy in utero.

Role of deformation imaging in left ventricular non-compaction and hypertrophic cardiomyopathy: an Indian perspective.

BACKGROUND: Speckle tracking echocardiography (STE) has emerged as a novel feasible tool for the assessment of left ventricular rotational parameters. Since hypertrophic cardiomyopathy(HCM) shares morphologic features with left ventricular non-compaction (LVNC), we used this imaging modality to compare rotational mechanics between these two entities. RESULTS: We compared global and regional LV function and rotational mechanics between LVNC, HCM, and healthy subjects using STE. Longitudinal strain and torsion were obtained from echocardiographic images from parasternal short axis as well as standard LV apical views. Twelve patients with LVNC [mean age 46.12 ± 14.66 years; median 47.5 IQR (39.25-58.5) years] were compared with 18 HCM patients [mean age 49.48± 17.22 years; median 56 IQR (33-65) years] and 18 healthy subjects [mean age: 51.50± 12.51 years; median 51(45.75-58) years]. LVNC group showed a significantly reduced longitudinal strain at the apical region compared to HCM group (- 12.18 ± 6.25 vs - 18.37 ± 3.67; P < 0.05). Rigid body rotation(RBR) was found in 50% of patients whereas the other half had a normal rotation at the apex and the base. Among the patients with RBR, all patients had a uniform counterclockwise rotation. CONCLUSION: Longitudinal strain was impaired in both the forms of cardiomyopathy; however, LVNC showed a more significant reduction in the apical region compared to patients with HCM suggesting a development abnormality in these regions. A reduction in left ventricular torsion was specifically noted among patients with LVNC with a uniform anticlockwise rotation of LV base and apex.

Comparison of Plasma Levels of Renin, Vasopressin and Atrial Natriuretic Peptide in Hypertensive Amlodipine Induced Pedal Oedema, Non-Oedema and Cilnidipine Treated Patients.

INTRODUCTION: Amlodipine is a third generation dihydropyridine group of calcium channel blocker and having an excellent antihypertensive profile. Pedal Oedema (PE) is the major drawback of amlodipine therapy and the incidence of Amlodipine Induced Pedal Oedema (AIPE) has been found significantly high. Several neurohumoral factors influence the incidence of oedema. AIM: We aimed to compare the plasma levels of renin, vasopressin and atrial natriuretic peptide in hypertensive AIPE, non-oedema and cilnidipine treated patients. MATERIALS AND METHODS: The present prospective, interventional study was conducted on 104 mild to moderate hypertensive patients (52 patients in each group), after due consideration of eligibility criteria. Plasma Renin (PR), Vasopressin (VAS), and the Atrial Natriuretic Peptide (ANP) was estimated by ELISA test and compared between the AIPE, Amlodipine Treated Non-Oedema (ATNE) in Phase I, and AIPE and Cilnidipine Treated (CT) Groups in Phase II. RESULTS: The clinical and demographic parameters were matched. PR was significantly high in AIPE group than the ATNE, and it was significantly reduced after one month follow up with the substitution of cilnidipine. The median (IQR) value of PR was 4.87 (3.58, 6.63), 3.50 (1.44, 5.47) and 2.66 (1.02, 5.66) ng/ml in AIPE, ATNE, CT group respectively. VAS was significantly high in AIPE group than ATNE, and it significantly reduced after one month follow up with CT group. The median (IQR) value of vasopressin was 6.78 (2.55, 9.16), 2.58 (1.61, 5.73) and 2.50 (1.23, 5.00) ng/ml in AIPE, ATNE and CT groups respectively. There was no significant difference seen in plasma ANP levels between the groups. The p-value was <0.05 which is statistically significant. CONCLUSION: The AIPE may not be volume overload or fluid retention; it may be due to persistent raise in adrenergic activity followed chronic amlodipine therapy. Cilnidipine relatively suppresses the sympathetic activity, and completely resolves the AIPE by significantly reducing PR and VAS levels. ANP did not show a difference between groups. Cilnidipine is the suitable alternative antihypertensive drug for AIPE patients.