R wave peak time and no reflow following primary percutaneous coronary intervention: Immediate and short-term outcomes.

BACKGROUND: No reflow (NR) remains an important constraint in management of ST elevation myocardial Infarction (STEMI) patients undergoing primary percutaneous coronary intervention (PCI). Most ECG parameters validated till date including ST resolution are postprocedural. R wave peak time (RWPT) is a dynamic parameter and reflects conduction delay in ischaemic myocardium in selected leads supplied by infarct related artery (IRA). The present study was undertaken to see whether preprocedural RWPT per se or RWPT following primary PCI can predict persistence of NR along with immediate and short-term clinical outcome. METHODS: 200 patients were enrolled after exclusion. Clinical, Biochemical, ECG parameters including RPWT and angiographic parameters (pre- and post-procedure) were recorded. ECG papers was analysed using digital image processing software (http://imagej.nih.gov/ij/). All patients were followed up for 6 months. RESULTS: NR was observed in 35% of the patients. Age, Diabetes, symptom to balloon time, higher thrombus burden, peak CPK-MB level (pre and post procedure) were significantly higher in NR group. On ECG analysis, baseline RWPT, QRS duration and pathological Q wave were significantly higher in NR group. On multivariate analysis, age (OR 1.10 CI 1.00-1.21 P = 0.04), thrombus grade ≥ 3 in IRA (OR 12.38 CI 2.08-73.58 P = 0.006), symptom to balloon time (OR 2.18 CI 1.6-3.0 P < 0.001) and baseline RWPT on ECG [OR 1.86 CI 1.24-2.78, P = 0.003] were found to be independent predictors of NR. Increase in RWPT following primary PCI was found to both highly sensitive and specific for diagnosing persistence of NR after primary PCI. Follow up at the end of 6 months has shown that patients with increased RWPT following primary PCI had worse short-term cardiovascular outcomes compared to those with decreased RWPT following primary PCI. CONCLUSION: Baseline RWPT is a significant predictor of NR in patients of STEMI undergoing primary PCI. A persistently increased RWPT following primary PCI is also a highly sensitive and specific ECG marker of persistence of NR which is associated with adverse short-term clinical outcome.

Unravelled multilevel transformation networks for predicting sparsely observed spatio-temporal dynamics.

In this paper, we address the problem of predicting complex, nonlinear spatio-temporal dynamics when available data are recorded at irregularly spaced sparse spatial locations. Most of the existing deep learning models for modelling spatio-temporal dynamics are either designed for data in a regular grid or struggle to uncover the spatial relations from sparse and irregularly spaced data sites. We propose a deep learning model that learns to predict unknown spatio-temporal dynamics using data from sparsely-distributed data sites. We base our approach on the radial basis function (RBF) collocation method which is often used for meshfree solution of partial differential equations. The RBF framework allows us to unravel the observed spatio-temporal function and learn the spatial interactions among data sites on the RBF-space. The learned spatial features are then used to compose multilevel transformations of the raw observations and predict its evolution in future time steps. We demonstrate the advantage of our approach using both synthetic and real-world climate data. This article is part of the theme issue 'Data-driven prediction in dynamical systems'.

Machine learning approaches for ray-based ocean acoustic tomography.

Underwater sound propagation is primarily driven by a nonlinear forward model relating variability of the ocean sound speed profile (SSP) to the acoustic observations (e.g., eigenray arrival times). Ocean acoustic tomography (OAT) methods aim at reconstructing SSP variations (with respect to a reference environment) from changes of the acoustic measurements between multiple source-receiver pairs. This article investigates the performance of three different OAT methods: (1) model-based methods (i.e., classical ray-based OAT using a linearized forward model), (2) data-driven methods (such as deep learning) to directly learn the inverse model, and (3) a hybrid solution [i.e., the neural adjoint (NA) method], which combines deep learning of the forward model with a standard recursive optimization to estimate SSPs. Additionally, synthetic SSPs were generated to augment the variability of the training set. These methods were tested with modeled ray arrivals calculated for a downward refracting environment with mild fluctuations of the thermocline. Idealized towed and fixed source configurations are considered. Results indicate that merging data-driven and model-based methods can benefit OAT predictions depending on the selected sensing configurations and actual ray coverage of the water column. But ultimately, the robustness of OAT predictions depends on the dynamics of the SSP variations.

Unmet Need for Further LDL-C Lowering in India despite Statin Therapy: Lipid Association of India Recommendations for the Use of Bempedoic Acid.

Lipid-lowering therapy plays a crucial role in reducing adverse cardiovascular (CV) events in patients with established atherosclerotic cardiovascular disease (ASCVD) and familial hypercholesterolemia. Lifestyle interventions along with high-intensity statin therapy are the first-line management strategy followed by ezetimibe. Only about 20-30% of patients who are on maximally tolerated statins reach recommended low-density lipoprotein cholesterol (LDL-C) goals. Several factors contribute to the problem, including adherence issues, prescription of less than high-intensity statin therapy, and de-escalation of statin dosages, but in patients with very high baseline LDL-C levels, including those with familial hypercholesterolemia and those who are intolerant to statins, it is critical to expand our arsenal of LDL-C-lowering medications. Moreover, in the extreme risk group of patients with an LDL-C goal of ≤30 mg/dL according to the Lipid Association of India (LAI) risk stratification algorithm, there is a significant residual risk requiring the addition of non-statin drugs to achieve LAI recommended targets. This makes bempedoic acid a welcome addition to the existing non-statin therapies such as ezetimibe, bile acid sequestrants, and PCSK9 inhibitors. A low frequency of muscle-related side effects, minimal drug interactions, a significant reduction in high-sensitivity C-reactive protein (hsCRP), and a lower incidence of new-onset or worsening diabetes make it a useful adjunct for LDL-C lowering. However, the CV outcomes trial results are still pending. In this LAI consensus document, we discuss the pharmacology, indications, contraindications, advantages, and evidence-based recommendations for the use of bempedoic acid in clinical practice.

Heart rate variability as a marker of cardiovascular dysautonomia in post-COVID-19 syndrome using artificial intelligence.

INTRODUCTION: Cardiovascular dysautonomia comprising postural orthostatic tachycardia syndrome (POTS) and orthostatic hypotension (OH) is one of the presentations in COVID-19 recovered subjects. We aim to determine the prevalence of cardiovascular dysautonomia in post COVID-19 patients and to evaluate an Artificial Intelligence (AI) model to identify time domain heart rate variability (HRV) measures most suitable for short term ECG in these subjects. METHODS: This observational study enrolled 92 recently COVID-19 recovered subjects who underwent measurement of heart rate and blood pressure response to standing up from supine position and a 12-lead ECG recording for 60 s period during supine paced breathing. Using feature extraction, ECG features including those of HRV (RMSSD and SDNN) were obtained. An AI model was constructed with ShAP AI interpretability to determine time domain HRV features representing post COVID-19 recovered state. In addition, 120 healthy volunteers were enrolled as controls. RESULTS: Cardiovascular dysautonomia was present in 15.21% (OH:13.04%; POTS:2.17%). Patients with OH had significantly lower HRV and higher inflammatory markers. HRV (RMSSD) was significantly lower in post COVID-19 patients compared to healthy controls (13.9 ± 11.8 ms vs 19.9 ± 19.5 ms; P = 0.01) with inverse correlation between HRV and inflammatory markers. Multiple perceptron was best performing AI model with HRV(RMSSD) being the top time domain HRV feature distinguishing between COVID-19 recovered patients and healthy controls. CONCLUSION: Present study showed that cardiovascular dysautonomia is common in COVID-19 recovered subjects with a significantly lower HRV compared to healthy controls. The AI model was able to distinguish between COVID-19 recovered patients and healthy controls.

Prevalence and predictors of left atrial appendage inactivity in patients of rheumatic mitral stenosis in sinus rhythm: An observational study.

BACKGROUND: Systemic thromboembolism is a known complication of rheumatic mitral stenosis (RMS) in sinus rhythm (SR). Left atrial appendage (LAA), the commonest site of thrombus formation is usually hypocontractile (inactive) in such patients. We aimed to study the prevalence of LAA inactivity (LAAI) in severe RMS and assess its independent predictors. METHODS: The study population consisted of 100 patients of severe RMS in SR. Transthoracic and transesophageal echocardiography were done to assess LAA contractile function. Patients with LAA-peak emptying velocity < 25 cm/seconds were defined as having LAAI. RESULTS: The mean age of study subjects was 31.66±8.69 years and 56% were females. 73% patients had LAAI (Group A), while remaining 27% had normal LAA function (Group B). Mitral-valve area (MVA) and lateral annulus systolic velocity (Sa-wave) were significantly lower while mitral valve mean gradient (MVMG) and serum fibrinogen were significantly higher (all p-values < 0.001) in group A patients. On multivariate binary logistic regression analysis, MVMG (p < 0.001), Sa-wave (p = 0.02), and serum fibrinogen (p = 0.005) were independent predictors of LAAI. Optimal cut-off values of MVMG, Sa-wave and serum fibrinogen for predicting LAAI were 11.5 mm Hg, 6.8 cm/seconds and 300 mg/dl, respectively. Sixty-Seven (90.55%) patients in group A compared to 13(48.1%) in group B had LA/LAA smoke. LAAI was the only independent predictor of left atrium (LA)/LAA smoke with or without associated thrombus. CONCLUSION: There is high prevalence of LAAI in patients of severe MS in SR. MVMG, Sa-wave, and serum fibrinogen levels are independent predictors of LAAI. LAAI is an independent predictor of LA/LAA smoke with or without associated thrombus.

Left ventricular global longitudinal strain in COVID-19 recovered patients.

OBJECTIVES: Myocardial injury during active coronavirus disease-2019 (COVID-19) infection is well described; however, its persistence during recovery is unclear. We assessed left ventricle (LV) global longitudinal strain (GLS) using speckle tracking echocardiography (STE) in COVID-19 recovered patients and its correlation with various parameters. METHODS: A total of 134 subjects within 30-45 days post recovery from COVID-19 infection and normal LV ejection fraction were enrolled. Routine blood investigations, inflammatory markers (on admission) and comprehensive echocardiography including STE were done for all. RESULTS: Of the 134 subjects, 121 (90.3%) were symptomatic during COVID-19 illness and were categorized as mild: 61 (45.5%), moderate: 50 (37.3%) and severe: 10 (7.5%) COVID-19 illness. Asymptomatic COVID-19 infection was reported in 13 (9.7%) patients. Subclinical LV and right ventricle (RV) dysfunction were seen in 40 (29.9%) and 14 (10.5%) patients, respectively. Impaired LVGLS was reported in 1 (7.7%), 8 (13.1%), 22 (44%) and 9 (90%) subjects with asymptomatic, mild, moderate and severe disease, respectively. LVGLS was significantly lower in patients recovered from severe illness(mild: -21 ± 3.4%; moderate: -18.1 ± 6.9%; severe: -15.5 ± 3.1%; p < 0.0001). Subjects with reduced LVGLS had significantly higher interleukin-6 (p < 0.0001), C-reactive protein (p = 0.001), lactate dehydrogenase (p = 0.009), serum ferritin (p = 0.03), and troponin (p = 0.01) levels during index admission. CONCLUSIONS: Subclinical LV dysfunction was seen in nearly a third of recovered COVID-19 patients while 10.5% had RV dysfunction. Our study suggests a need for closer follow-up among COVID-19 recovered subjects to elucidate long-term cardiovascular outcomes.

A Task-Driven Feedback Imager with Uncertainty Driven Hybrid Control.

Deep Neural Network (DNN) systems tend to produce overconfident or uncalibrated outputs. This poses problems for active sensor systems that have a DNN module as the main feedback controller. In this paper, we study a closed-loop feedback smart camera from the lens of uncertainty estimation. The uncertainty of the task output is used to characterize and facilitate the feedback operation. The DNN uncertainty in the feedback system is estimated and characterized using both sampling and non-sampling based methods. In addition, we propose a closed-loop control that incorporates uncertainty information when providing feedback. We show two modes of control, one that prioritizes false positives and one that prioritizes false negatives, and a hybrid approach combining the two. We apply the uncertainty-driven control to the tasks of object detection, object tracking, and action detection. The hybrid system improves object detection and tracking accuracy on the CAMEL dataset by 1.1% each respectively. For the action detection task, the hybrid approach improves accuracy by 1.4%.

Prevalence and patterns of coronary artery anomalies in 28,800 adult patients undergoing angiography in a large tertiary care centre in India.

Coronary artery anomalies (CAAs) are a diverse group of disorders with varied clinical presentation and pathophysiological mechanisms. A majority of these anomalies are asymptomatic and often an incidental finding on coronary angiogram or autopsy. This retrospective study included 28,800 patients who underwent coronary angiography from 2016 to 2020. The coronary angiograms were reviewed by two independent reviewers and CAAs were documented. CAAs were classified into i) anomalies of coronary artery connection, ii) anomalies of intrinsic coronary arterial anatomy and iii) anomalies of myocardial/coronary artery interaction as proposed by the European Society of Cardiology. Of the 28,800 coronary angiograms, CAAs were present in 4.12% with anomalies in the left coronary artery (LCA) being most common. Anomalies of coronary artery connection were most common (48.48%) followed by anomalies of myocardial/coronary artery interaction (34.49%) and anomalies of intrinsic coronary artery anatomy (17.03%). Among anomalies of coronary artery connection, absent left main trunk or split LCA with separate origins of left anterior descending coronary artery and left circumflex coronary artery from the left coronary sinus of Valsalva (22.59%) was most common. An intramural course or "myocardial bridge" had an incidence of 1.16%  while incidence of coronary artery fistulae (CAF) was 0.115%.

Bradyarrhythmias in patients with COVID-19: A case series.

Novel coronavirus disease (COVID-19) can have variety of cardiac manifestations; however, less is known about the prevalence, clinical characteristics and outcomes of bradyarrhythmias in patients with COVID-19. In the present case series of bradyarrhythmia in patients with COVID-19, we report complete heart block requiring intervention in 5 patients and sinus node dysfunction in 2 patients.

Accidental retrieval of a fully deployed stent: A case report.

We describe a case of accidental retrieval of a fully deployed stent in ostial left anterior descending (LAD) artery. This occurred while attempting stent delivery in obtuse marginal (OM) artery due to entrapment of a wire which inadvertently had passed through the struts of ostial LAD stent. The proposed mechanism and recommendations to avoid this rare complication are discussed.