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. 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. 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. 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.

COVID-19 pandemic has led to unprecedented increase in rates of stress and burn out among healthcare workers (HCWs). Heart rate variability (HRV) has been shown to be reflective of stress and burnout. The present study evaluated the prevalence of burnout and attempted to develop a HRV based predictive machine learning (ML) model to detect burnout among HCWs during COVID-19 pandemic. Mini-Z 1.0 survey was collected from 1615 HCWs, of whom 664, 512 and 439 were frontline, second-line and non-COVID HCWs respectively. Burnout was defined as score ≥3 on Mini-Z-burnout-item. A 12-lead digitized ECG recording was performed and ECG features of HRV were obtained using feature extraction. A ML model comprising demographic and HRV features was developed to detect burnout. Burnout rates were higher among second-line workers 20.5% than frontline 14.9% and non-COVID 13.2% workers. In multivariable analyses, features associated with higher likelihood of burnout were feeling stressed (OR = 6.02), feeling dissatisfied with current job (OR = 5.15), working in a chaotic, hectic environment (OR = 2.09) and feeling that COVID has significantly impacted the mental wellbeing (OR = 6.02). HCWs with burnout had a significantly lower HRV parameters like root mean square of successive RR intervals differences (RMSSD) [p < 0.0001] and standard deviation of the time interval between successive RR intervals (SDNN) [p < 0.001]) as compared to normal subjects. Extra tree classifier was the best performing ML model (sensitivity: 84%) In this study of HCWs from India, burnout prevalence was lower than reports from developed nations, and was higher among second-line versus frontline workers. Incorporation of HRV based ML model predicted burnout among HCWs with a good accuracy.

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.

There is no large contemporary data from India to see the prevalence of burnout in HCWs in covid era. Burnout and mental stress is associated with electrocardiographic changes detectable by artificial intelligence (AI). The present study aims to estimate the prevalence of burnout in HCWs in COVID-19 era using Mini Z-scale and to develop predictive AI model to detect burnout in HCWs in COVID-19 era. This is an observational and cross-sectional study to evaluate the presence of burnout in HCWs in academic tertiary care centres of North India in the COVID-19 era. At least 900 participants will be enrolled in this study from four leading premier government-funded/public-private centres of North India. Each study centre will be asked to recruit HCWs by approaching them through various listed ways for participation in the study. Interested participants after initial screening and meeting the eligibility criteria, will be asked to fill the questionnaire (having demographic and work related with Mini Z questionnaire) to assess burnout. The healthcare workers will include physicians at all levels of training, nursing staff and paramedical staff who are involved directly or indirectly in COVID-19 care. The analysis of the raw electrocardiogram (ECG) data and development of algorithm using convolutional neural networks (CNN) will be done by experts. In Summary, we propose that ECG data generated from the people with burnout can be utilized to develop AI-enabled model to predict the presence of stress and burnout in HCWs in COVID-19 era.

Cardiovascular disease is the leading cause of death in India. Previously it was thought to be a disease of the elderly but now an epidemiological transition is being seen with increasing incidence in the very young adults as well. Such patterns are not well studied in the South East Asian population. To study the epidemiological profile of very young (≤35 years) adults presenting with an acute myocardial infarction (AMI) in comparison with an older cohort. The present study included all patients presenting with AMI to the emergency or to the out-patient department of the G B Pant Institute of Postgraduate Medical Education and Research (GIPMER), New Delhi between January 2014 and October 2015. A total of 102 subjects ≤35 years with AMI comprised the study subjects (cases) and were compared with 104 subjects >35 years old with AMI (Controls). Most of the very young AMI patients presented with an anterior wall ST segment elevation myocardial infarction, a stable hemodynamic profile, single vessel disease and better left ventricular ejection fraction as compared with controls. Most of the patients were from urban and semi-urban areas, middle and lower middle socioeconomic status and were smokers but lacked other traditional risk factors.

Background: Incidence rate of acute myocardial infarction (MI) has increased in younger population over the years. The young patients have a different risk profile, presentation, and prognosis than the elderly. Hence, it is essential to understand the risk factors in young patients for proper treatment. Methods: Apolipoprotein E (ApoE) polymorphism and biochemicals such as total cholesterol, serum triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), lipoprotein(a), insulin, interleukin-6, homocysteine, fibrinogen, and highly sensitive C-reactive protein were investigated in very young MI (yMI patients; age <= 35 years; n = 125), in old MI (oMI patients; age >35 and < 80 years; n = 111), and healthy controls (age <= 35 years; n = 103). Results: HDL-C was significantly lower in yMI patients than in controls (p = 2.63E-04) and oMI patients (p = 1.29E-05). ApoA1 was also lowest in yMI patients, but significant only in comparison to controls (p = 2.62E.04). The yMI group had the highest ratios of total cholesterol:HDL-C (p = 0.027 in yMI patients versus controls and p = 0.018 in yMI patients versus oMI patients), LDL-C:HDL-C (p = 0.002 in yMI patients versus controls and p = 0.005 in yMI patients versus oMI patients), and ApoB:ApoAl (p = 8.75E05 in yMI patients versus controls and p > 0.05 in yMI patients versus oMI patients). No significant pattern of ApoE polymorphisms was observed. Conclusion: The lower level of HDL-C and ApoA1 and higher ratios of total cholesterol:HDL-C, LDL-C:HDL-C, and ApoB:ApoA1 are risk factors for MI in young patients. (C) 2018 Cardiological Society of India. Published by Elsevier B.V.