Role of cardiovascular magnetic resonance imaging in COVID-19 recovered patients: A short-term follow-up study.

OBJECTIVE: Cardiac involvement in recovered COVID-19 patients assessed by cardiac magnetic resonance imaging (MRI). METHODS: Subjects recently recovered from COVID-19 and with an abnormal left ventricular global longitudinal strain were enrolled. Cardiac MRI in all the enrolled subjects was done at baseline (within 30-90 days following recovery from COVID-19) with a follow-up scan at 6 months in individuals with an abnormal baseline scan. Additionally, 20 age-and sex-matched individuals were enrolled as healthy controls (HCs). RESULTS: All the 30 enrolled subjects were symptomatic during active COVID-19 disease and were categorized as mild: 11 (36.7%), moderate: 6 (20%), and severe: 13 (43.3%). Of the 30 patients, 16 (53.3%) had abnormal CMR findings. Myocardial edema was reported in 12 (40%) patients while 10 (33.3%) had late gadolinium enhancement (LGE). No difference was observed in terms of conventional left ventricular (LV) parameters; however, COVID-19-recovered patients had significantly lower right ventricular (RV) ejection fraction, RV stroke volume, and RV cardiac index compared to HCs. Follow-up scan was abnormal in 4/16 (25%) with LGE persisting in three patients (who had severe COVID-19 [3/4;75%]). Subjects with severe COVID-19 had a greater frequency of LGE (53.8%) and myocardial edema (61.5%) as compared to mild and moderate cases. Myocardial T1 (1284 ± 43.8 ms vs. 1147.6 ± 68.4 ms; p < .0001) and T2 values (50.8 ± 16.7 ms vs. 42.6 ± 3.6 ms; p = .04) were significantly higher in post COVID-19 subjects compared to HCs. Similarly, T1 and T2 values of severe COVID-19 patients were significantly higher compared to mild and moderate cases. CONCLUSIONS: An abnormal CMR was seen in half of the recovered patients with persistent abnormality in one-fourth at 6 months. Our study suggests a need for closer follow-up among recovered subjects in order to evaluate for long-term cardiovascular sequelae. COVID-19 causes structural changes in the myocardium in a small segment of patients with partial spontaneous resolution.

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.

COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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%) CONCLUSION: 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.

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.

Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study.

BACKGROUND: 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). OBJECTIVE: 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. METHODS: 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. CONCLUSIONS: 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.

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.