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.

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.

Hypoxia is a major cause of pulmonary hypertension (PH) worldwide, and it is likely that interstitial pulmonary macrophages contribute to this vascular pathology. We observed in hypoxia-exposed mice an increase in resident interstitial macrophages, which expanded through proliferation and expressed the monocyte recruitment ligand CCL2. We also observed an increase in CCR2 ⁺ macrophages through recruitment, which express the protein thrombospondin-1, which functionally activates TGF-β to cause vascular disease. Blockade of monocyte recruitment with either CCL2-neutralizing antibody treatment or CCR2 deficiency in the bone marrow compartment suppressed hypoxic PH. These data were supported by analysis of plasma samples from humans who traveled from low (225 m) to high (3500 m) elevation, revealing an increase in thrombospondin-1 and TGF-β expression following ascent, which was blocked by dexamethasone prophylaxis. In the hypoxic mouse model, dexamethasone prophylaxis recapitulated these findings by mechanistically suppressing CCL2 expression and CCR2 ⁺ monocyte recruitment. These data suggest a pathologic cross talk between 2 discrete interstitial macrophage populations, which can be therapeutically targeted.

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.