Ambient Fine Particulate Matter and COVID-19 in India

We examine the correlation between COVID-19 case activity and air pollution in two cities of Delhi and Mumbai in India. Data regarding air quality index (AQI) of PM2.5 and PM10 from Delhi and Mumbai were collected between July and November 2020. Within the same time period, confirmed cases and daily deaths due to COVID-19 in these two cities were also recorded. AQI levels in Delhi were worst in November (PM2.5: 446 ± 144.6 µg/m3;PM10: 318 ± 131.7 µg/m3) and were significantly higher as compared to Mumbai (PM2.5: 130 ± 41.2 µg/m3;PM10: 86 ± 21.2 µg/m3). This correlated with greater number of cases and higher mortality in Delhi (cases: 6243;deaths: 85) relative to Mumbai (cases: 1526;deaths: 35) during the same time period. This observational study shows that air pollution is associated with poor outcomes in patients with COVID-19. There is an urgent unmet need for appropriate public health measures to decrease air pollution along with strict policy change. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

X-ECGNet: An Interpretable DL model for Stress Detection using ECG in COVID-19 Healthcare Workers

COVID-19 pandemic erupted in December 2019, spreading extremely fast and stretching the healthcare infras-tructure of most countries beyond their capacities. This impacted the healthcare workers (HCW) adversely because 1) they were pressured to work almost round the clock without a break;2) they were in close contact with the COVID-19 patients and hence, were at high risk;and 3) they suffered from the fear of spreading COVID to their families. Hence, many HCWs were stressed and burnout. It is known that stress directly affects the heart and can lead to serious cardiovascular problems. Currently, stress is measured subjectively via self-declared questionnaires. Objective markers of stress are required to ascertain the quantitative impact of stress on the heart. Thus, this paper aims to detect stress contributing factors in HCWs and determine the changes in the ECG of stressed HCWs. We collected data from multiple hospitals in Northern India and developed a deep learning model, namely X-ECGNet, to detect stress. We also tried to add interpretability to the model using the recent method of SHAP analysis. Deployment of such models can help the government and hospital administrations timely detect stress in HCWs and make informed decisions to save systems from collapse during such calamities. © 2021 IEEE.

Interpretable AI Model-Based Predictions of ECG changes in COVID-recovered patients

COVID-19 has caused immense social and economic losses throughout the world. Subjects recovered from COVID are learned to have complications. Some studies have shown a change in the heart rate variability (HRV) in COVID-recovered subjects compared to the healthy ones. This change indicates an increased risk of heart problems among the survivors of moderate-to-severe COVID. Hence, this study is aimed at finding HRV features that get altered in COVID-recovered subjects compared to healthy subjects. Data of COVID-recovered and healthy subjects were collected from two hospitals in Delhi, India. Seven ML models have been built to classify healthy versus COVID-recovered subjects. The best-performing model was further analyzed to explore the ranking of altered heart features in COVID-recovered subjects via AI interpretability. Ranking of these features can indicate cardiovascular health status to doctors, who can provide support to the COVID-recovered subjects for timely safeguard from heart disorders. To the best of our knowledge, this is the first study with an in-depth analysis of the heart status of COVID-recovered subjects via ECG analysis. © 2021 IEEE.

Building an AI Model on ECG Data for Identifying Burnout/Stressed Healthcare Workers Involved in Covid-19 Management

An electrocardiogram (ECG) is used to monitor electrical activity of the heart. ECG data with 12 leads can help in detecting various cardiac (heart) problems. One of the significant factors that contribute to various cardiac diseases is work/personal stress. Use of various machine and deep learning approaches to analyse ECG data has yielded promising results in the field of predictive and diagnostic healthcare with less human error or bias. In our study, 10sec of 500Hz, 12-lead ECG samples were collected from the healthcare workers, who were involved directly or indirectly in taking care of COVID-19 patients. The present study was designed to determine whether Healthcare workers were stressed by using only ECG as input to a deep learning model. To the best of our knowledge, no earlier ECG based study has been carried out to identify stressed persons among the healthcare workers who are giving support to COVID-19 patients. In this study, ECG data of healthcare workers giving services to COVID-19 patients is utilized. This data was collected from four tertiary academic care centres of India. A modified version of AlexNet is utilized on this data that is able to identify a stressed healthcare worker with 99.397% accuracy and 99.411% AUC score. Successful deployment of such systems can help governments and hospital administrations make appropriate policy decisions during pandemics. © 2021 IEEE.

Heart rate variability in post-covid-19 recovered subjects using machine learning

Introduction: A significant proportion of patients recovering from COVID-19 infection experience symptoms attributable to autonomic cardiovascular dysregulation. Heart rate variability (HRV) is a non-invasive marker of cardiovascular dysautonomia. Machine learning (ML) models based on HRV can be used to identify post COVID-19 patients with autonomic dysfunction. Methods: We evaluated HRV and blood pressure (BP) responses to orthostatic stress (3-min active standing) in 92 patients within 30-45 days of recovery from COVID-19 infection and 120 healthy controls. HRV was evaluated based on 12-lead electrocardiogram over a 60 second period during supine paced breathing. Lead II was used to extract ECG features including (a) average RR interval, (b) R wave height, (c) Heart Rate (HR) standard deviation and (d) HRV root mean square [HRVRMS]. We also assed for (1) orthostatic hypotension (OH;>20/10 mmHg fall in BP) and (2) postural orthostatic tachycardia syndrome (POTS;HR increase >30 bpm without OH). Using ML, eleven candidate features were tested with eight algorithms (logistic regression, RandomForests, CatBoost, XGBoost, Extra-tree classifier, Multiple Perceptron (ANN), Support Vector Machines and AdaBoost Classifier) to distinguish between COVID-19 recovered and healthy controls. Results: HRV was significantly lower in post COVID-19 recovered subjects as compared to healthy controls (6.25+4.9 ms vs 9.8+8.9 ms;P<0.001). OH was reported in 12 patients (13.1%) while two patients (2.2%) had POTS. Patients with OH had a significantly lower HRV as compared to those without OH (3.29+3.16 ms vs 6.69+5.01 ms;P=0.025). Accuracy of various ML models varied between 67-80% with multiple perceptron being top model [80% weighted accuracy, AUC: 79.8%, Matthews's correlation coefficient: 0.59]. Permutation importance feature ranking showed HRV, Average RR and HR to be top feature that distinguish between COVID-19 recovered and healthy controls. Conclusions: A significant proportion of COVID-19 recovered patients experienced autonomic dysfunction as evident by lower HRV and presence of OH and POTS. ML model can help in early identification of autonomic dysfunction thereby leading to proper management in these patients.

Subclinical left ventricular dysfunction in covid-19 recovered patients using speckle tracking echocardiography

Introduction: 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 studied 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 subjects. Results: Of the 134 subjects, 121 (90.3%) were symptomatic during COVID-19 illness and were categorized into mild: 61 (45.5%), moderate: 50 (37.3%) and severe: 10 (7.5%) COVID-19 illness groups. 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 high interleukin-6 (P < 0.0001), C-reactive protein (P = 0.001), lactate dehydrogenase (P = 0.009) and serum ferritin (P = 0.03) 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 of close follow-up among COVID-19 recovered subjects to elucidate long-term cardiovascular outcomes.

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.

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.