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.

Effect of government-issued state of emergency and reopening orders on cardiovascular hospitalizations during the COVID-19 pandemic.

OBJECTIVE: Little is known about the effect of government-issued State of Emergency (SOE) and Reopening orders on health care behaviors. We aimed to determine the effect of SOE and Phase 1 of Reopening orders on hospitalizations for acute myocardial infarction (AMI) or acute decompensated heart failure (ADHF). METHODS: Hospitalizations for AMI and ADHF in the UNC Health system, which includes 10 hospitals in both urban and rural counties, were identified. An interrupted time series design was used to compare weekly hospitalization rates for eight weeks before the March 10th SOE declaration, eight weeks between the SOE order and Phase 1 of Reopening order, and the subsequent eight weeks. RESULTS: Overall, 3,792 hospitalizations for AMI and 7,223 for ADHF were identified. Rates before March 10th were stable. AMI/ADHF hospitalizations declined about 6% per week in both urban and rural hospitals from March 11th to May 5th. Larger declines in hospitalizations were seen in adults ≥65 years old (-8% per week), women (-7% per week), and White individuals (-6% per week). After the Reopening order, AMI/ADHF hospitalizations increased by 8% per week in urban centers and 9% per week in rural centers, including a significant increase in each demographic group. The decline and rebound in acute CV hospitalizations were most pronounced in the two weeks following the government orders. CONCLUSIONS: AMI and ADHF hospitalization rates closely correlated to SOE and Reopening orders. These data highlight the impact of public health measures on individuals seeking care for essential services; future policies may benefit from clarity regarding when individuals should present for care.

Management of STEMI during the COVID-19 pandemic: Lessons learned in 2020 to prepare for 2021.

As the prevalence of asymptomatic COVID-19 continues to increase, there is an increasing possibility that patients with COVID-19 may presen with ST-segment elevation myocardial infarction (STEMI). With social distancing and restricted access to preventive healthcare and emergency services, the management of acute cardiac emergencies such as myocardial infarction has suffered collateral damage. Thus far, global trends suggest a decrease in STEMI activations with possible worse outcomes due to delayed presentation and management. In this review, we discuss the challenges to STEMI management in the COVID-19 era and provide potential solutions for adherence to evidence-based therapies as the pandemic progresses into the year 2021.