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.

No abstract

No abstract

Serum protein electrophoresis (SPEP) is a method by which proteins present in serum are separated into different fractions based on their molecular weight and electric charge. Presence of M spike, composed of monoclonal protein, on electrophoretogram is a characteristic finding that can be seen in monoclonal gammopathies like multiple myeloma. M spike is most commonly seen in the gamma region however, the M-spike can be observed in fraction other than the Y fraction as well i.e. in the beta region and rarely alpha region. Here we have enumerated few cases where M protein has been seen in fractions other than the gamma region. Thus one needs to be cautious about the variable appearance of M-spike during interpretation of SPEP as some physiological proteins if elevated can also give rise to similar spike sometimes referred as pseudo monoclonal pattern.