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

Indian Heart Journal

Manish Kumar Jha¹  M.P. Girish²  Vishal Batra²  Sivasubramanian Ramakrishanan²  Ajeet Jain³  Ankit Bansal⁴  P.S. Biswas⁵  Prattay G. Sarkar⁶  Vimal Mehta⁶  Jamal Yusuf⁶  Mohit D. Gupta⁶  Rajeev K. Malhotra⁶  Sanjay Tyagi⁶  Manu Kumar Shetty⁶  Rakesh Yadav⁶  Anubha Gupta⁶  Safal⁶  Sanjeev Kathuria⁶  Deepankar Kansal⁷  Sandeep Garg⁷  Sameer Arora⁸  Satish Kumar⁸  Saibal Mukhopadhyay⁹  Vandana Gupta⁹  Arman Qamar^1,10  Rajni Saijpaul^1,11  Suresh Kumar^1,12 

[1] Corresponding author. Academic Block, Department of Cardiology, GB Pant Hospital, New Delhi, 110002, India.;All India Institute of Medical Sciences, New Delhi, India;Bokaro General Hospital, Bokaro Steel City, Jharkhand, India;Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA;Division of Cardiology, University of North Carolina, Chapel Hill, NC, USA;GB Pant Institute of Post Graduate Education and Research, New Delhi, India;Indraprastha Institute of Information Technology, Delhi, India;Lok Nayak Hospital, New Delhi, India;Maulana Azad Medical College, New Delhi, India;Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India;Rajiv Gandhi Super Specialty Hospital, Tahirpur, Delhi, India;Section of Interventional Cardiology and Vascular Medicine, NorthShore University Health System, University of Chicago Pritzker School of Medicine, Evanston, IL, USA;

关键词: Burnout;    Stress;    COVID-19;    Heart rate variability;    Machine learning;    Health care worker;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown