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

Autor: Mohit D. Gupta, Manish Kumar Jha, Ankit Bansal, Rakesh Yadav, Sivasubramanian Ramakrishanan, M.P. Girish, Prattay G. Sarkar, Arman Qamar, Suresh Kumar, Satish Kumar, Ajeet Jain, Rajni Saijpaul, Vandana Gupta, Deepankar Kansal, Sandeep Garg, Sameer Arora, P.S. Biswas, Jamal Yusuf, Rajeev K. Malhotra, Vishal Batra, Sanjeev Kathuria, Vimal Mehta, Safal, Manu Kumar Shetty, Saibal Mukhopadhyay, Sanjay Tyagi, Anubha Gupta
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Indian Heart Journal, Vol 73, Iss 6, Pp 674-681 (2021)
Druh dokumentu: article
ISSN: 0019-4832
DOI: 10.1016/j.ihj.2021.10.002
Popis: 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
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