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 ba...
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2021
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oai:doaj.org-article:b3b70a454f3644a1bc46789bb61656c42021-12-02T04:58:00ZCOVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study0019-483210.1016/j.ihj.2021.10.002https://doaj.org/article/b3b70a454f3644a1bc46789bb61656c42021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0019483221002212https://doaj.org/toc/0019-4832Objectives: 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.Mohit D. GuptaManish Kumar JhaAnkit BansalRakesh YadavSivasubramanian RamakrishananM.P. GirishPrattay G. SarkarArman QamarSuresh KumarSatish KumarAjeet JainRajni SaijpaulVandana GuptaDeepankar KansalSandeep GargSameer AroraP.S. BiswasJamal YusufRajeev K. MalhotraVishal BatraSanjeev KathuriaVimal Mehta SafalManu Kumar ShettySaibal MukhopadhyaySanjay TyagiAnubha GuptaElsevierarticleBurnoutStressCOVID-19Heart rate variabilityMachine learningHealth care workerSurgeryRD1-811Diseases of the circulatory (Cardiovascular) systemRC666-701ENIndian Heart Journal, Vol 73, Iss 6, Pp 674-681 (2021) |
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DOAJ |
| collection |
DOAJ |
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EN |
| topic |
Burnout Stress COVID-19 Heart rate variability Machine learning Health care worker Surgery RD1-811 Diseases of the circulatory (Cardiovascular) system RC666-701 |
| spellingShingle |
Burnout Stress COVID-19 Heart rate variability Machine learning Health care worker Surgery RD1-811 Diseases of the circulatory (Cardiovascular) system RC666-701 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 COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study |
| description |
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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Mohit D. Gupta |
| title |
COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study |
| title_short |
COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study |
| title_full |
COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study |
| title_fullStr |
COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study |
| title_full_unstemmed |
COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study |
| title_sort |
covid 19-related burnout among healthcare workers in india and ecg based predictive machine learning model: insights from the brucee- li study |
| publisher |
Elsevier |
| publishDate |
2021 |
| url |
https://doaj.org/article/b3b70a454f3644a1bc46789bb61656c4 |
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