AUTHOR=Sathyadas Anil , Reghukumar Aravind , Anish Thekkumkara Surendran , Libu Gnanaseelan Kanakamma , Kiran Vishnu Narayan , Prajitha Kannamkottapilly Chandrasekharan , Sharahudeen Anisha , Chandran Dhanusha , Athirarani Muralidharan Rohini , Sindhu L. , Sona Parackalparambil Sukumaran , Sreekanth Thekkumkara Prabhakaran , Chintha Sujatha , Iype Thomas , Suresh Muthezhathu Kesavadas , Ravi Kavitha , Rajamohanan K. , Mathew Thomas , Panicker John , Nair M. K. C. , Nizarudeen A. , Hari Parameswaran 

TITLE=Predictors of mortality among critically ill SARS-CoV-2 infected patients—a retrospective cohort study, Kerala, India

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1635476

DOI=10.3389/fpubh.2025.1635476

ISSN=2296-2565

ABSTRACT=BackgroundPreventing the in-hospital mortality of critically ill patient is the last opportunity to saves lives during a pandemic. It was a need for the hospital settings of global south to further prioritize the individuals in this vulnerable group to allocate scares resources because of large numbers of such patients admitted in hospitals during pandemics. We, in this study flag the risk factors for in-hospital mortality for critically ill patients at the time of a pandemic like COVID-19.MethodsThis retrospective cohort study aimed to analyze the in-hospital mortality rate and predictors of mortality of patients with critically ill SARS-CoV-2 infection admitted to a Level 3 multi-disciplinary intensive care unit in India from15th September 2020 to 31st March 2021. We compared the incidence proportion of in-hospital mortality in different subgroups. We calculated the relative risk (RR) of clinical and biochemical factors under study for mortality outcome. We used principal component analysis to identify risky groups because the mortality predictors were found to have been highly correlated with one another in univariable analyses.FindingsOf the 431 adult study participants with a median (IQR) age of 48 (34–60) years, 26.2% (n = 113) were aged 60 years or above, and 58.9% (n = 254) were men. Significant predictors of mortality in patients with severe SARS-CoV-2 infection were; age more than 60 years [RR 1.67 (1.36–2.02), p < 0.001], chronic kidney disease [RR 1.7 (1.01–3.14), p = 0.022], systemic arterial hypertension [RR 1.69 (1.32–2.15), p < 0.001], diabetes mellitus [RR 1.22 (1.00–1.49), p = 0.042], coronary artery disease [RR 1.59 (1.03–2.43), p = 0.012], any malignancy [RR 2.79 (1.17–6.65), p = 0.020], SARS-CoV-2 unvaccinated status [RR 1.59 (1.33–2.22), p = 0.008] COVID ARDS [RR 5.34 (2.54–11.25), p < 0.001], COVID Bronchopneumonia [RR 1.16 (1.03–1.31) p = 0.017], sepsis [RR 4.28 (1.76–10.38) p = 0.001], septic shock [RR 25.65 (3.48–189) p = 0.002], acute kidney injury [RR 10.59 (3.25–34.45) p < 0.001] and infection-related ventilator-associated condition (IVAC) [RR 2.13 (1.43–3.17) p < 0.001].InterpretationRenal insufficiency, transaminitis, coronary artery disease and elevated inflammatory markers, comorbidities and lack of vaccination, Pneumonia, Breathlessness and ARDS, sepsis and septic shock, cough, and diarrhea at the time of admission were identified as nine domains/variables that contributed to mortality. It is relevant in the clinical setting of LMICs (low- and middle-income countries) with limited healthcare resources. These predictors would help in prognostication of the disease and guide in rationalizing the management of patients in the context of pandemic threats.