Introduction: The Omicron variant is the present predominant COVID-19 strain worldwide. Accurate mortality prediction can facilitate risk stratification and targeted therapies. The study aimed to evaluate the feasibility of the difference in hematocrit and albumin (HCT-ALB) levels, alone or combined with the pediatric Sequential Organ Failure Assessment (pSOFA) score and lactate level, to predict the in-hospital mortality of COVID-19 Omicron variant-infected pediatric patients.
Methods: A multicenter retrospective cohort study was performed for children with COVID-19 Omicron variant infection between December 2021 and January 2022. The demographics, clinical characteristics, hospital admission laboratory test results, and treatments were recorded. The in-hospital mortality was documented. The associations between HCT-ALB levels and mortality, and between HCT-ALB+pSOFA+lactate and mortality were analyzed.
Results: A total of 119 children were included. The median age was 1.6 (interquartile range: 0.5-6.2) years old. There were 70 boys and 49 girls. The mortality rate was 14.3% (17/119). The univariate and multivariate Cox regression analysis revealed that HCT-ALB was associated to in-hospital mortality (hazard ratio: 1.500, 95% confidence interval: 1.235-1.822, <0.001). The receiver operating characteristic curve analysis revealed that HCT-ALB can be used to accurately predict in-hospital mortality at a cut-off value of -0.7 (area under the curve: 0.888, sensitivity: 0.882, specificity: 0.225, Youden index: 0.657, <0.001). These patients were assigned into three groups based on the HCT-ALB level, pSOFA score, and lactate level (low-, medium-, and high-risk groups). The Kaplan-Meier analysis revealed that the mortality increased in the high-risk group, when compared to the medium-risk group (<0.01). The latter group had a higher mortality, when compared to the low-risk group (<0.01).
Conclusion: The HCT-ALB level can be applied to predict the in-hospital mortality of children infected with the COVID-19 Omicron variant. Its combination with other variables can improve prediction performance.
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http://dx.doi.org/10.2147/IDR.S479580 | DOI Listing |
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Division of Pulmonary, Allergy, and Critical Care (G. L. A.), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA; the Division of Pulmonary and Critical Care Medicine (S. M. S.), University of Rochester Medical Center, Rochester, NY; the Department of Anaesthesia and Critical Care (A. R., Z. F., and M. T. D. S.), Greys Hospital, KwaZulu-Natal Department of Health, the Department of Anaesthesia and Critical Care (J. I.), Harry Gwala Regional Hospital, KwaZulu-Natal Department of Health, Pietermaritzburg, the Department of Anaesthesia and Critical Care (R. D. W. and M. T. D. S.), School of Clinical Medicine, University of KwaZulu-Natal, Durban, South Africa; the Faculty Medicine and Pharmacy (R. D. W.), Vrije Universiteit Brussel (VUB), Brussels, Belgium; and the Department of Intensive Care (R. D. W.), John Radcliffe Hospital, Oxford University Trust Hospitals, Oxford, England.
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Division of Critical Care and Pulmonary Medicine, Department of Pediatrics, St Jude Children's Research Hospital, Memphis, TN, United States.
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