SARS-CoV-2 Variants of Concern and Clinical Severity in the Mexican Pediatric Population.
Anahí Maldonado-CabreraJesus Alejandro Colin-VilchisUbydul HaqueCarlos VelazquezAndrea Socorro Alvarez VillaseñorLuis Eduardo Magdaleno-MárquezCarlos Iván Calleros-MuñozKaren Fernanda Figueroa-EnríquezAracely Angulo-MolinaAna Lucía Gallego-HernándezPublished in: Infectious disease reports (2023)
The emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants of concern (VOCs) presents global heterogeneity, and their relative effect on pediatric severity is still limited. In this study, we associate VOCs with pediatric clinical severity outcomes in Mexico. Bioinformatics methods were used to characterize VOCs and single amino acid (aa) mutations in 75,348 SARS-CoV-2 genetic sequences from February 2020 to October 2022. High-predominance VOCs groups were calculated and subsequently associated with 372,989 COVID-19 clinical pediatric outcomes. We identified 21 high-frequency mutations related to Omicron lineages with an increased prevalence in pediatric sequences compared to adults. Alpha and the other lineages had a significant increase in case fatality rate (CFR), intensive critical unit (ICU) admission, and automated mechanical ventilation (AMV). Furthermore, a logistic model with age-adjusted variables estimated an increased risk of hospitalization, ICU/AMV, and death in Gamma and Alpha, in contrast to the other lineages. We found that, regardless of the VOCs lineage, infant patients presented the worst severity prognoses. Our findings improve the understanding of the impact of VOCs on pediatric patients across time, regions, and clinical outcomes. Enhanced understanding of the pediatric severity for VOCs would enable the development and improvement of public health strategies worldwide.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- mechanical ventilation
- high frequency
- public health
- intensive care unit
- coronavirus disease
- acute respiratory distress syndrome
- emergency department
- end stage renal disease
- magnetic resonance
- machine learning
- chronic kidney disease
- transcranial magnetic stimulation
- amino acid
- type diabetes
- deep learning
- ejection fraction
- computed tomography
- risk factors
- metabolic syndrome
- dna methylation
- adipose tissue
- skeletal muscle
- drug induced