Disease Spectrum and Predictors of Prolonged Hospitalization in Lebanese Children With COVID-19

黎巴嫩儿童新冠肺炎疾病谱及延长住院时间的预测因素

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Abstract

Background While pediatric COVID-19 is typically mild, severe outcomes such as multisystem inflammatory syndrome in children (MIS-C) can occur. Data on hospitalized children in Lebanon are scarce. This study aimed to describe the clinical features of pediatric COVID-19 hospitalizations and identify factors associated with prolonged length of stay (LOS). Methodology A retrospective, observational study was conducted at Sheikh Ragheb Harb University Hospital among 127 patients aged <18 years hospitalized with polymerase chain reaction-confirmed COVID-19 between January and December 2021. Data on demographics, clinical presentation, laboratory/radiological findings, treatment, and outcomes were collected from medical records. Bivariate and multivariable analyses were performed to identify factors associated with LOS. Results The median age was 0.6 years (interquartile range (IQR) = 0.25-1.90), and 84 (66.1%) were male. Fever, 91 (71.7%), and cough, 66 (52.0%), were the most common symptoms. Intensive care unit (ICU) admission and oxygen therapy were required in five (3.9%) cases. Overall, one (0.8%) patient developed MIS-C. There was no mortality. The median LOS was three days (IQR = 2-4). On bivariate analysis, consolidation on imaging, oxygen requirement, ICU admission, and use of antibiotics and steroids were significantly associated with longer LOS (p < 0.001 for all). On multivariable analysis, steroid use (B = 2.94, p = 0.001), pulmonary consolidation (B = 2.53, p = 0.003), and antibiotic use (B = 1.66, p = 0.035) remained independent predictors. Conclusions Most hospitalized children with COVID-19 had mild disease. Radiological evidence of pneumonia and the need for anti-inflammatory or antimicrobial therapy were key drivers of extended hospitalization, highlighting targets for early intervention and resource planning. These findings can assist clinicians in risk stratification and optimizing bed capacity in similar resource-limited settings.

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