Abstract
Background: Hypoxic-ischemic encephalopathy (HIE) often necessitates admission to neonatal intensive care units (NICUs) for specialized care.– Among the various complications associated with HIE, hypoglycemia is a notable concern due to its potential to exacerbate neurological injury and compromise neurodevelopmental outcomes. However, the frequency of hypoglycemia in neonates with HIE, particularly in resource-limited settings like Pakistan, remains poorly characterized. Objectives: Aim of this study is to determine the frequency of hypoglycemia in neonates with HIE admitted to the NICU of Sir Ganga Ram Hospital, Lahore, Pakistan. By systematically reviewing medical records, this study is aimed to describe the prevalence of hypoglycemia in this population.
Methods: Medical records of neonates diagnosed with HIE and admitted to the NICU were reviewed from July 2023 to October 2023. Data on demographic characteristics, perinatal history, clinical presentation, laboratory investigations including blood glucose levels, and neurodevelopmental outcomes were extracted. The frequency of hypoglycemia was determined as the proportion of neonates with documented hypoglycemia among all neonates diagnosed with HIE during the study period. Results: Among the cohort of neonates diagnosed with HIE (n = 120), 20.8% were found to experience hypoglycemic episodes during their hospital stay. Subgroup analyses revealed associations between hypoglycemia and variables such as gestational age, birth weight, and severity of HIE. Descriptive statistics were used to summarize the demographic characteristics of this population, providing context for understanding the prevalence and implications of hypoglycemia in neonates with HIE. Conclusion: This study highlights the prevalence of hypoglycemia as a common metabolic disturbance in neonates with HIE, underscoring the need for vigilant monitoring and prompt intervention in this vulnerable population. Standardized protocols for managing hypoglycemia in neonates with HIE are imperative to optimize clinical outcomes.