Abstract

Hypoglycemia is a life-threatening complication of severe malaria in African children. Early identification of predictors of baseline hypoglycemia is crucial for timely intervention and improved clinical outcomes. This retrospective study aimed to identify predictors of baseline hypoglycemia in severe malaria African children using univariate and multivariable logistic regression, as well as a nomogram scoring system. Secondary data from the AQUAMAT study was analyzed, including the following variables: sex, age, weight, sepsis, ICU admission, respiratory distress, blood systolic and diastolic pressure, and anemia. Univariate logistic regression was first employed to identify significant predictors for the final Multivariable logistic regression model. Additionally, a nomogram scoring system was developed. The study found the following variables as predictors of baseline hypoglycemia; ICU admission, sepsis, blood transfusion, and respiratory distress. The study further rated sepsis and blood transfusion as the most important factors associated with baseline hypoglycemia. These findings have important clinical implications for risk stratification and early intervention. Further, validation through prospective studies and exploration of additional predictors are warranted to enhance predictive accuracy and inform targeted interventions. The nomogram would help medical staff obtain a risk score for hypoglycemia for a participant with a combination of demographic, clinical, and laboratory parameters.

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School	: School of Natural and Applied Sciences
Issued Date	: 2024

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