Abstract

Acute respiratory distress (ARD) is a global health concern due to its high rates of morbidity and mortality in children. Early identification of the predictors of baseline ARD is very vital to necessitate timely interventions and improved clinical management of the condition. This study aimed at establishing a predictive model for predicting baseline ARD in African children with severe malaria. This retrospective cohort study used secondary data from ‘African Quinine-Artesunate Malaria Trial’ (AQUAMAT) that was conducted from 2005 to 2010, among children (<15 years) who had been hospitalized for severe malaria. The predictors of baseline ARD were determined using univariable and multivariable binary logistic regression models. A nomogram was constructed to visualise the predictive model. The Receiver Operating Characteristic (ROC) curve was plotted to evaluate the discriminative power of the predictive model. Classification tree analysis was done to classify patients at a higher or lower risk of developing baseline ARD. The outcome of interest was baseline ARD. The study included 5,426 children admitted with severe malaria. The multivariable binary logistic regression model revealed that the major predictors of baseline ARD were pneumonia [Odds Ratio (OR): 2.49, CI: 1.99 - 3.13, p-value <0.001], severe acidosis (OR: 2.49, CI: 2.09 - 2.97, p-value <0.001), patient is currently treated for chronic illness (OR: 2.32, CI: 1.05-5.14, p-value =0.038), hyperparasitaemia (OR: 1.96, CI: 1.21 - 3.16, p-value =0.006), sepsis (OR: 1.46, CI: 1.18 - 1.82, p-value =0.001), respiratory rate (OR: 1.03, CI: 1.03 - 1.04, p-value <0.001). The predictive model was valuable in predicting baseline ARD with overall correct classification of 68.72% and area under the ROC curve of 0.75 (95% CI: 0.73 - 0.77). Classification tree ranked pneumonia, severe acidosis, hyperparasitaemia, sepsis, respiratory rate as well as severe prostration as major conditions classifying a patient of being at high risk of developing baseline ARD. These findings will help medical practitioners in early identification of severe malaria children who are at high risk of developing baseline ARD. This will necessitate improved management and timely interventions provided to such patients in order to prevent development of baseline ARD.

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

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