Clinical, immune and genetic risk factors of malaria-associated acute kidney injury in Zambian Children: A study protocol

Author:

Mwaba ChisamboORCID,Munsaka SodyORCID,Mwakazanga David,Rutagwerae DavidORCID,Ngalamika Owen,Mwanza Suzanna,McCulloch Mignon,Mpabalwani Evans

Abstract

AbstractBackgroundAcute kidney injury (AKI) affects nearly half of children with severe malaria and increases the risk of adverse outcomes such as death and poor cognitive function. The pathogenesis and predictors of malaria-associated acute kidney injury (MAKI) are not fully described. This study aims to determine the clinical, immune, and genetic correlates of risk to AKI in Zambian children admitted with malaria. In addition, we intend to assess a modified renal angina index (mRAI), kidney injury molecule – 1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL)) and soluble urokinase receptor (suPAR), when done on the first day of admission, for ability to predict AKI 48 hours later (day 3) in children admitted with malaria.MethodsThis is an unmatched case-control study, with a case to control ratio of 1:1, in which 380 children with malaria and aged less than 16 years are being recruited from two hospitals in Zambia. Eligible children are recruited after obtaining written informed consent. Recruitment began 6thMarch,2024 and will continue until July 2025. AKI is defined using the 2012 KIDGO AKI creatinine criteria and cases are defined as children admitted with malaria who develop AKI within 72 hours of admission while controls are children admitted with malaria but with no AKI. Serum creatinine is collected on day 1 within 24hours of admission and then again on Day 3 post admission. Baseline biomarker concentrations will be determined using the Luminex multiplex Elisa system or high-sensitivity ELISA. SPSS version 29 will be used for data analysis. Descriptive statistics and inferential statistical tests will be run as appropriate. A p ≤ 0.05 will be considered as significant. The utility of the renal angina score for predicting MAKI will be assessed using sensitivity, specificity, and estimates of the area under the curve (AUC).

Publisher

Cold Spring Harbor Laboratory

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