Introduction: Acute kidney injury (AKI) is a major cause of mortality worldwide, particularly in lowresource settings with limited diagnostic testing. Neutrophil gelatinase-associated lipocalin (NGAL) has shown promise in predicting AKI. Nested within a larger, prospective cohort study evaluating AKI incidence in admitted trauma patients, our objective was to evaluate a novel dipstick, NGALds, for the prediction of AKI in Malawi, Africa. Methods: Participants were >6 months of age. Spearman rank correlation coefficients (R) assessed NGAL categories (negative [#50 ng/ml], low risk [51À149 ng/ml], moderate risk [150À299 ng/ml], and high risk [$300 ng/ml]) for the urine NGALds dipstick and laboratory-based NGAL Test. Results: We enrolled 285 participants (one-third children). Thirteen percent developed AKI. The dipstick captured 45 of 52 participants (86.5%) with moderate-or high-risk NGAL values on laboratory-based testing (R ¼ 0.74). The dipstick had sensitivity of 44.4%, specificity of 73.5%, positive predictive value of 19.5%, and negative predictive value of 90.2% for predicting AKI. Acute kidney injury was associated with an increased risk of mortality (relative risk [RR] ¼ 3.9, 95% confidence interval [CI] ¼ 1.9À8.2), but mortality risk greatly increased among children who first had a positive ($150 ng/ml) NGALds result (RR ¼ 12.0, 95% CI ¼ 1.8À78.4). Conclusions: The NGALds dipstick performed similarly to the NGAL Test in this low-resource setting and may be a useful tool to rule out AKI. It may be even more important in predicting high mortality risk among children.
Background: Many low-resource settings have limited access to serum creatinine tests necessary for kidney disease identification. Among Malawian patients who are hospitalized after trauma, we evaluated the use of point-of-care saliva urea nitrogen (SUN) dipsticks to predict acute kidney injury (AKI). Methods: In a nested prospective cohort study, we enrolled hospitalized acute trauma patients aged ≥6 months to evaluate AKI (defined by KDIGO criteria) and the test characteristics of SUN to predict AKI.
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