A cross-sectional study on risk factors for infection with Parvovirus B19 and the association with anaemia in a febrile paediatric population in Ghana

Assessment of 17 clinically available renal biomarkers to predict acute kidney injury in critically ill patients

Background:

Systematic estimation of renal biomarkers in the intensive care unit (ICU) patients is lacking. Seventeen biomarkers were assessed to predict acute kidney injury (AKI) after admission to ICU.

Materials and methods:

A prospective, observational study was conducted in the general ICU of Guangdong Provincial People’s Hospital. Seventeen serum or urine biomarkers were studied for their abilities alone or in combination for predicting AKI and severe AKI.

Results:

Of 1498 patients, 376 (25.1%) developed AKI. Serum cystatin C (CysC) showed the best performance for predicting both AKI (area under the receiver operator characteristic curve [AUC] = 0.785, mean square error [MSE] = 0.118) and severe AKI (AUC = 0.883, MSE = 0.06). Regarding biomarkers combinations, CysC plus N-acetyl-β-d-glucosaminidase-to-creatinine ratio (NAG/Cr) was the best for predicting AKI (AUC = 0.856, MSE = 0.21). At the same time, CysC plus lactic acid (LAC) performed the best for predicting severe AKI (AUC = 0.907, MSE = 0.058). Regarding combinations of biomarkers and clinical markers, CysC plus Acute Physiology and Chronic Health Evaluation (APACHE) II score showed the best performance for predicting AKI (AUC = 0.868, MSE = 0.407). In contrast, CysC plus Multiple Organ Dysfunction Score (MODS) had the highest predictive ability for severe AKI (AUC = 0.912, MSE = 0.488).

Conclusion:

Apart from CysC, the combination of most clinically available biomarkers or clinical markers does not significantly improve the forecasting ability, and the cost–benefit ratio is not economical.