Serum N-terminal pro-B-type natriuretic peptide and cystatin C for acute kidney injury detection in critically ill adults in China: a prospective, observational study

ObjectiveSerum N-terminal pro-B-type natriuretic peptide (NT-proBNP) and cystatin C (sCysC) are available clinically and beneficial in diagnosing acute kidney injury (AKI). Our purpose is to identify the performance of their combined diagnosis for AKI in critically ill patients.DesignA prospectively recruited, observational study was performed.SettingAdults admitted to the intensive care unit of a tertiary hospital in China.ParticipantsA total of 1222 critically ill patients were enrolled in the study.Main outcome measuresTo identify the performance of the combined diagnosis of serum NT-proBNP and sCysC for AKI in critically ill patients. The area under the receiver operating characteristic curve (AUC-ROC), category-free net reclassification index (NRI) and incremental discrimination improvement (IDI) were utilised for comparing the discriminative powers of a combined and single biomarker adjusted model of clinical variables enriched with NT-proBNP and sCysC for AKI.ResultsAKI was detected in 256 out of 1222 included patients (20.9%). AUC-ROC for NT-proBNP and sCysC to detect AKI had a significantly higher accuracy than any individual biomarker (p<0.05). After multivariate adjustment, a level of serum NT-proBNP >= 204 pg/mL was associated with 3.5-fold higher odds for AKI compared with those below the cut-off value. Similar results were obtained for sCysC levels (p<0.001). To detect AKI, adding NT-proBNP and sCysC to a clinical model further increased the AUC-ROC to 0.859 beyond that of the clinical model with or without sCysC (p<0.05). Moreover, the addition of these two to the clinical model significantly improved risk reclassification of AKI beyond that of the clinical model alone or with single biomarker (p<0.05), as measured by NRI and IDI.ConclusionsIn critically ill individuals, serum NT-proBNP, sCysC and clinical risk factors combination improve the discriminative power for diagnosing AKI.