Serum and urine cystatin C are poor biomarkers for acute kidney injury and renal replacement therapy.
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ABSTRACT: Purpose
To evaluate whether cystatin C in serum (sCyC) and urine (uCyC) can predict early acute kidney injury (AKI) in a mixed heterogeneous intensive care unit (ICU), and also whether these biomarkers can predict the need for renal replacement therapy (RRT).Methods
Multicenter prospective observational cohort study in patients ≥18 years old and with expected ICU stay ≥72 h. The RIFLE class for AKI was calculated daily, while sCyC and uCyC were determined on days 0, 1, and alternate days until ICU discharge. Test characteristics were calculated to assess the diagnostic performance of CyC.Results
One hundred fifty-one patients were studied, and three groups were defined: group 0 (N = 60), non-AKI; group 1 (N = 35), AKI after admission; and group 2 (N = 56), AKI at admission. We compared the two days prior to developing AKI from group 1 with the first two study days from group 0. On Day -2, median sCyC was significantly higher (0.93 versus 0.80 mg/L, P = 0.01), but not on Day -1 (0.98 versus 0.86 mg/L, P = 0.08). The diagnostic performance for sCyC was fair on Day -2 [area under the curve (AUC) 0.72] and poor on Day -1 (AUC 0.62). Urinary CyC had no diagnostic value on either of the two days prior to AKI (AUC <0.50). RRT was started in 14 patients with AKI; sCyC and uCyC determined on Day 0 were poor predictors for the need for RRT (AUC ≤0.66).Conclusions
In this study, sCyC and uCyC were poor biomarkers for prediction of AKI and the need for RRT.
SUBMITTER: Royakkers AA
PROVIDER: S-EPMC3042095 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Electronic health records (EHR) detect the onset of acute kidney injury (AKI) in hospitalized patients, and may identify those at highest risk of mortality and renal replacement therapy (RRT), for earlier targeted intervention. METHODS:Prospective observational study to derive prediction models for hospital mortality and RRT, in inpatients aged ?18?years with AKI detected by EHR over 1 year in a tertiary institution, fulfilling modified KDIGO criterion based on serial serum creatinine (sCr) measures. RESULTS:We studied 3333 patients with AKI, of 77,873 unique patient admissions, giving an AKI incidence of 4%. KDIGO AKI stages at detection were 1(74%), 2(15%), 3(10%); corresponding peak AKI staging in hospital were 61, 20, 19%. 392 patients (12%) died, and 174 (5%) received RRT. Multivariate logistic regression identified AKI onset in ICU, haematological malignancy, higher delta sCr (sCr rise from AKI detection till peak), higher serum potassium and baseline eGFR, as independent predictors of both mortality and RRT. Additionally, older age, higher serum urea, pneumonia and intraabdominal infections, acute cardiac diseases, solid organ malignancy, cerebrovascular disease, current need for RRT and admission under a medical specialty predicted mortality. The AUROC for RRT prediction was 0.94, averaging 0.93 after 10-fold cross-validation. Corresponding AUROC for mortality prediction was 0.9 and 0.9 after validation. Decision tree analysis for RRT prediction achieved a balanced accuracy of 70.4%, and identified delta-sCr???148??mol/L as the key factor that predicted RRT. CONCLUSION:Case fatality was high with significant renal deterioration following hospital-wide AKI. EHR clinical model was highly accurate for both RRT prediction and for mortality; allowing excellent risk-stratification with potential for real-time deployment.
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