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Postoperative biomarkers predict acute kidney injury and poor outcomes after adult cardiac surgery.

ABSTRACT: Acute kidney injury (AKI) is a frequent complication of cardiac surgery and increases morbidity and mortality. The identification of reliable biomarkers that allow earlier diagnosis of AKI in the postoperative period may increase the success of therapeutic interventions. Here, we conducted a prospective, multicenter cohort study involving 1219 adults undergoing cardiac surgery to evaluate whether early postoperative measures of urine IL-18, urine neutrophil gelatinase-associated lipocalin (NGAL), or plasma NGAL could identify which patients would develop AKI and other adverse patient outcomes. Urine IL-18 and urine and plasma NGAL levels peaked within 6 hours after surgery. After multivariable adjustment, the highest quintiles of urine IL-18 and plasma NGAL associated with 6.8-fold and 5-fold higher odds of AKI, respectively, compared with the lowest quintiles. Elevated urine IL-18 and urine and plasma NGAL levels associated with longer length of hospital stay, longer intensive care unit stay, and higher risk for dialysis or death. The clinical prediction model for AKI had an area under the receiver-operating characteristic curve (AUC) of 0.69. Urine IL-18 and plasma NGAL significantly improved the AUC to 0.76 and 0.75, respectively. Urine IL-18 and plasma NGAL significantly improved risk prediction over the clinical models alone as measured by net reclassification improvement (NRI) and integrated discrimination improvement (IDI). In conclusion, urine IL-18, urine NGAL, and plasma NGAL associate with subsequent AKI and poor outcomes among adults undergoing cardiac surgery.

SUBMITTER: Parikh CR

PROVIDER: S-EPMC3171945 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Postoperative biomarkers predict acute kidney injury and poor outcomes after adult cardiac surgery.

Parikh Chirag R CR Coca Steven G SG Thiessen-Philbrook Heather H Shlipak Michael G MG Koyner Jay L JL Wang Zhu Z Edelstein Charles L CL Devarajan Prasad P Patel Uptal D UD Zappitelli Michael M Krawczeski Catherine D CD Passik Cary S CS Swaminathan Madhav M Garg Amit X AX

Journal of the American Society of Nephrology : JASN 20110811 9

Acute kidney injury (AKI) is a frequent complication of cardiac surgery and increases morbidity and mortality. The identification of reliable biomarkers that allow earlier diagnosis of AKI in the postoperative period may increase the success of therapeutic interventions. Here, we conducted a prospective, multicenter cohort study involving 1219 adults undergoing cardiac surgery to evaluate whether early postoperative measures of urine IL-18, urine neutrophil gelatinase-associated lipocalin (NGAL) ...[more]

PMID: 21836143

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Project description:PurposeTo establish novel prediction models for predicting acute kidney injury (AKI) after cardiac surgery based on early postoperative biomarkers.Patients and methodsThis study enrolled patients who underwent cardiac surgery in a Chinese tertiary cardiac center and consisted of a discovery cohort (n = 452, from November 2018 to June 2019) and a validation cohort (n = 326, from December 2019 to May 2020). 43 biomarkers were screened using the least absolute shrinkage and selection operator and logistic regression to construct a nomogram model. Three tree-based machine learning models were also established: eXtreme Gradient Boosting (XGBoost), random forest (RF) and deep forest (DF). Model performance was accessed using area under the receiver operating characteristic curve (AUC). AKI was defined according to the Kidney Disease Improving Global Outcomes criteria.ResultsFive biomarkers were identified as independent predictors of AKI and were included in the nomogram: soluble ST2 (sST2), N terminal pro-brain natriuretic peptide (NT-proBNP), heart-type fatty acid binding protein (H-FABP), lactic dehydrogenase (LDH), and uric acid (UA). In the validation cohort, the nomogram achieved good discrimination, with AUC of 0.834. The machine learning models also exhibited adequate discrimination, with AUC of 0.856, 0.850, and 0.836 for DF, RF, and XGBoost, respectively. Both nomogram and machine learning models had well calibrated. The AUC of sST2, NT-proBNP, H-FABP, LDH, and UA to discriminate AKI were 0.670, 0.713, 0.725, 0.704, and 0.749, respectively. In addition, all of these biomarkers were significantly correlated with AKI after adjusting clinical confounders (odds ratio and 95% confidence interval of the third vs. the first tertile: sST2, 3.55 [2.34-5.49], NT-proBNP, 5.50 [3.54-8.71], H-FABP, 6.64 [4.11-11.06], LDH, 7.47 [4.54-12.64], and UA, 8.93 [5.46-15.06]).ConclusionOur study provides a series of novel predictive models and five biomarkers for enhancing the risk stratification of AKI after cardiac surgery.

Project description:ObjectiveConventional ultrafiltration (CUF) during cardiopulmonary bypass (CPB) serves to hemoconcentrate blood volume to avoid allogeneic blood transfusions. Previous studies have determined CUF volumes as a continuous variable are associated with postoperative acute kidney injury (AKI) after cardiac surgery, but optimal weight-indexed volumes that predict AKI have not been described.DesignRetrospective cohort.SettingSingle-center university hospital.ParticipantsA total of 1,641 consecutive patients who underwent elective cardiac surgery between June 2013 and December 2015.InterventionsThe CUF volume was removed during CPB in all participants as part of routine practice. The authors investigated the association of dichotomized weight-indexed CUF volume removal with postoperative AKI development to provide pragmatic guidance for clinical practice at the authors' institution.Measurements and main resultsPrimary outcomes of postoperative AKI were defined by the Kidney Disease: Improving Global Outcomes staging criteria and dichotomized, weight-indexed CUF volumes (mL/kg) were defined by (1) extreme quartiles (<Q1 v >Q3) and (2) Youden's criterion that best predicted AKI development. Multivariate logistic regression models were developed to test the association of these dichotomized indices with AKI status. Postoperative AKI occurred in 827 patients (50.4%). Higher CUF volumes were associated with AKI development by quartiles (CUF >Q3 = 32.6 v CUF < Q1 = 10.4 mL/kg; odds ratio [OR] = 1.68, 95% CI: 1.19-2.3) and Youden's criterion (CUF ≥ 32.9 v CUF <32.9 mL/kg; OR = 1.60, 95% CI: 1.21-2.13). Despite similar intraoperative nadir hematocrits among groups (p = 0.8), higher CUF volumes were associated with more allogeneic blood transfusions (p = 0.002) and longer lengths of stay (p < 0.001).ConclusionsRemoval of weight-indexed CUF volumes > 32 mL/kg increased the risk for postoperative AKI development. Importantly, CUF volume removal of any amount did not mitigate allogeneic blood transfusion during elective cardiac surgery. Prospective studies are needed to validate these findings.

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Postoperative biomarkers predict acute kidney injury and poor outcomes after adult cardiac surgery.

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Postoperative biomarkers predict acute kidney injury and poor outcomes after adult cardiac surgery.

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