Evaluation of 32 urine biomarkers to predict the progression of acute kidney injury after cardiac surgery
2013; Elsevier BV; Volume: 85; Issue: 2 Linguagem: Inglês
10.1038/ki.2013.333
ISSN1523-1755
AutoresJohn M. Arthur, Elizabeth G. Hill, Joseph L. Alge, Evelyn C. Lewis, Benjamin A. Neely, Michael G. Janech, James A. Tumlin, Lakhmir S. Chawla, Andrew Shaw,
Tópico(s)Cardiac Arrest and Resuscitation
ResumoBiomarkers for acute kidney injury (AKI) have been used to predict the progression of AKI, but a systematic comparison of the prognostic ability of each biomarker alone or in combination has not been performed. In order to assess this, we measured the concentration of 32 candidate biomarkers in the urine of 95 patients with AKIN stage 1 after cardiac surgery. Urine markers were divided into eight groups based on the putative pathophysiological mechanism they reflect. We then compared the ability of the markers alone or in combination to predict the primary outcome of worsening AKI or death (23 patients) and the secondary outcome of AKIN stage 3 or death (13 patients). IL-18 was the best predictor of both outcomes (AUC of 0.74 and 0.89). L-FABP (AUC of 0.67 and 0.85), NGAL (AUC of 0.72 and 0.83), and KIM-1 (AUC of 0.73 and 0.81) were also good predictors. Correlation between most of the markers was generally related to their predictive ability, but KIM-1 had a relatively weak correlation with other markers. The combination of IL-18 and KIM-1 had a very good predictive value with an AUC of 0.93 to predict AKIN 3 or death. Thus, a combination of IL-18 and KIM-1 would result in improved identification of high-risk patients for enrollment in clinical trials. Biomarkers for acute kidney injury (AKI) have been used to predict the progression of AKI, but a systematic comparison of the prognostic ability of each biomarker alone or in combination has not been performed. In order to assess this, we measured the concentration of 32 candidate biomarkers in the urine of 95 patients with AKIN stage 1 after cardiac surgery. Urine markers were divided into eight groups based on the putative pathophysiological mechanism they reflect. We then compared the ability of the markers alone or in combination to predict the primary outcome of worsening AKI or death (23 patients) and the secondary outcome of AKIN stage 3 or death (13 patients). IL-18 was the best predictor of both outcomes (AUC of 0.74 and 0.89). L-FABP (AUC of 0.67 and 0.85), NGAL (AUC of 0.72 and 0.83), and KIM-1 (AUC of 0.73 and 0.81) were also good predictors. Correlation between most of the markers was generally related to their predictive ability, but KIM-1 had a relatively weak correlation with other markers. The combination of IL-18 and KIM-1 had a very good predictive value with an AUC of 0.93 to predict AKIN 3 or death. Thus, a combination of IL-18 and KIM-1 would result in improved identification of high-risk patients for enrollment in clinical trials. Acute kidney injury (AKI) is increasing in frequency1.Lameire N. Van Biesen W. Vanholder R. The changing epidemiology of acute renal failure.Nat Clin Pract Nephrol. 2006; 2: 364-377Crossref PubMed Scopus (219) Google Scholar and is associated with a high incidence of adverse outcomes.2.Hoste E.A. Kellum J.A. RIFLE criteria provide robust assessment of kidney dysfunction and correlate with hospital mortality.Crit Care Med. 2006; 34: 2016-2017Crossref PubMed Scopus (69) Google Scholar Identification of biomarkers that diagnose or predict the magnitude of AKI after cardiac surgery has been a goal of investigators for over a decade. The most well-studied biomarkers are those that reflect an inflammatory process in AKI, such as interleukin (IL)-18,3.Melnikov V.Y. Faubel S. Siegmund B. et al.Neutrophil-independent mechanisms of caspase-1- and IL-18-mediated ischemic acute tubular necrosis in mice.J Clin Invest. 2002; 110: 1083-1091Crossref PubMed Scopus (339) Google Scholar and biomarkers that have increased tubular cell synthesis following renal injury, such as neutrophil gelatinase–associated lipocalin (NGAL)4.Mishra J. Ma Q. Prada A. et al.Identification of neutrophil gelatinase-associated lipocalin as a novel early urinary biomarker for ischemic renal injury.J Am Soc Nephrol. 2003; 14: 2534-2543Crossref PubMed Scopus (1422) Google Scholar and kidney injury molecule-1 (KIM-1).5.Han W.K. Bailly V. Abichandani R. et al.Kidney Injury Molecule-1 (KIM-1): a novel biomarker for human renal proximal tubule injury.Kidney Int. 2002; 62: 237-244Abstract Full Text Full Text PDF PubMed Scopus (1353) Google Scholar Recently, there has been an increased interest in the use of combinations of biomarkers to predict the development of AKI.6.Devarajan P. Biomarkers for the early detection of acute kidney injury.Curr Opin Pediatr. 2011; 23: 194-200Crossref PubMed Scopus (203) Google Scholar Combinations could account for differing time courses of biomarker release,7.Devarajan P. Neutrophil gelatinase-associated lipocalin: a promising biomarker for human acute kidney injury.Biomark Med. 2010; 4: 265-280Crossref PubMed Scopus (244) Google Scholar or they could reflect different pathophysiological mechanisms. In a recent study, the area under the curve (AUC) values to predict AKI after cardiac surgery were 0.65 for KIM-1, 0.61 for N-acetyl-β-D-glucosaminidase, and 0.67 for NGAL. The combination of the three markers had an AUC of 0.78 to predict the development of AKI.8.Han W.K. Wagener G. Zhu Y. et al.Urinary biomarkers in the early detection of acute kidney injury after cardiac surgery.Clin J Am Soc Nephrol. 2009; 4: 873-882Crossref PubMed Scopus (319) Google Scholar Biomarkers could also be added to clinical variables. Addition of L-fatty acid–binding protein and N-acetyl-β-D-glucosaminidase to a clinical model improved the ability to predict the development of AKI after cardiac surgery from an AUC value of 0.79–0.86.9.Katagiri D. Doi K. Honda K. et al.Combination of two urinary biomarkers predicts acute kidney injury after adult cardiac surgery.Ann Thorac Surg. 2012; 93: 577-583Abstract Full Text Full Text PDF PubMed Scopus (97) Google Scholar Recently, the identification of biomarkers that predict the outcomes of patients with established AKI rather than the development has been highlighted. Predictive biomarkers could be used to select patients at higher risk of adverse outcomes. Identification of patients with existing AKI who will develop worsening kidney disease would enable more timely interventions. The recent KDIGO (Kidney Disease: Improving Global Outcome) clinical practice guidelines for AKI suggest that consideration of intensive care unit admission, renal replacement therapy, and adjustments in drug dosing be made for patients with more severe AKI.10.Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group KDIGO Clinical Practice Guidelines for acute kidney injury.Kidney Int. 2012; 2: 1-138Abstract Full Text Full Text PDF Scopus (2005) Google Scholar Biomarkers have been used to predict worsening AKI among patients with AKI, but these studies have attempted to predict any change in AKI (defined as worsening of Acute Kidney Injury Network (AKIN) stage) rather than development of severe AKI defined as stage 3 or death.11.Hall I.E. Coca S.G. Perazella M.A. et al.Risk of poor outcomes with novel and traditional biomarkers at clinical AKI diagnosis.Clin J Am Soc Nephrol. 2011; 6: 2740-2749Crossref PubMed Scopus (88) Google Scholar,12.Koyner J.L. Garg A.X. Coca S.G. et al.Biomarkers predict progression of acute kidney injury after cardiac surgery.J Am Soc Nephrol. 2012; 23: 905-914Crossref PubMed Scopus (200) Google Scholar The single study that has attempted to predict the development of severe AKI at the time of AKI diagnosis demonstrated that urine NGAL had an AUC value of 0.78, although only nine patients progressed to severe AKI.13.Koyner J.L. Vaidya V.S. Bennett M.R. et al.Urinary biomarkers in the clinical prognosis and early detection of acute kidney injury.Clin J Am Soc Nephrol. 2010; 5: 2154-2165Crossref PubMed Scopus (287) Google Scholar Interventions could be made earlier if patients at high risk of worsening AKI could be identified. Predictive biomarkers could also be used to guide enrollment in clinical trials, allowing for selection of patients most likely to benefit from intervention. Individual biomarkers have not been robust predictors of worsening AKI. Although studies have used combinations of biomarkers to predict the development of AKI,6.Devarajan P. Biomarkers for the early detection of acute kidney injury.Curr Opin Pediatr. 2011; 23: 194-200Crossref PubMed Scopus (203) Google Scholar,9.Katagiri D. Doi K. Honda K. et al.Combination of two urinary biomarkers predicts acute kidney injury after adult cardiac surgery.Ann Thorac Surg. 2012; 93: 577-583Abstract Full Text Full Text PDF PubMed Scopus (97) Google Scholar,14.Krawczeski C.D. Goldstein S.L. Woo J.G. et al.Temporal relationship and predictive value of urinary acute kidney injury biomarkers after pediatric cardiopulmonary bypass.J Am Coll Cardiol. 2011; 58: 2301-2309Abstract Full Text Full Text PDF PubMed Scopus (266) Google Scholar fewer have used biomarker combinations to predict the worsening of AKI.12.Koyner J.L. Garg A.X. Coca S.G. et al.Biomarkers predict progression of acute kidney injury after cardiac surgery.J Am Soc Nephrol. 2012; 23: 905-914Crossref PubMed Scopus (200) Google Scholar We measured 32 candidate biomarkers in patients with stage 1 AKI after cardiac surgery to determine the ability of the biomarkers alone or in combination to predict worsening AKI. Urine samples and clinical data were collected from 95 subjects who had AKIN stage 1 AKI at the time of urine collection after cardiac surgery. Seventy-three of these patients achieved a maximum AKIN stage of 1, of whom 1 died; 12 patients had a maximum stage of 2, with 2 deaths; and 10 patients had a maximum stage of 3, with 6 deaths. Twenty-three patients met the combined end point of AKI progression (reaching AKIN stage 2 or 3) or death within 30 days of the urine sample collection. There was no difference between the outcome groups based on gender, race, comorbidities, type of surgery, baseline serum creatinine, creatinine at collection, and time to collection from the time of surgery (Table 1). We measured the concentration of 32 urine analytes in order to determine the ability of each biomarker to predict the combined outcome of AKI progression or death. Seven of the biomarkers had at least 19 (≥20%) samples for which the biomarker was ‘out of range low’ (OOR<), a situation in which the fluorescent signal falls below the lower asymptote of the fitted dose–response curve, thereby precluding concentration estimation. Primary analysis was performed using the AUC of the receiver operating characteristic curve using a leave-one-out cross-validation approach. Initial analysis (Supplementary Tables S1 and S2 online) showed that adjustment for urine creatinine improved the ability to predict the outcome for most of the biomarkers, and thus adjusted values are reported. In contrast to most of the markers, NGAL had slightly higher predictive values for both end points without adjustment. To provide an initial framework for characterization of the biomarkers, we divided the biomarkers into mechanistic groups. Table 2 shows the predictive characteristics for each of the 32 urine analytes broken down by biomarker functional category. We ranked biomarkers by their mean squared error (MSE)—lower MSE indicates better fit—and evaluated predictive performance using AUC. The highest AUC value of 0.74 was seen for IL-18 (Figure 1) and renin. KIM-1 had an AUC of 0.73 and VEGF, IL-6, and NGAL all had AUC of 0.72. For comparison, percentage of change in serum creatinine at the time of collection and Cleveland Clinic scores had AUC values of 0.76 and 0.64, respectively.Table 1Univariate associations of patient characteristics with progression statusProgression to AKIN stage 2/3 or deathNo (n=72)Yes (n=23)VariableMeasureaData are shown as median (interquartile range) or n(%). Race is missing for one non-progressor, weight is missing for two non-progressors, bypass time excludes the patients who did not undergo cardiac bypass.MeasureaData are shown as median (interquartile range) or n(%). Race is missing for one non-progressor, weight is missing for two non-progressors, bypass time excludes the patients who did not undergo cardiac bypass.PbP-value based on a test of the overall association of surgery with progression.Female gender22 (31)7 (30)>0.99African AmericancP-values based on Fisher's exact test for categorical variables, and Wilcoxon rank sum test for continuous variables.17 (24)4 (17)0.58Age (years)65.5 (31–86)68 (41–88)0.07Weight (kg)cP-values based on Fisher's exact test for categorical variables, and Wilcoxon rank sum test for continuous variables.87.3 (52.7–152.3)88.2 (51–159)0.89Preoperative use of intra-aortic balloon pump11 (15)4 (17)0.75Left ventricular ejection fraction 0.99Chronic obstructive pulmonary disease8 (11)3 (13)0.72Cardiac surgery type Coronary artery bypass grafting (CABG)36 (50)7 (30)0.15 Valve21 (29)6 (26)>0.99 CABG and valve10 (14)7 (30)0.11 Other5 (7)3 (13)0.40Emergency surgery17 (24)7 (30)0.58Bypass59 (82)20 (87)0.75Congestive heart failure25 (35)9 (39)0.80Bypass time (minutes)151 (55–383)168.5 (49–396)0.61Cleveland Clinic score4 (0–10)5 (0–10)0.05Baseline serum creatinine (mg/dl)1.1 (0.7–2.7)1.2 (0.7–2.4)0.51Collection creatinine (mg/dl)1.6 (1–3.1)2 (1.1–4.2)0.06Percentage of change in creatinine35.5 (14–89)62 (22–94)0.0002Time to collection (days)0.89 (0.1–3)1.79 (0.5–2.8)0.09Renal replacement therapy0 (0)8 (35)<0.0001Abbreviation: AKIN, Acute Kidney Injury Network.a Data are shown as median (interquartile range) or n(%). Race is missing for one non-progressor, weight is missing for two non-progressors, bypass time excludes the patients who did not undergo cardiac bypass.b P-value based on a test of the overall association of surgery with progression.c P-values based on Fisher's exact test for categorical variables, and Wilcoxon rank sum test for continuous variables. Open table in a new tab Download .doc (1.3 MB) Help with doc files Supplementary TablesTable 2Predictive characteristics of molecular and clinical biomarkers for progression defined as AKIN stage 2/3 or death (includes adjustment for urinary creatinine)Leave-one-out cross-validation results% MisclassifiedFunctionNameOOR<AUC95% CIMSEProgressorsNon-progressorsMolecular markers InflammationIL-1800.74(0.60, 0.85)0.15239.127.8IL-640.72(0.57, 0.84)0.16434.830.6VEGF70.72(0.58, 0.83)0.16834.829.2MCP-100.68(0.54, 0.79)0.17243.543.1IL-1ra00.67(0.52, 0.79)0.17547.830.6IL-8100.71(0.59, 0.80)0.17626.138.9GRO alpha180.69(0.54, 0.81)0.17939.137.5LIF410.62(0.44, 0.78)0.18543.537.5IL-1000.60(0.46, 0.73)0.18539.147.2Eotaxin190.62(0.45, 0.76)0.18752.233.3VCAM-100.57(0.41, 0.72)0.18956.538.9RANTES630.45(0.40, 0.50)0.19169.630.6TNF-alpha740.53(0.35, 0.70)0.19278.316.7MIP-1 alpha300.48(0.31, 0.65)0.19256.551.4 Hemodynamic regulationRenin00.74(0.61, 0.85)0.16243.536.1 Injury response (up)NGAL30.72(0.59, 0.82)0.16321.745.8KIM-100.73(0.60, 0.83)0.16426.136.1L-FABP00.67(0.51, 0.80)0.16847.831.9HGF00.66(0.51, 0.79)0.17343.537.5Netrin-100.66(0.50, 0.79)0.17447.829.2Clusterin00.63(0.47, 0.76)0.18039.138.9Fetuin-A20.64(0.48, 0.77)0.18156.518.1 Tubular dysfunctionCystatin C00.68(0.53, 0.79)0.16952.231.9Albumin00.67(0.52, 0.79)0.18039.148.6Total protein80.66(0.50, 0.78)0.18047.834.7Beta-2-microglobulin350.59(0.39, 0.76)0.18152.241.7RBP00.60(0.45, 0.72)0.18656.544.4alpha-1 antitrypsin00.59(0.44, 0.72)0.18660.927.8 Reactive oxygen species8-Isoprostane00.50(0.34, 0.65)0.1958750 Injury response (down)TFF-300.62(0.47, 0.75)0.18552.244.4 Injured cell enzymesNAG40.69(0.55, 0.80)0.16947.844.4 ApoptosisTRAIL590.57(0.37, 0.75)0.18856.533.3Clinical markers Percentage of change in creatinine0.76(0.61, 0.86)0.15439.111.1 Cleveland Clinic score0.63(0.49, 0.76)0.18482.65.6Abbreviations: AKIN, Acute Kidney Injury Network; AUC, area under the curve; CI, confidence interval; GRO alpha, growth-related oncogene alpha; HGF, hepatocyte growth factor; IL, interleukin; IL-1ra, interleukin 1 receptor antagonist; KIM-1, kidney injury molecule-1; L-FABP, liver-type fatty acid–binding protein; LIF, leukemia inhibitory factor; MCP-1, monocyte chemotactic protein-1; MIP-1 alpha, macrophage inflammatory protein-1 alpha; MSE, mean squared error; NAG, N-acetyl-β-D-glucosaminidase; NGAL, neutrophil gelatinase–associated lipocalin; OOR<, out of range low; RANTES, regulated on activation, normal T-cell expressed and secreted; RBP, retinol-binding protein; TFF-3, trefoil factor 3; TNF-alpha, tumor necrosis factor alpha; TRAIL, TNF-related apoptosis-inducing ligand; VCAM-1, vascular cell adhesion molecule-1; VEGF, vascular endothelial growth factor. Open table in a new tab Abbreviation: AKIN, Acute Kidney Injury Network. Abbreviations: AKIN, Acute Kidney Injury Network; AUC, area under the curve; CI, confidence interval; GRO alpha, growth-related oncogene alpha; HGF, hepatocyte growth factor; IL, interleukin; IL-1ra, interleukin 1 receptor antagonist; KIM-1, kidney injury molecule-1; L-FABP, liver-type fatty acid–binding protein; LIF, leukemia inhibitory factor; MCP-1, monocyte chemotactic protein-1; MIP-1 alpha, macrophage inflammatory protein-1 alpha; MSE, mean squared error; NAG, N-acetyl-β-D-glucosaminidase; NGAL, neutrophil gelatinase–associated lipocalin; OOR<, out of range low; RANTES, regulated on activation, normal T-cell expressed and secreted; RBP, retinol-binding protein; TFF-3, trefoil factor 3; TNF-alpha, tumor necrosis factor alpha; TRAIL, TNF-related apoptosis-inducing ligand; VCAM-1, vascular cell adhesion molecule-1; VEGF, vascular endothelial growth factor. We next compared the ability of the biomarkers to predict the outcome of development of severe AKI (defined as AKIN stage 3) or death within 30 days. Baseline demographic and clinical characteristics were similar between groups (Supplementary Table S3 online). Change in serum creatinine and Cleveland Clinic score showed only marginal improvements in prediction, but the predictive ability of many of the biomarkers was markedly improved (Table 3). IL-18 was the best predictor with an AUC of 0.89 and the smallest MSE. Figure 1 shows the receiver operating characteristic curves for prediction of both outcomes and boxplots for the values of creatinine-adjusted IL-18 for each of the eventual outcomes. These data demonstrate that the currently available biomarkers are better predictors of severe AKI than they are for the outcome of any degree of worsening in AKI and that IL-18 is an excellent predictor of severe AKI or death.Table 3Predictive characteristics of molecular and clinical biomarkers for progression defined as AKIN stage 3 or death (includes adjustment for urinary creatinine)Leave-one-out cross-validation results% MisclassifiedFunctionNameOOR<AUC95% CIMSEProgressorsNon-progressorsMolecular markers InflammationIL-1800.89(0.75, 0.95)0.07830.823.2IL-640.87(0.75, 0.93)0.08915.426.8VEGF70.8(0.64, 0.90)0.09630.831.7VCAM-100.83(0.67, 0.92)0.09738.526.8MCP-100.81(0.66, 0.90)0.09838.519.5IL-1ra00.77(0.58, 0.89)0.10438.513.4GRO alpha180.77(0.62, 0.88)0.11346.213.4IL-8100.75(0.60, 0.86)0.11338.542.7IL-1000.67(0.47, 0.82)0.11553.830.5LIF410.67(0.44, 0.85)0.11746.239Eotaxin190.6(0.37, 0.79)0.12353.832.9MIP-1 alpha300.55(0.39, 0.70)0.12353.848.8RANTES630.55(0.49, 0.61)0.12310015.9TNF-alpha740.53(0.49, 0.57)0.12317.128.4 Hemodynamic regulationRenin00.73(0.52, 0.87)0.10846.212.2 Injury response (up)L-FABP00.85(0.65, 0.95)0.07923.120.7NGAL30.83(0.67, 0.93)0.09338.526.8Clusterin00.85(0.71, 0.93)0.09638.513.4KIM-100.81(0.68, 0.90)0.09930.829.3Fetuin-A20.79(0.60, 0.91)0.10238.528HGF00.82(0.67, 0.91)0.10338.526.8Netrin-100.68(0.47, 0.83)0.11853.830.5 Tubular dysfunctionCystatin C00.84(0.69, 0.93)0.09130.830.5Beta-2-microglobulin350.76(0.51, 0.90)0.10146.240.2alpha-1 antitrypsin00.76(0.57, 0.88)0.10738.525.6Total protein80.7(0.48, 0.86)0.10846.224.4Albumin00.79(0.62, 0.89)0.10946.234.1RBP00.75(0.57, 0.87)0.11238.523.2 Reactive oxygen species8-Isoprostane00.59(0.38, 0.77)0.12253.834.1 Injury response (down)TFF-300.75(0.56, 0.88)0.11638.514.6 Injured cell enzymesNAG40.81(0.64, 0.91)0.09538.526.8 ApoptosisTRAIL590.62(0.35, 0.83)0.12053.830.5Clinical markers Percentage of change in creatinine0.79(0.60, 0.91)0.10138.59.8 Cleveland Clinic score0.68(0.53, 0.80)0.12053.822.0Abbreviations: AKIN, Acute Kidney Injury Network; AUC, area under the curve; CI, confidence interval; GRO alpha, growth-related oncogene alpha; HGF, hepatocyte growth factor; IL, interleukin; IL-1ra, interleukin 1 receptor antagonist; KIM-1, kidney injury molecule-1; L-FABP, liver-type fatty acid–binding protein; LIF, leukemia inhibitory factor; MCP-1, monocyte chemotactic protein-1; MIP-1 alpha, macrophage inflammatory protein-1 alpha; MSE, mean squared error; NAG, N-acetyl-β-D-glucosaminidase; NGAL, neutrophil gelatinase–associated lipocalin; OOR<, out of range low; RANTES, regulated on activation, normal T-cell expressed and secreted; RBP, retinol-binding protein; TFF-3, trefoil factor 3; TNF-alpha, tumor necrosis factor alpha; TRAIL, TNF-related apoptosis-inducing ligand; VCAM-1, vascular cell adhesion molecule-1; VEGF, vascular endothelial growth factor. Open table in a new tab Abbreviations: AKIN, Acute Kidney Injury Network; AUC, area under the curve; CI, confidence interval; GRO alpha, growth-related oncogene alpha; HGF, hepatocyte growth factor; IL, interleukin; IL-1ra, interleukin 1 receptor antagonist; KIM-1, kidney injury molecule-1; L-FABP, liver-type fatty acid–binding protein; LIF, leukemia inhibitory factor; MCP-1, monocyte chemotactic protein-1; MIP-1 alpha, macrophage inflammatory protein-1 alpha; MSE, mean squared error; NAG, N-acetyl-β-D-glucosaminidase; NGAL, neutrophil gelatinase–associated lipocalin; OOR<, out of range low; RANTES, regulated on activation, normal T-cell expressed and secreted; RBP, retinol-binding protein; TFF-3, trefoil factor 3; TNF-alpha, tumor necrosis factor alpha; TRAIL, TNF-related apoptosis-inducing ligand; VCAM-1, vascular cell adhesion molecule-1; VEGF, vascular endothelial growth factor. To determine the relationship of individual biomarkers with each other, we performed two analyses. First, we performed an unsupervised cluster analysis to determine which biomarkers were similar to each other (Figure 2). We found many similarities to our a priori grouping of biomarkers, but we also found interesting differences. Many of the proteins that we had proposed were filtered plasma proteins that are not reabsorbed in the tubule because of tubular dysfunction were grouped together (albumin, alpha-1 antitrypsin, cystatin C, beta-2 microglobulin, and retinol-binding protein). Similarly, many of the proteins we described as inflammatory proteins were also grouped together. However, some of the proteins that we thought would be similar to each other were clustered differently. NGAL and KIM-1, which were placed in the injury response (up) group, were geographically distant from each other in the dendrogram. We also compared correlation coefficients for each of the biomarkers within the groups and with the best marker in each of the other groups (Supplementary Tables S4–11 online). Overall, the correlation within each group was stronger for biomarkers that had better predictive ability. A notable exception was KIM-1, which had a poor correlation with other markers in its group, as well as with markers in other groups (Supplementary Table S6 online). KIM-1 was a strong predictor of both outcomes (AUC=0.73 and 0.81) but had a correlation coefficient of 0.20 with L-FABP and of 0.24 with IL-18, suggesting that the combination of KIM-1 with one of these other markers may be beneficial. We determined the ability of combinations of biomarkers to predict the two outcomes, ranking groups of biomarkers according to MSE. The combination of IL-18 and percentage of change in serum creatinine (Table 4) had the lowest MSE to predict AKIN 2/3 or death (AUC=0.80), whereas the combination of cystatin C and percentage of change in serum creatinine had the lowest MSE to predict AKIN 3 or death (AUC=0.88). As suggested by our correlation analysis, the combination of IL-18 and KIM-1 was also a very good predictor of AKIN 3 or death, with an AUC of 0.93, a positive predictive value of 63%, and sensitivity of 77%. KIM-1 combined with L-FABP also was a strong predictor of AKIN 3 or death (AUC=0.89), as suggested by our correlation analysis. The combination of IL-18 and percentage of change in serum creatinine had an excellent AUC (0.93) but a slightly lower MSE (0.074). The positive predictive value and sensitivity of this combination were 65% and 85%, respectively. Supplementary Tables S12 and S13 online show the characteristics of each combination of biomarkers to predict the two outcomes.Table 4Biomarker test operating and performance characteristics for combinationsAUC95% CIMSEProbability thresholdT+, N (%)PPV (D+|T+), N (%)Sens (T+|D+), N (%)AKIN 2/3 or death IL-18 +percentage of change in creatinine0.80(0.67, 0.89)0.1340.3023 (24)14 (61)14 (61) IL-8 +percentage of change in creatinine0.81(0.68, 0.89)0.1380.3024 (25)14 (58)14 (61) NGAL +percentage of change in creatinine0.82(0.70, 0.90)0.1390.2827 (28)15 (56)15 (65)AKIN 3 or death Cystatin C +percentage of change in creatinine0.88(0.70, 0.96)0.0670.1326 (27)10 (38)10 (77) KIM-1+IL-180.93(0.80, 0.98)0.0690.3116 (17)10 (63)10 (77) NGAL +percentage of change in creatinine0.89(0.72, 0.96)0.0710.1324 (25)10 (42)10 (77) IL-18 +percentage of change in creatinine0.93(0.79, 0.98)0.0740.1717 (18)11 (65)11 (85)Abbreviations: AKIN, Acute Kidney Injury Network; AUC, area under the curve; CI, confidence interval; D+|T+, disease positive given test positive; IL, interleukin; KIM-1, kidney injury molecule-1; MSE, mean squared error; NGAL, neutrophil gelatinase–associated lipocalin; PPV, positive predictive value; Sens, sensitivity; T+, test positive; T+|D+, test positive given disease positive. Open table in a new tab Abbreviations: AKIN, Acute Kidney Injury Network; AUC, area under the curve; CI, confidence interval; D+|T+, disease positive given test positive; IL, interleukin; KIM-1, kidney injury molecule-1; MSE, mean squared error; NGAL, neutrophil gelatinase–associated lipocalin; PPV, positive predictive value; Sens, sensitivity; T+, test positive; T+|D+, test positive given disease positive. We measured the ability of 32 AKI biomarkers to predict worsening of renal function in patients with AKIN stage 1 AKI after cardiac surgery. IL-18 had the highest AUC value for the prediction of both outcomes we evaluated. Most of the biomarkers were better predictors of severe AKI (AKIN 3 or death) than they were of any degree of progression (AKIN 2/3 or death). The values for prediction of AKI were similar to those seen in the literature, although this is the first larger-scale side-by-side comparison of the ability of these markers to predict an outcome of worsening of AKI. Koyner et al.13.Koyner J.L. Vaidya V.S. Bennett M.R. et al.Urinary biomarkers in the clinical prognosis and early detection of acute kidney injury.Clin J Am Soc Nephrol. 2010; 5: 2154-2165Crossref PubMed Scopus (287) Google Scholar compared the ability of several biomarkers to predict the progression to AKIN stage 3 AKI in patients with an increase in serum creatinine after cardiac surgery. They showed that π-GST (π-glutathione S-transferase) adjusted for urine creatinine (AUC=0.86) had the best performance followed by NGAL (AUC=0.78), cystatin C (AUC=0.77), hepatocyte growth factor (AUC=0.68), KIM-1 (AUC=0.65), and α-GST (AUC=0.54). They did not test IL-18, which was the best performer in our study. Because of the small numbers of patients included in the prognostic analysis in that paper (n=46), the confidence intervals (CIs) were large. The current study has refined the predictive ability and compared a larger number of biomarkers, although the total number of outcomes is still small. In a larger study from the TRIBE-AKI consortium, the ability of urine NGAL, albumin-to-creatinine ratio, IL-18, and plasma NGAL to predict the progression to a higher AKIN stage for patients with stage 1 or stage 2 AKI at the time of sample collection was determined.12.Koyner J.L. Garg A.X. Coca S.G. et al.Biomarkers predict progression of acute kidney injury after cardiac surgery.J Am Soc Nephrol. 2012; 23: 905-914Crossref PubMed Scopus (200) Google Scholar They found the following AUC values: urine NGAL, 0.58; albumin-to-creatinine ratio, 0.67; IL-18, 0.63; and plasma NGAL, 0.74. Adjustment for clinical factors improved the AUC values to 0.79, 0.78, 0.77, and 0.80, respectively. A second study from TRIBE-AKI showed that combining urine concentrations of IL-18 with a clinical model improved the prediction for the development of AKI from 0.69 in the clinical model to 0.76 for the clinical model plus urinary IL-18.15.Parikh C.R. Devarajan P. Zappitelli M. et al.Postoperative biomarkers predict acute kidney injury and poor outcomes after adult cardiac surgery.J Am Soc Nephrol. 2011; 22: 1748-1757Crossref PubMed Scopus (415) Google Scholar Hall et al.11.Hall I.E. Coca S.G. Perazella M.A. et al.Risk of poor outcomes with novel and traditional biomarkers at clinical AKI diagnosis.Clin J Am Soc Nephrol. 2011; 6: 2740-2749Crossref PubMed Scopus (88) Google Scholar looked at the ability of biomarkers to predict worsening AKI defined as an increased stage of AKI at the time of diagnosis with AKI in 284 patients. The majority of these patients were thought to have prerenal azotemia. They found that urine NG
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