Derivation and validation of the renal angina index to improve the prediction of acute kidney injury in critically ill children
2013; Elsevier BV; Volume: 85; Issue: 3 Linguagem: Inglês
10.1038/ki.2013.349
ISSN1523-1755
AutoresRajit K. Basu, Michael Zappitelli, Lori Brunner, Yu Wang, Hector R. Wong, Lakhmir S. Chawla, Derek S. Wheeler, Stuart L. Goldstein,
Tópico(s)Cardiac Arrest and Resuscitation
ResumoReliable prediction of severe acute kidney injury (AKI) has the potential to optimize treatment. Here we operationalized the empiric concept of renal angina with a renal angina index (RAI) and determined the predictive performance of RAI. This was assessed on admission to the pediatric intensive care unit, for subsequent severe AKI (over 200% rise in serum creatinine) 72h later (Day-3 AKI). In a multicenter four cohort appraisal (one derivation and three validation), incidence rates for a Day 0 RAI of 8 or more were 15–68% and Day-3 AKI was 13–21%. In all cohorts, Day-3 AKI rates were higher in patients with an RAI of 8 or more with the area under the curve of RAI for predicting Day-3 AKI of 0.74–0.81. An RAI under 8 had high negative predictive values (92–99%) for Day-3 AKI. RAI outperformed traditional markers of pediatric severity of illness (Pediatric Risk of Mortality-II) and AKI risk factors alone for prediction of Day-3 AKI. Additionally, the RAI outperformed all KDIGO stages for prediction of Day-3 AKI. Thus, we operationalized the renal angina concept by deriving and validating the RAI for prediction of subsequent severe AKI. The RAI provides a clinically feasible and applicable methodology to identify critically ill children at risk of severe AKI lasting beyond functional injury. The RAI may potentially reduce capricious AKI biomarker use by identifying patients in whom further testing would be most beneficial. Reliable prediction of severe acute kidney injury (AKI) has the potential to optimize treatment. Here we operationalized the empiric concept of renal angina with a renal angina index (RAI) and determined the predictive performance of RAI. This was assessed on admission to the pediatric intensive care unit, for subsequent severe AKI (over 200% rise in serum creatinine) 72h later (Day-3 AKI). In a multicenter four cohort appraisal (one derivation and three validation), incidence rates for a Day 0 RAI of 8 or more were 15–68% and Day-3 AKI was 13–21%. In all cohorts, Day-3 AKI rates were higher in patients with an RAI of 8 or more with the area under the curve of RAI for predicting Day-3 AKI of 0.74–0.81. An RAI under 8 had high negative predictive values (92–99%) for Day-3 AKI. RAI outperformed traditional markers of pediatric severity of illness (Pediatric Risk of Mortality-II) and AKI risk factors alone for prediction of Day-3 AKI. Additionally, the RAI outperformed all KDIGO stages for prediction of Day-3 AKI. Thus, we operationalized the renal angina concept by deriving and validating the RAI for prediction of subsequent severe AKI. The RAI provides a clinically feasible and applicable methodology to identify critically ill children at risk of severe AKI lasting beyond functional injury. The RAI may potentially reduce capricious AKI biomarker use by identifying patients in whom further testing would be most beneficial. Approximately 10% of all children admitted to an intensive care unit (ICU) develop acute kidney injury (AKI), and this rate increases up to 82% with increasing patient severity of illness.1.Schneider J. Khemani R. Grushkin C. et al.Serum creatinine as stratified in the RIFLE score for acute kidney injury is associated with mortality and length of stay for children in the pediatric intensive care unit.Crit Care Med. 2010; 38: 933-939Crossref PubMed Scopus (242) Google Scholar,2.Akcan-Arikan A. Zappitelli M. Loftis L.L. et al.Modified RIFLE criteria in critically ill children with acute kidney injury.Kidney Int. 2007; 71: 1028-1035Abstract Full Text Full Text PDF PubMed Scopus (958) Google Scholar Increasing AKI severity, characterized by serum creatinine (SCr)- and urine output (UOP)-based stratifications of AKI, is associated with increased mortality in adults3.Srisawat N. Hoste E.E. Kellum J.A. Modern classification of acute kidney injury.Blood Purif. 2010; 29: 300-307Crossref PubMed Scopus (105) Google Scholar and children.4.Slater M.B. Anand V. Uleryk E.M. et al.A systematic review of RIFLE criteria in children, and its application and association with measures of mortality and morbidity.Kidney Int. 2012; 81: 791-798Abstract Full Text Full Text PDF PubMed Scopus (44) Google Scholar Even small increases in SCr (0.3mg/dl) reflect significant kidney damage and are associated with poor patient outcome.5.Chertow G.M. Burdick E. Honour M. et al.Acute kidney injury, mortality, length of stay, and costs in hospitalized patients.J Am Soc Nephrol. 2005; 16: 3365-3370Crossref PubMed Scopus (2502) Google Scholar,6.Zappitelli M. Bernier P.L. Saczkowski R.S. et al.A small post-operative rise in serum creatinine predicts acute kidney injury in children undergoing cardiac surgery.Kidney Int. 2009; 76: 885-892Abstract Full Text Full Text PDF PubMed Scopus (246) Google Scholar The well-recognized limitations of SCr for real-time accurate AKI diagnosis have prevented timely therapeutic interventions.7.Goldstein S.L. Acute kidney injury biomarkers: renal angina and the need for a renal troponin I.BMC Med. 2011; 9: 135Crossref PubMed Scopus (45) Google Scholar Thus, extensive research efforts have been expended to find earlier, more sensitive biomarkers for AKI. Several AKI biomarkers have demonstrated promising results for the identification and prediction of AKI in children. However, most have been validated only in the cardiopulmonary bypass (CPB) setting, where demographic homogeneity, lack of comorbidities, and a known onset and duration of ischemic injury provide an ideal biomarker validation environment.8.Kwiatkowski D.M. Goldstein S.L. Krawczeski C.D. Biomarkers of acute kidney injury in pediatric cardiac patients.Biomark Med. 2012; 6: 273-282Crossref PubMed Scopus (16) Google Scholar,9.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 Demographic heterogeneity likely contributes to the poor discriminatory performance of these biomarkers in non-cardiac pediatric intensive care unit (PICU) patients (area under the curve (AUC) values range from 0.54 to 0.78).10.Herrero-Morin J.D. Malaga S. Fernandez N. et al.Cystatin C and beta2-microglobulin: markers of glomerular filtration in critically ill children.Crit Care. 2007; 11: R59Crossref PubMed Scopus (82) Google Scholar, 11.Washburn K.K. Zappitelli M. Arikan A.A. et al.Urinary interleukin-18 is an acute kidney injury biomarker in critically ill children.Nephrol Dial Transplant. 2008; 23: 566-572Crossref PubMed Scopus (155) Google Scholar, 12.Zappitelli M. Washburn K.K. Arikan A.A. et al.Urine neutrophil gelatinase-associated lipocalin is an early marker of acute kidney injury in critically ill children: a prospective cohort study.Crit Care. 2007; 11: R84Crossref PubMed Scopus (342) Google Scholar, 13.Wheeler D.S. Devarajan P. Ma Q. et al.Serum neutrophil gelatinase-associated lipocalin (NGAL) as a marker of acute kidney injury in critically ill children with septic shock.Crit Care Med. 2008; 36: 1297-1303Crossref PubMed Scopus (286) Google Scholar We previously found that children with persistent AKI at PICU admission (AKI after 48h) were at the highest risk for requiring renal replacement therapy (RRT).2.Akcan-Arikan A. Zappitelli M. Loftis L.L. et al.Modified RIFLE criteria in critically ill children with acute kidney injury.Kidney Int. 2007; 71: 1028-1035Abstract Full Text Full Text PDF PubMed Scopus (958) Google Scholar Identifying patients at risk for severe and long-lasting AKI in the PICU, and as importantly, identifying patients unlikely to be at risk, is imperative as risk stratification could allow more judicious AKI biomarker assessment to drive therapeutic intervention, increasing their predictive performance and cost-effectiveness.14.Al-Ismaili Z. Palijan A. Zappitelli M. Biomarkers of acute kidney injury in children: discovery, evaluation, and clinical application.Pediatr Nephrol. 2011; 26: 29-40Crossref PubMed Scopus (71) Google Scholar,15.Glassford N.J. Eastwood G.M. Young H. et al.Rationalizing the use of NGAL in the intensive care unit.Am J Respir Crit Care Med. 2011; 184: 142Crossref PubMed Scopus (6) Google Scholar Along these lines, the recent 10th Acute Dialysis Quality Initiative Conference (ADQI-X) issued a directive to use combinations of biomarkers to identify and differentiate functional AKI (‘pre-renal’ or ‘reversible’) from kidney damage (persistent).16.Endre Z.H. Kellum J.A. Di Somma S. et al.Differential diagnosis of AKI in clinical practice by functional and damage biomarkers workgroup statements from the tenth Acute Dialysis Quality Initiative Consensus Conference.Contrib Nephrol. 2013; 182: 30-44Crossref PubMed Scopus (89) Google Scholar The context-based disparity of biomarker efficacy for acute coronary syndrome provides important lessons for the AKI field; troponin demonstrates suboptimal efficacy with capricious, undirected use.17.Lim W. Whitlock R. Khera V. et al.Etiology of troponin elevation in critically ill patients.J Crit Care. 2010; 25: 322-328Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar,18.Stein R. Gupta B. Agarwal S. et al.Prognostic implications of normal (<0.10 ng/ml) and borderline (0.10 to 1.49 ng/ml) troponin elevation levels in critically ill patients without acute coronary syndrome.Am J Cardiol. 2008; 102: 509-512Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar Although the ability to detect and subsequently expeditiously treat myocardial infarction was augmented with the discovery and incorporation of troponin into the clinical context of cardiac angina, repeated evidence highlights the erosion of troponin performance when measured in patients at low demographic and/or clinical risk of myocardial infarction from coronary disease.17.Lim W. Whitlock R. Khera V. et al.Etiology of troponin elevation in critically ill patients.J Crit Care. 2010; 25: 322-328Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar, 18.Stein R. Gupta B. Agarwal S. et al.Prognostic implications of normal (<0.10 ng/ml) and borderline (0.10 to 1.49 ng/ml) troponin elevation levels in critically ill patients without acute coronary syndrome.Am J Cardiol. 2008; 102: 509-512Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar, 19.Meyer M. Fink C. Roeger S. et al.Benefit of combining quantitative cardiac CT parameters with troponin I for predicting right ventricular dysfunction and adverse clinical events in patients with acute pulmonary embolism.Eur J Radiol. 2012; 81: 3294-3299Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar, 20.Agewall S. Giannitsis E. Jernberg T. et al.Troponin elevation in coronary vs. non-coronary disease.Eur Heart J. 2011; 32: 404-411Crossref PubMed Scopus (456) Google Scholar, 21.Agewall S. Tjora S. Physical exertion may cause high troponin levels.Tidsskr Nor Laegeforen. 2011; 131: 2226Crossref PubMed Scopus (3) Google Scholar, 22.Tjora S. Gjestland H. Mordal S. et al.Troponin rise in healthy subjects during exercise test.Int J Cardiol. 2011; 151: 375-376Abstract Full Text Full Text PDF PubMed Scopus (21) Google Scholar, 23.Ammann P. Fehr T. Minder E.I. et al.Elevation of troponin I in sepsis and septic shock.Intensive Care Med. 2001; 27: 965-969Crossref PubMed Scopus (311) Google Scholar In addition, independent of troponin, the absence of cardiac angina carries high negative predictive value (NPV) for the diagnosis of a heart attack.24.Bassan R. Pimenta L. Scofano M. et al.Probability stratification and systematic diagnostic approach for chest pain patients in the emergency department.Crit Pathw Cardiol. 2004; 3: 1-7Crossref PubMed Scopus (7) Google Scholar,25.Bassan R. Pimenta L. Scofano M. et al.Accuracy of a neural diagnostic tree for the identification of acute coronary syndrome in patients with chest pain and no ST-segment elevation.Crit Pathw Cardiol. 2004; 3: 72-78Crossref PubMed Scopus (9) Google Scholar To that end, we recently proposed the empiric clinical model of renal angina to identify which critically ill patients would be at the greatest risk of AKI.26.Goldstein S.L. Chawla L.S. Renal angina.Clin J Am Soc Nephrol. 2010; 5: 943-949Crossref PubMed Scopus (160) Google Scholar Using patient demographic factors and early signs of injury, renal angina aims to delineate patients at risk for subsequent severe AKI (AKI beyond the period of functional injury) versus those at low risk (Figure 1a). In the current study, we operationalize renal angina fulfillment by deriving an index (renal angina index: RAI) and, in separate derivation and validation cohorts, test the hypotheses that: (1) renal angina fulfillment using a RAI threshold improves prediction of subsequent severe AKI over severity of illness or risk factors alone and (2) RAI prediction of AKI outperforms currently used clinical thresholds for early signs of kidney injury. Demographics for each cohort (C1 (n=144): derivation; C2–C4 (n=118, 108, and 214, respectively): validation) are shown in Table 1. Other than the absence of transplant patients, there were no significant demographic differences between C1 and C3. C4 patients were more severely ill (Pediatric Risk of Mortality II (PRISM-II) score27.Pollack M.M. Ruttimann U.E. Getson P.R. Pediatric risk of mortality (PRISM) score.Crit Care Med. 1988; 16: 1110-1116Crossref PubMed Scopus (1109) Google Scholar) and had higher use of inotropy and mechanical ventilation than the other cohorts. The overall incidence of the subsequent severe AKI outcome 72–96h from PICU admission (Day-3 AKI) in the cohorts was 10–20% (C1: 19%, C2: 10.2%, C3: 10.2%, and C4: 13.6%). The optimal RAI cutoff for fulfillment of renal angina (ANG(+), defined by RAI ≥8) was derived by studying patients from cohort 1 (Supplementary A online).Table 1Demographic and clinical data for all cohortsCohort 1: CCHMC sepsis 1 derivationCohort 2: MCH pro validation 1Cohort 3: MCH retro validation 2Cohort 4: CCHMC sepsis 2 validation 3N144118108214Very high risk341927184High risk32009Moderate risk78998121Age, years3.8 (1.2, 12.5)3.0 (0.2, 11.7)1.5 (0.3, 10.6)2.2 (0.8, 5.9)Male, n (%)83 (57.6)74 (62.7)64 (59.2)134 (62.6)PRISM-II11 (7, 18)6 (4, 10)7 (4, 10)14 (8, 21)*Transplant, n (%)39 (27.1)009 (4.2)Inotropy, n (%)56 (38.9)23 (19.5)28 (25.9)214 (100)*MV, n (%)69 (47.9)87 (73.7)83 (76.9)184 (85.9)*Day-3 AKI, n (%)28 (19.4)12 (10.2)11 (10.2)29 (13.6)PICU LOS, days5 (3, 13)6 (4, 8)9 (6, 13)13 (8, 24)*RRT, n (%)13 (9.0)3 (2.5)3 (2.8)N/AMortality, n (%)13 (9.0)7 (5.9)4 (3.7)23 (10.7)Abbreviations: AKI, acute kidney disease; CCHMC, Cincinnati Children’s Hospital; LOS, length of stay; MCH, Montreal Children’s Hospital; MV, mechanical ventilation; N/A, not available; PICU, non-cardiac pediatric intensive care unit; PRISM-II, Pediatric Risk of Mortality score; pro, prospective; retro, retrospective; RRT, renal replacement therapy.Descriptive characteristics for each cohort of patients are listed above. Transplant refers to solid organs and bone marrow. Day-3 AKI refers to KDIGO stage 2 or 3 at Day 3 of PICU admission. Age, PRISM-II, and LOS are expressed as median (interquartile range).*P-value <0.05 Cohort 4 versus Cohort 1. Open table in a new tab Download .doc (.04 MB) Help with doc files Supplementary A Abbreviations: AKI, acute kidney disease; CCHMC, Cincinnati Children’s Hospital; LOS, length of stay; MCH, Montreal Children’s Hospital; MV, mechanical ventilation; N/A, not available; PICU, non-cardiac pediatric intensive care unit; PRISM-II, Pediatric Risk of Mortality score; pro, prospective; retro, retrospective; RRT, renal replacement therapy. Descriptive characteristics for each cohort of patients are listed above. Transplant refers to solid organs and bone marrow. Day-3 AKI refers to KDIGO stage 2 or 3 at Day 3 of PICU admission. Age, PRISM-II, and LOS are expressed as median (interquartile range). *P-value <0.05 Cohort 4 versus Cohort 1. Day 0 (PICU admission day) ANG(+) occurred in 51/144 (35%) of patients. Compared with ANG(-) (RAI <8) patients, ANG(+) patients had higher Day-3 AKI rates, longer PICU length of stay (LOS), higher RRT provision, and higher hospital mortality rates (Table 2). Day 0 RAI predicted Day-3 AKI with an AUC of 0.77 (95% confidence interval (CI)=0.68–0.86). RAI <8 had a high NPV of 92% (95% CI=85–97%) (Table 3).Table 2Demographics of each cohort by fulfillment of renal anginaCohort 1: CCHMC sepsis 1 derivationCohort 2: MCH pro validation 1Cohort 3: MCH retro validation 2Cohort 4: CCHMC sepsis 2 validation 3ANG(-)ANG(+)PANG(-)ANG(+)PANG(-)ANG(+)PANG(-)ANG(+)PN (%)93 (65)51 (35)100 (84.7)18 (15.3)70 (64.8)38 (35.2)69 (32.2)145 (67.8)Age (years)3.1 (1, 11)5.4 (2, 14)0.0886.0 (0, 12)5.9 (0.4, 10)0.905.6 (0, 11.7)4.5 (0, 10.7)0.373.8 (1.6, 6.8)1.7 (0.5, 5)<0.001Male, n (%)53 (56.9)30 (58.8)0.99762 (62.0)12 (67.7)0.7144 (62.9)20 (52.6)0.3040 (57.9)94 (44)0.413PRISM-II10 (5, 16)15 (8, 22)0.0166.8 (1, 12)7.9 (3, 13)0.416.2 (1, 12)8.9 (3, 15)0.00410 (5, 15)16 (10, 24)<0.001Day-3 AKI, n (%)7 (7.5)21 (41.2)<0.0015 (5.0)7 (38.9)<0.0010 (0)11 (28.9)<0.0012 (2.9)27 (19)0.003LOS, days5 (2, 10)9 (4, 15)0.0117 (2, 11)6 (4, 9)0.6612.3 (4, 20)10.1 (3, 17)0.2411 (8, 16)15 (7, 27)0.032RRT, n (%)4 (4.3)9 (17.6)0.022 (2.0)1 (5.6)0.390 (0)3 (7.9)0.04N/AN/AMortality, n (%)4 (4.3)9 (17.6)0.026 (6.0)1 (5.6)13 (4.3)1 (2.6)0.560 (0)23 (16)<0.001Abbreviations: AKI, acute kidney disease; ANG, renal angina; CCHMC, Cincinnati Children’s Hospital; LOS, length of stay; MCH, Montreal Children’s Hospital; N/A, not available; PRISM-II, Pediatric Risk of Mortality score; pro, prospective; retro, retrospective; RRT, renal replacement therapy.Select descriptive characteristics and outcomes for each cohort of patients are listed above. Day-3 AKI refers to KDIGO stage 2 or 3 at Day 3 of PICU admission. Data are expressed as medians with interquartile ranges in parentheses. P-values compare ANG(-) versus ANG(+) for each individual cohort. Open table in a new tab Table 3Performance of the renal angina index for prediction of subsequent severe AKICohort 1: CCHMC sepsis 1 derivationCohort 2: MCH pro validation 1Cohort 3: MCH retro validation 2Cohort 4: CCHMC sepsis 2 validation 3ANG(+), n (%)51 (35)18 (15)38 (35)145 (68)Day-3 AKI, n (%)28 (19)12 (10)11 (10)29 (13)Sensitivity, % (95% CI)75 (55–89)58 (28–85)91 (59–100)93 (76–99)Specificity, % (95% CI)73 (64–81)90 (82–95)71 (61–80)36 (33–37)PPV, % (95% CI)40 (27–55)39 (17–64)26 (13–43)18 (15–19)NPV, % (95% CI)92 (85–97)95 (89–98)99 (92–100)97 (90–99)AUC, (95% CI)0.77 (0.68–0.86)0.74 (0.59–0.88)0.81 (0.71–0.91)0.80 (0.75–0.86)Abbreviations: AKI, acute kidney disease; ANG, renal angina; AUC, area under the curve; CCHMC, Cincinnati Children’s Hospital; CI, confidence interval; MCH, Montreal Children’s Hospital; NPV, negative predictive value; PPV, positive predictive value; pro, prospective; retro, retrospective.The performance of the renal angina index (RAI) for prediction of severe AKI is shown above. For each patient in each cohort, an RAI was derived (a score of ≥8 was considered fulfillment of renal angina). The predictive performance of fulfillment of ANG on day 0 for the presence of Day-3 AKI was evaluated, which comprised the following: sensitivity, specificity, PPV, and NPV. The absolute value of the RAI (range 1–40) was used to derive the AUC receiver operating characteristic. ANG(+)refers to patients who fulfilled angina. Sensitivity, specificity, NPV, PPV, and AUC are listed with 95% CI. Open table in a new tab Abbreviations: AKI, acute kidney disease; ANG, renal angina; CCHMC, Cincinnati Children’s Hospital; LOS, length of stay; MCH, Montreal Children’s Hospital; N/A, not available; PRISM-II, Pediatric Risk of Mortality score; pro, prospective; retro, retrospective; RRT, renal replacement therapy. Select descriptive characteristics and outcomes for each cohort of patients are listed above. Day-3 AKI refers to KDIGO stage 2 or 3 at Day 3 of PICU admission. Data are expressed as medians with interquartile ranges in parentheses. P-values compare ANG(-) versus ANG(+) for each individual cohort. Abbreviations: AKI, acute kidney disease; ANG, renal angina; AUC, area under the curve; CCHMC, Cincinnati Children’s Hospital; CI, confidence interval; MCH, Montreal Children’s Hospital; NPV, negative predictive value; PPV, positive predictive value; pro, prospective; retro, retrospective. The performance of the renal angina index (RAI) for prediction of severe AKI is shown above. For each patient in each cohort, an RAI was derived (a score of ≥8 was considered fulfillment of renal angina). The predictive performance of fulfillment of ANG on day 0 for the presence of Day-3 AKI was evaluated, which comprised the following: sensitivity, specificity, PPV, and NPV. The absolute value of the RAI (range 1–40) was used to derive the AUC receiver operating characteristic. ANG(+)refers to patients who fulfilled angina. Sensitivity, specificity, NPV, PPV, and AUC are listed with 95% CI. Day 0 ANG(+) occurred in 15.3% (C2), 35.2% (C3), and 67.8% (C4) of patients. ANG(+) patients had significantly higher Day-3 AKI rates than ANG(-) patients in all cohorts (Table 2). Day 0 RAI predicted Day-3 AKI with an AUC between 0.74 and 0.81 and RAI <8 had an NPV ≥95% for all three cohorts (Table 3). In addition, RRT provision rates were higher, PICU LOS was longer, and mortality was higher in ANG(+) than in ANG(-) patients (Table 2). Both the predictive variable (RAI) and the outcome variable (AKI) were broken down by composite factors of kidney injury. The discrimination of RAI for Day-3 AKI by change in creatinine clearance from baseline (ΔeCCl) resulted in AUC values consistently superior to the discrimination by percent fluid overload (FO). Although FO did not perform as well for prediction as ΔeCCl, the AUC for RAI for Day-3 AKI improved when RAI incorporated both day of admission ΔeCCl and FO (Table 4). The AUC values for RAI prediction of Day-3 AKI were not different for whichever outcome criterion was used for outcome (UOP or ΔeCCl).Table 4Renal angina index performance broken down by individual criterionDay-3 AKI outcomeUOPCrWorseRAI ?eCCl0.81 (0.71–0.90)0.73 (0.59–0.88)0.78 (0.69–0.87) FO0.57 (0.44–0.71)0.63 (0.49–0.76)0.60 (0.49–0.71) Worse0.78 (0.68–0.88)0.75 (0.62–0.89)0.77 (0.68–0.86)Illness score PRISM-II0.65 (0.52–0.79)0.61 (0.45–0.79)0.66 (0.54–0.79)Abbreviations: ΔeCCl, estimated change in creatinine clearance from baseline; AKI, acute kidney disease; Cr, creatinine; FO, fluid overload; RAI, renal angina index; UOP, urine output.The area under the curve (AUC) values were calculated for prediction of Day-3 AKI for the RAI broken down by individual components (ΔeCCl, FO, or the ‘worse’ of the two). The AUC values demonstrate discriminatory superiority of ΔeCCl-derived RAI over FO-derived RAI for Day-3 AKI by all metrics of outcome used (UOP, Cr, or the “worse” variable). Including the FO metric for RAI improved the AUC for AKI measured by creatinine at Day 3 from 0.73 (RAI derived solely from ΔeCCl) to 0.75 (RAI derived from ‘worse’). Renal angina outperforms severity of illness scores for prediction of Day-3 AKI. For patients in cohort 1, the discrimination of day of admission RAI for Day-3 AKI was compared against Pediatric Risk of Mortality-II (PRISM-II) scores. Although the performance of FO-derived RAI is not as robust as ΔeCCl-derived RAI, it is comparable to PRISM-II and in some cases (Day-3 AKI outcome measured by creatinine clearance change (Cr)) improved. AUC values are expressed with 95% confidence intervals. Open table in a new tab Abbreviations: ΔeCCl, estimated change in creatinine clearance from baseline; AKI, acute kidney disease; Cr, creatinine; FO, fluid overload; RAI, renal angina index; UOP, urine output. The area under the curve (AUC) values were calculated for prediction of Day-3 AKI for the RAI broken down by individual components (ΔeCCl, FO, or the ‘worse’ of the two). The AUC values demonstrate discriminatory superiority of ΔeCCl-derived RAI over FO-derived RAI for Day-3 AKI by all metrics of outcome used (UOP, Cr, or the “worse” variable). Including the FO metric for RAI improved the AUC for AKI measured by creatinine at Day 3 from 0.73 (RAI derived solely from ΔeCCl) to 0.75 (RAI derived from ‘worse’). Renal angina outperforms severity of illness scores for prediction of Day-3 AKI. For patients in cohort 1, the discrimination of day of admission RAI for Day-3 AKI was compared against Pediatric Risk of Mortality-II (PRISM-II) scores. Although the performance of FO-derived RAI is not as robust as ΔeCCl-derived RAI, it is comparable to PRISM-II and in some cases (Day-3 AKI outcome measured by creatinine clearance change (Cr)) improved. AUC values are expressed with 95% confidence intervals. Approximately 25% (35/144) of patients in C1 required an imputed baseline creatinine owing to a lack of a baseline creatinine from which to compute ΔeCCl on the day of admission for the RAI calculation. Only 9 of these 35 were ANG(+) on the day of admission and only 1 had Day-3 AKI (this patient was ANG(+)). For the remaining 109 patients who had a known baseline creatinine for calculation of ΔeCCl, the RAI discrimination for Day-3 AKI was greater than for PRISM-II values and similar to the cohort as a whole (Supplementary B online). Download .doc (.04 MB) Help with doc files Supplementary B RAI prediction of Day-3 AKI was superior to simply having Kidney Diseases Improving Global Outcomes (KDIGO) stage 1 injury on the day of admission; fulfillment of renal angina demonstrated higher positive predictive value (PPV), NPV and a higher Youden’s index28.Bohning D. Bohning W. Holling H. Revisiting Youden's index as a useful measure of the misclassification error in meta-analysis of diagnostic studies.Stat Methods Med Res. 2008; 17: 543-554Crossref PubMed Scopus (67) Google Scholar for severe subsequent AKI than KDIGO stage 1. ANG(+) demonstrated similar predictive efficacy for Day-3 AKI compared with KDIGO stages 2–3, but had higher sensitivity, higher NPV, and higher Youden’s index (Table 5).Table 5Fulfillment of renal angina outperforms KDIGO stages of AKI for prediction of subsequent severe AKINSensitivitySpecificityPPVNPVYouden’s indexKDIGO 12521 (8–41)84 (76–90)24 (9–45)82 (73–88)5KDIGO 2–32446 (28–66)91 (84–95)54 (33–74)87 (80–93)37ANG(+)5275 (55–89)73 (64–81)40 (27–55)92 (85–97)48Abbreviations: AKI, acute kidney disease; ANG, renal angina; KDIGO, Kidney Diseases Improving Global Outcomes; NPV, negative predictive value; PPV, positive predictive value; RAI, renal angina index.Renal angina outperforms signs of early injury alone for prediction of Day-3 AKI. On the day of admission, patients in cohort 1 were assessed for ANG(+) by RAI ≥ 8 and compared with KDIGO stage 1 or KDIGO stages 2 and 3. The results demonstrate that ANG(+) is superior to KDIGO stage 1 for prediction of severe subsequent AKI and as effective as KDIGO stages 2 and 3. Both sensitivity and negative predictive value for ANG(+) are higher than for KDIGO stages 2 and 3. Results are shown as percentages with 95% confidence intervals. Open table in a new tab Abbreviations: AKI, acute kidney disease; ANG, renal angina; KDIGO, Kidney Diseases Improving Global Outcomes; NPV, negative predictive value; PPV, positive predictive value; RAI, renal angina index. Renal angina outperforms signs of early injury alone for prediction of Day-3 AKI. On the day of admission, patients in cohort 1 were assessed for ANG(+) by RAI ≥ 8 and compared with KDIGO stage 1 or KDIGO stages 2 and 3. The results demonstrate that ANG(+) is superior to KDIGO stage 1 for prediction of severe subsequent AKI and as effective as KDIGO stages 2 and 3. Both sensitivity and negative predictive value for ANG(+) are higher than for KDIGO stages 2 and 3. Results are shown as percentages with 95% confidence intervals. Significant differences in PRISM-II scores between ANG(+) and ANG(-) patients were analyzed by risk tranche and found to be attributable to weighting of patients in each group (Supplementary C online). On the basis of the univariate associations observed for patients with or without Day-3 AKI in both C1 and C4 (cohorts with similar admission diagnoses), we constructed a multivariable predictive model for Day-3 AKI using fulfillment of ANG, patient age, and PRISM-II score. Independent predictors of Day-3 AKI were as follows: ANG(+) (odds ratio (OR)=3.91, 95% CI=1.89–8.2), age (OR=1.01 for 1-year increase in age, 95% CI=1.02–1.11), and PRISM-II score (OR=1.04 for 1-unit increase in score, 95% CI=1.0–1.1) (Supplementary C online). When compared directly, RAI outperformed PRISM-II for the prediction of AKI outcome measured either by UOP, change in creatinine, or the worse metric of the two (Table 4). Download .doc (.09 MB) Help with doc files Supplementary C Renal angina fulfillment identifies children at the highes
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