Voltar
Artigo Acesso aberto Revisado por pares
BIBTEX RIS

The risk of chronic kidney disease and mortality are increased after community-acquired acute kidney injury

2016; Elsevier BV; Volume: 90; Issue: 5 Linguagem: Inglês

10.1016/j.kint.2016.07.018

ISSN

1523-1755

Autores

Karina Soto, Pedro Campos, Iola Pinto, Bruno Rodrigues, Francisca Frade, Ana Luísa Papoila, Prasad Devarajan,

Tópico(s)

Trauma, Hemostasis, Coagulopathy, Resuscitation

Resumo

We investigated whether community-acquired acute kidney injury encountered in a tertiary hospital emergency department setting increases the risk of chronic kidney disease (CKD) and mortality, and whether plasma biomarkers could improve the prediction of those adverse outcomes. In a prospective cohort study, we enrolled 616 patients at admission to the emergency department and followed them for a median of 62.1 months. Within this cohort, 130 patients were adjudicated as having acute kidney injury, 159 transient azotemia, 15 stable CKD, and 312 normal renal function. Serum cystatin C and plasma neutrophil gelatinase-associated lipocalin (NGAL) were measured at index admission. After adjusting for clinical variables, the risk of developing CKD stage 3, as well as the risk of death, were increased in the acute kidney injury group (hazard ratio [HR], 5.7 [95% confidence interval, 3.8–8.7] and HR, 1.9 [95% confidence interval, 1.3–2.8], respectively). The addition of serum cystatin C increased the ability to predict the risk of developing CKD stage 3, and death (HR, 1.5 [1.1–2.0] and 1.6 [1.1–2.3], respectively). The addition of plasma NGAL resulted in no improvement in predicting CKD stage 3 or mortality (HR, 1.0 [0.7–1.5] and 1.2 [0.8–1.8], respectively). The risk of developing CKD stage 3 was also significantly increased in the transient azotemia group (HR, 2.4 [1.5–3.6]). Thus, an episode of community acquired acute kidney injury markedly increases the risk of CKD, and moderately increases the risk of death. Our findings highlight the importance of follow-up of patients with community acquired acute kidney injury, for potential early initiation of renal protective strategies. We investigated whether community-acquired acute kidney injury encountered in a tertiary hospital emergency department setting increases the risk of chronic kidney disease (CKD) and mortality, and whether plasma biomarkers could improve the prediction of those adverse outcomes. In a prospective cohort study, we enrolled 616 patients at admission to the emergency department and followed them for a median of 62.1 months. Within this cohort, 130 patients were adjudicated as having acute kidney injury, 159 transient azotemia, 15 stable CKD, and 312 normal renal function. Serum cystatin C and plasma neutrophil gelatinase-associated lipocalin (NGAL) were measured at index admission. After adjusting for clinical variables, the risk of developing CKD stage 3, as well as the risk of death, were increased in the acute kidney injury group (hazard ratio [HR], 5.7 [95% confidence interval, 3.8–8.7] and HR, 1.9 [95% confidence interval, 1.3–2.8], respectively). The addition of serum cystatin C increased the ability to predict the risk of developing CKD stage 3, and death (HR, 1.5 [1.1–2.0] and 1.6 [1.1–2.3], respectively). The addition of plasma NGAL resulted in no improvement in predicting CKD stage 3 or mortality (HR, 1.0 [0.7–1.5] and 1.2 [0.8–1.8], respectively). The risk of developing CKD stage 3 was also significantly increased in the transient azotemia group (HR, 2.4 [1.5–3.6]). Thus, an episode of community acquired acute kidney injury markedly increases the risk of CKD, and moderately increases the risk of death. Our findings highlight the importance of follow-up of patients with community acquired acute kidney injury, for potential early initiation of renal protective strategies. Acute kidney injury (AKI) is associated with high short-term morbidity and mortality.1Lameire N.H. Bagga A. Cruz D. et al.Acute kidney injury: an increasing global concern.Lancet. 2013; 382: 170-179Abstract Full Text Full Text PDF PubMed Scopus (617) Google Scholar, 2Remuzzi G. Horton R. Acute renal failure: an unacceptable death sentence globally.Lancet. 2013; 382: 2041-2042Abstract Full Text Full Text PDF PubMed Scopus (41) Google Scholar The global rising incidence, and the devastating yet potentially preventable short-term outcomes, has prompted the International Society of Nephrology's 0by25 initiative to increase AKI awareness and to change its prognosis.3Remuzzi G. Benigni A. Finkelstein F.O. et al.Kidney failure: aims for the next 10 years and barriers to success.Lancet. 2013; 382: 353-362Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar, 4Horton R. Berman P. Eliminating acute kidney injury by 2025: an achievable goal.Lancet. 2015; 385: 2551-2552Abstract Full Text Full Text PDF PubMed Scopus (14) Google Scholar, 5Mehta R.L. Cerdá J. Burdmann E.A. et al.International Society of Nephrology's 0by25 initiative for acute kidney injury (zero preventable deaths by 2025): a human rights case for nephrology.Lancet. 2015; 385: 2616-2643Abstract Full Text Full Text PDF PubMed Scopus (604) Google Scholar Indeed, it is now widely recognized that critically ill hospitalized patients who survive an AKI episode are at considerable risk for progression to chronic kidney disease (CKD).6Chawla L.S. Amdur R.L. Amodeo S. et al.The severity of acute kidney injury predicts progression to chronic kidney disease.Kidney Int. 2011; 79: 1361-1369Abstract Full Text Full Text PDF PubMed Scopus (514) Google Scholar, 7Ishani A. Xue J.L. Himmelfarb J. et al.Acute kidney injury increases risk of ESRD among elderly.J Am Soc Nephrol. 2009; 20: 223-228Crossref PubMed Scopus (885) Google Scholar, 8Wald R. Quinn R.R. Luo J. et al.for the University of Toronto Acute Kidney Injury Research GroupChronic dialysis and death among survivors of acute kidney injury requiring dialysis.JAMA. 2009; 302: 1179-1185Crossref PubMed Scopus (555) Google Scholar, 9Amdur R.L. Chawla L.S. Amodeo S. et al.Outcomes following diagnosis of acute renal failure in U.S. veterans: focus on acute tubular necrosis.Kidney Int. 2009; 76: 1089-1097Abstract Full Text Full Text PDF PubMed Scopus (238) Google Scholar, 10Chawla L.S. Eggers P.W. Star R.A. Kimmel P.L. Acute kidney injury and chronic kidney diseases as interconnected syndromes.N Engl J Med. 2014; 371: 58-66Crossref PubMed Scopus (1140) Google Scholar, 11Lo L.J. Go A.S. Chertow G.M. et al.Dialysis-requiring acute renal failure increases the risk of progressive chronic kidney disease.Kidney Int. 2009; 76: 893-899Abstract Full Text Full Text PDF PubMed Scopus (447) Google Scholar, 12Coca S.G. Singanamala S. Parikh C.R. Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis.Kidney Int. 2012; 81: 442-448Abstract Full Text Full Text PDF PubMed Scopus (1369) Google Scholar, 13Kellum J.A. Bellomo R. Ronco C. Kidney attack.JAMA. 2012; 307: 2265-2266Crossref PubMed Scopus (79) Google Scholar However, there is still no direct evidence for a causal relationship between AKI and long-term CKD or mortality in patients with less severe forms of community-acquired AKI, which constitutes the most common setting for AKI worldwide.5Mehta R.L. Cerdá J. Burdmann E.A. et al.International Society of Nephrology's 0by25 initiative for acute kidney injury (zero preventable deaths by 2025): a human rights case for nephrology.Lancet. 2015; 385: 2616-2643Abstract Full Text Full Text PDF PubMed Scopus (604) Google Scholar Furthermore, there are no reliable biomarkers to predict long-term adverse outcomes of AKI, another major unmet need identified by the 0by25 initiative,5Mehta R.L. Cerdá J. Burdmann E.A. et al.International Society of Nephrology's 0by25 initiative for acute kidney injury (zero preventable deaths by 2025): a human rights case for nephrology.Lancet. 2015; 385: 2616-2643Abstract Full Text Full Text PDF PubMed Scopus (604) Google Scholar although plasma biomarkers such as serum cystatin C14Soto K. Coelho S. Rodrigues B. et al.Cystatin C as a marker of acute kidney injury in the emergency department.Clin J Am Soc Nephrol. 2010; 5: 1745-1754Crossref PubMed Scopus (89) Google Scholar, 15Feng Y. Zhang Y. Li G. Wang L. Relationship of cystatin-C change and the prevalence of death or dialysis need after acute kidney injury: a meta-analysis.Nephrology (Carlton). 2014; 19: 679-684Crossref PubMed Scopus (16) Google Scholar and plasma neutrophil gelatinase-associated lipocalin16Mishra J. Dent C. Tarabishi R. et al.Neutrophil gelatinase-associated lipocalin (NGAL) as a biomarker for acute renal injury after cardiac surgery.Lancet. 2005; 365: 1231-1238Abstract Full Text Full Text PDF PubMed Scopus (1942) Google Scholar, 17Soto K. Papoila A.L. Coelho S. et al.Plasma NGAL for the diagnosis of AKI in patients admitted from the emergency department setting.Clin J Am Soc Nephrol. 2013; 8: 2053-2063Crossref PubMed Scopus (51) Google Scholar have short-term prognostic value. We aimed to determine whether an AKI episode encountered in a tertiary hospital emergency department setting has an impact on the long-term incidence of CKD and mortality, and whether serum cystatin C (SCysC) and plasma neutrophil gelatinase-associated lipocalin (pNGAL) measured during the index AKI episode could improve the prediction of those adverse outcomes. A total of 113,385 patients presented to the emergency department of Hospital Fernando Fonseca from March 1, 2008 to November 30, 2008. Among those, 4742 required hospital admission. Following the inclusion and exclusion criteria, 800 patients were eligible to be sequentially recruited and enrolled, and 616 consented to the initial inclusion and follow-up (Supplementary Figure S1). This cohort was studied during the index hospitalization14Soto K. Coelho S. Rodrigues B. et al.Cystatin C as a marker of acute kidney injury in the emergency department.Clin J Am Soc Nephrol. 2010; 5: 1745-1754Crossref PubMed Scopus (89) Google Scholar, 17Soto K. Papoila A.L. Coelho S. et al.Plasma NGAL for the diagnosis of AKI in patients admitted from the emergency department setting.Clin J Am Soc Nephrol. 2013; 8: 2053-2063Crossref PubMed Scopus (51) Google Scholar and was classified into 4 clinical groups: AKI (n = 130), transient azotemia (TAz) (n = 159), stable chronic kidney disease (sCKD) (n = 15), and normal function (NF) (n = 312). The cohort was followed after hospital discharge for a median time of 19.9 (P25–75, 7.3–26.2) and 62.1 (P25–75, 49.9–67.9) months (F1 and F2, respectively), as shown in Figure 1. Patient characteristics are shown in Table 1. Data regarding clinical diagnoses at discharge and the etiology of AKI and TAz are presented in Supplementary Tables S1 and S2, respectively.Table 1Patient characteristicsCharacteristicAll patients (n = 616)AKI 21.1% (n = 130)TAz 25.8% (n = 159)sCKD 2.4% (n = 15)NF 50.7% (n = 312)P valueAge (mean ± SD)59.1 ± 15.866.3 ± 12.258.4 ± 16.168.5 ± 11.556.0 ± 15.7<0.001aKruskal-Wallis test P-values.,bAge P < 0.001 comparing AKI with TAz and NF patients, AKI with CKD patients 0.496, TAz with CKD patients 0.007.Men (n [%])386 (62.7)84 (64.6)93 (58.5)10 (66.7)199 (63.8)0.65cChi-square test P-values.Nonblack (n [%])536 (87.0)114 (87.7)137 (86.2)15 (100.0)270 (86.5)0.56cChi-square test P-values.1st follow-up time (median [P25–P75])19.9 (7.3–26.2)17.3 (6.2–25.9)21.3 (11.1-26.4)19.9 (8.1–22.5)20.1 (5.8–26.5)0.34aKruskal-Wallis test P-values.2nd follow-up time (median [P25–P75])62.1 (49.9–67.9)57.3 (46.3–66.1)59.0 (48.4–66.9)55.6 (50.9–66.5)63.7 (52.0–68.7)0.05aKruskal-Wallis test P-values.SCr baseline (mg/dl) (median [P25–P75])0.8 (0.6–0.9)1.0 (0.7–1.2)0.7 (0.6–0.9)1.2 (1.1–1.9)0.7 (0.6–0.8)<0.001aKruskal-Wallis test P-values.,dSCr baseline P < 0.001 except between TAz and NF patients (P = 0.797).Baseline eGFR (ml/min/1.73 m2) (median [P25–P75])96.42 (82.02–111.35)75.58 (53.50–97.27)95.14 (84.00–113.99)54.30 (44.83–60.64)101.14 (91.17–114.02)<0.001aKruskal-Wallis test P-values.Discharge eGFR<0.001aKruskal-Wallis test P-values. Median (P25–P75)89.54 (65.78–106.21)47.42 (29.57–72.2)87.45 (71.27–104.76)48.46 (39.96–54.68)99.30 (85.61–113.32) n (%)616 (100)130 (100)159 (100)15 (100)312 (100)1st follow-up eGFR<0.001aKruskal-Wallis test P-values. Median (P25–P75)83.55 (63.17–103.49)42.78 (23.32–70.81)74.82 (53.46–96.13)45.72 (28.70–51.20)95.31 (77.16–107.12) n (%)499 (81)102 (78.5)132 (83)15 (100)249 (79.8)2nd follow-up eGFR<0.001aKruskal-Wallis test P-values.,eCorrespond to 2nd follow-up eGFR P < 0.001 except between TAz and NF patients (P = 0.009), and between sCKD and TAz patients (P = 0.002). Median (P25–P75)73.78 (50.67–96.54)40.29 (20.54–62.48)70.37 (53.55–91.03)30.47 (16.36–40.77)85.50 (69.70–101.80) n (%)335 (54.4)53 (40.8)80 (50.3)9 (60.0)193 (61.9)Main comorbidities (n [%]) HT391 (63.6)101 (77.7)98 (62.0)12 (80.0)180 (57.7)<0.001cChi-square test P-values. CVD219 (35.6)71 (54.6)61 (38.4)8 (53.3)79 (25.3)<0.001cChi-square test P-values. DM192 (31.3)55 (42.3)46 (29.1)8 (53.3)83 (26.8)0.003cChi-square test P-values. CHF81 (13.1)39 (30)21 (13.2)4 (26.7)17 (5.4)<0.001cChi-square test P-values. CLD50 (8.1)13 (10.0)11 (7.0)0 (0)26 (8.4)0.52cChi-square test P-values. CKD65 (10.6)43 (33.1)7 (4.4)15 (100.0)0 (0)<0.001cChi-square test P-values.CCI3.0 (2.0–5.0)5.0 (3.0–6.0)3.0 (1.0–4.0)5.0 (3.5–6.0)3.0 (2.0–5.0)<0.001aKruskal-Wallis test P-values.,fP < 0.001 except between NF and TAz (P = 1.000), TAz with CKD (P = 0.007), NF with CKD (P = 0.015), AKI with CKD (P = 1.000).Susceptibility stage III–IV70 (11.4)44 (33.8)9 (5.7)11 (73.3)6 (1.9)<0.001cChi-square test P-values.pNGAL T3 (ng/ml) (median [P25–P75])83.0 (60–138)167.0 (109–253)94.0 (60–136)115.0 (78–167)64.0 (60–93)<0.001aKruskal-Wallis test P-values.Clinical evolution All sepsis (n [%])188 (30.5)50 (38.5)60 (37.7)3 (20)75 (24)0.002cChi-square test P-values. Severe sepsis (n [%])27 (4.4)16 (2.3)7 (4.4)04 (1.3)<0.001cChi-square test P-values. ICU admission (n [%])53 (8.6)18 (13.8)11 (6.9)1 (6.7)23 (7.4)0.12cChi-square test P-values. MV (n [%])22 (3.6)9 (6.9)8 (5)05 (1.5)0.03cChi-square test P-values. Mean LOS (mean ± SD)11.3 ± 1016.9 ± 15.510.5 ± 8.48.5 ± 3.89.5 ± 6.9<0.001cChi-square test P-values. Outcomes Overall RRT (n [%])23 (3.7)14 (10.8)6 (3.8)1 (6.7)2 (0.6)<0.001gExtension of Fisher exact test P-values. Overall mortality (n [%])159 (25.8)58 (44.6)36 (22.6)2 (13.3)63 (20.2)<0.001cChi-square test P-values. Long-term CKD (n [%])199 (32.3)96 (73.8)49 (30.8)15 (100)39 (12.6)<0.001cChi-square test P-values.AKI, acute kidney injury; CCI: Comorbidity Charlson Index score; CHF: chronic heart failure; CKD: previous chronic kidney disease; CKD incidence, chronic kidney disease development during follow-up; CLD: chronic liver disease; CVD: cardiovascular disease; DM: diabetes mellitus; eGFR, glomerular filtration rate estimated based on serum creatinine using the Chronic Kidney Disease–Epidemiology Collaboration equation; HT, hypertension; ICU, intensive care unit; LOS, length of stay; MV, mechanical ventilation; NF, normal function; pNGAL, plasma neutrophil gelatinase-associated lipocalin; RRT, renal replacement therapy; sCKD, stable chronic kidney disease; SCr, serum creatinine; TAz, transient azotemia; T3, 12h study time.14Soto K. Coelho S. Rodrigues B. et al.Cystatin C as a marker of acute kidney injury in the emergency department.Clin J Am Soc Nephrol. 2010; 5: 1745-1754Crossref PubMed Scopus (89) Google Scholar, 20Chawla L.S. Kimmel P.L. Acute kidney injury and chronic kidney disease: an integrated clinical syndrome.Kidney Int. 2012; 82: 516-524Abstract Full Text Full Text PDF PubMed Scopus (573) Google Scholara Kruskal-Wallis test P-values.b Age P < 0.001 comparing AKI with TAz and NF patients, AKI with CKD patients 0.496, TAz with CKD patients 0.007.c Chi-square test P-values.d SCr baseline P < 0.001 except between TAz and NF patients (P = 0.797).e Correspond to 2nd follow-up eGFR P < 0.001 except between TAz and NF patients (P = 0.009), and between sCKD and TAz patients (P = 0.002).f P < 0.001 except between NF and TAz (P = 1.000), TAz with CKD (P = 0.007), NF with CKD (P = 0.015), AKI with CKD (P = 1.000).g Extension of Fisher exact test P-values. Open table in a new tab AKI, acute kidney injury; CCI: Comorbidity Charlson Index score; CHF: chronic heart failure; CKD: previous chronic kidney disease; CKD incidence, chronic kidney disease development during follow-up; CLD: chronic liver disease; CVD: cardiovascular disease; DM: diabetes mellitus; eGFR, glomerular filtration rate estimated based on serum creatinine using the Chronic Kidney Disease–Epidemiology Collaboration equation; HT, hypertension; ICU, intensive care unit; LOS, length of stay; MV, mechanical ventilation; NF, normal function; pNGAL, plasma neutrophil gelatinase-associated lipocalin; RRT, renal replacement therapy; sCKD, stable chronic kidney disease; SCr, serum creatinine; TAz, transient azotemia; T3, 12h study time.14Soto K. Coelho S. Rodrigues B. et al.Cystatin C as a marker of acute kidney injury in the emergency department.Clin J Am Soc Nephrol. 2010; 5: 1745-1754Crossref PubMed Scopus (89) Google Scholar, 20Chawla L.S. Kimmel P.L. Acute kidney injury and chronic kidney disease: an integrated clinical syndrome.Kidney Int. 2012; 82: 516-524Abstract Full Text Full Text PDF PubMed Scopus (573) Google Scholar The median estimated glomerular filtration rate (eGFR) values at different study times are shown in Table 1 and Figure 2a. Kidney function decreased in all 4 clinical groups, most markedly in AKI patients, with a decrease from 75.6 at baseline to 42.8 at F1, and 40.3 ml/min/1.73 m2 at F2. The TAz group displayed a decrease from 95.1 at baseline to 74.8 at F1 and 70.4 ml/min/1.73 m2 at F2. The sCKD group showed a progressive decrease in eGFR at both times of follow-up (54.3 at baseline, 45.7 at F1, and 30.5 ml/min/1.73 m2 at F2). The NF group showed very minimal decreases in kidney function, with eGFR median levels of 101.1, 95.3, and 85.5 ml/min/1.73 m2 at baseline, F1, and F2, respectively. Within all 4 groups, the eGFR change was significant when comparing baseline with F2 (P < 0.001). When analyzed as change in eGFR between baseline and F2, the ΔeGFRs in the AKI and TAz groups were significantly greater than those for the NF group (P < 0.001), as shown in Figure 2b. There were no significant differences in ΔeGFR between the NF and sCKD groups. In the AKI group, the incidence of CKD stage ≥3 (defined as eGFR ≤ 60 ml/min/1.73 m2) increased significantly during the follow-up period, with 64.7% (66 of 102) and 71.7% (38 of 53) reaching this outcome at F1 and F2, respectively (P < 0.001 comparing F1 and F2 with baseline) (Figure 3). The incidence also increased significantly in the TAz group to 30.3% (40 of 132) and 33.8% (27 of 80) at F1 and F2, respectively (P < 0.001 comparing F1 and F2 with baseline). In the NF group, only 6.8% (17 of 249) and 16.1% (31 of 193) developed CKD stage ≥3 at F1 and F2, respectively (P < 0.001 comparing F2 with baseline) (Table 2). Analysis of the cumulative incidence of CKD stage ≥3 demonstrated that 68% of AKI patients developed CKD by 5 years of follow-up, in contrast to 30% in the TAz group and 11.3% in the NF group (Figure 4).Table 2Patients with eGFR ≤ 60 ml/min by group classification and follow-up timeFollow-up timeN (%)AKI 21.1% (n = 130)TAz 25.8% (n = 159)NF 50.7% (n = 312)P valueBaseline616 (100.0)41/130 (31.5)7/159 (4.4)0 (0)<0.001aP-value obtained by Pearson chi-squared test, referring to the comparison between classification categories at each study time.Discharge616 (100.0)85/130 (65.4)20/159 (12.6)6/312 (1.9)<0.001aP-value obtained by Pearson chi-squared test, referring to the comparison between classification categories at each study time.1st follow-up (F1)499 (81.0)66/102 (64.7)40/132 (30.3)17/249 (6.8)<0.001aP-value obtained by Pearson chi-squared test, referring to the comparison between classification categories at each study time.2nd follow-up (F2)335 (54.4)38/53 (71.7)27/80 (33.8)31/193 (16.1)<0.001aP-value obtained by Pearson chi-squared test, referring to the comparison between classification categories at each study time.P-value<0.001bP-value obtained by mixed-effects logistic regression model, referring to the comparison of the odds of having eGFR ≤ 60 at study times, for each classification category.<0.001bP-value obtained by mixed-effects logistic regression model, referring to the comparison of the odds of having eGFR ≤ 60 at study times, for each classification category.<0.001bP-value obtained by mixed-effects logistic regression model, referring to the comparison of the odds of having eGFR ≤ 60 at study times, for each classification category.AKI, acute kidney injury; eGFR, estimated glomerular filtration rate; NF, normal function; TAz, transient azotemia. Number (%) of patients with eGFR ≤60 ml/min/1.73 m2 at discharge, and both follow-up time points (F1 and F2).a P-value obtained by Pearson chi-squared test, referring to the comparison between classification categories at each study time.b P-value obtained by mixed-effects logistic regression model, referring to the comparison of the odds of having eGFR ≤ 60 at study times, for each classification category. Open table in a new tab Figure 4Cumulative incidence of chronic kidney disease (CKD) during follow-up time. (a) Kaplan-Meier curve estimates (P < 0.001 for both transient azotemia [TAz] and acute kidney injury [AKI] compared with normal function [NF]) for the entire follow-up period are shown. (b) The cumulative incidence rates during the 5 years of follow-up are shown.View Large Image Figure ViewerDownload (PPT) AKI, acute kidney injury; eGFR, estimated glomerular filtration rate; NF, normal function; TAz, transient azotemia. Number (%) of patients with eGFR ≤60 ml/min/1.73 m2 at discharge, and both follow-up time points (F1 and F2). To explore the risk factors for developing CKD stage ≥3, several multivariable Cox regression models were fitted to the data (Table 3). Model 1 is the clinical model. In this model, following univariable analysis of sex, categorized age (<63 years is the reference category), race, categorized susceptibility,14Soto K. Coelho S. Rodrigues B. et al.Cystatin C as a marker of acute kidney injury in the emergency department.Clin J Am Soc Nephrol. 2010; 5: 1745-1754Crossref PubMed Scopus (89) Google Scholar, 17Soto K. Papoila A.L. Coelho S. et al.Plasma NGAL for the diagnosis of AKI in patients admitted from the emergency department setting.Clin J Am Soc Nephrol. 2013; 8: 2053-2063Crossref PubMed Scopus (51) Google Scholar, 18Mehta R.L. Chertow G.M. Acute renal failure definitions and classification: time for change?.J Am Soc Nephrol. 2003; 14: 2178-2187Crossref PubMed Scopus (263) Google Scholar all studied comorbidities, and the Charlson Comorbidity Index score (CCI),19Charlson M.E. Pompei P. Ales K.L. Mackenzie C.R. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis. 1987; 40: 373-383Abstract Full Text PDF PubMed Scopus (34253) Google Scholar the following multivariable model was obtained: age: hazard ratio (HR), 2.4 (95% confidence interval [CI], 1.7–3.4; P < 0.001); cardiovascular disease: HR, 1.9 (95% CI, 1.4–2.6; P < 0.001); CCI: HR, 1.9 (95% CI, 1.4–2.6; P < 0.001); and susceptibility: HR, 2.2 (95% CI, 1.5–3.0; P < 0.001). In model 2, the clinical classification of patient groups (AKI, TAz, and NF) was added to model 1, yielding significant HR for age, cardiovascular disease, and CCI, but not for susceptibility (P < 0.001, P = 0.007, P = 0.003, and P = 0.75, respectively). Patients with AKI had a 6-fold risk of developing CKD stage ≥3, when compared with NF (HR, 5.7; 95% CI, 3.8–8.7; P < 0.001). Patients with TAz had 2.4-fold risk of CKD stage ≥3 (HR, 2.4; 95% CI, 1.5–3.6; P < 0.001).Table 3Multivariable Cox regression models to identify risk factors for CKD stage 3VariablesModel 1clinicalAIC = 1908.4Model 2clinical + classificationAIC = 1841.9Model 3model 2 + SCysCAIC = 1720.0Model 4model 2 + pNGALAIC = 1774.6AgeaThe cut point of 63 years for age was obtained by the analysis of Martingale residuals, age <63 years old was considered as the reference category.2.4 (1.7–3.4)2.3 (1.6–3.2)2.1 (1.5–3.1)2.2 (1.5–3.2)CVD1.9 (1.4–2.6)1.5 (1.1–2.1)1.7 (1.2–2.4)1.5 (1.1–2.1)CCIbReference category: CCI ≤ 3.1.9 (1.4–2.6)1.6 (1.2–2.2)1.6 (1.1–2.2)1.7 (1.2–2.3)SusceptibilitycSusceptibility stages I to II considered as the reference category.2.2 (1.5–3.0)1.1 (0.7–1.5)0.8 (0.5–1.3)1.1 (0.7–1.6)ClassificationdReference category: NF. AKI5.7 (3.8–8.7)4.7 (2.9–7.7)5.6 (3.5–8.8) TAz2.4 (1.5–3.6)2.3 (1.5–3.5)2.3 (1.5–3.5)SCysC1.5 (1.1–2.0)pNGALeReference category: pNGAL ≤ 133 ng/ml.1.0 (0.7–1.5)AIC, Akaike Information Criterion; AKI, Acute Kidney Injury; CCI, Charlson Comorbidity Index; CVD, cardiovascular disease; pNGAL, plasma neutrophil gelatinase-associated lipocalin; SCysC, serum cystatin C; TAz, Transient azotemia. Results are expressed as hazard ratios (95% confidence intervals). Model 1: P < 0.001 for all variables; model 2: CVD P = 0.007, CCI P = 0.003, susceptibility P = 0.749, P < 0.001 for the remaining variables; model 3: CVD P = 0.002, CCI P = 0.011, susceptibility P = 0.399, SCysC P = 0.011, P < 0.001 for the remaining variables; model 4: CVD P = 0.010, CCI P = 0.002, susceptibility P = 0.779, pNGAL P = 0.809, P < 0.001 for the remaining variables.a The cut point of 63 years for age was obtained by the analysis of Martingale residuals, age <63 years old was considered as the reference category.b Reference category: CCI ≤ 3.c Susceptibility stages I to II considered as the reference category.d Reference category: NF.e Reference category: pNGAL ≤ 133 ng/ml. Open table in a new tab AIC, Akaike Information Criterion; AKI, Acute Kidney Injury; CCI, Charlson Comorbidity Index; CVD, cardiovascular disease; pNGAL, plasma neutrophil gelatinase-associated lipocalin; SCysC, serum cystatin C; TAz, Transient azotemia. Results are expressed as hazard ratios (95% confidence intervals). Model 1: P < 0.001 for all variables; model 2: CVD P = 0.007, CCI P = 0.003, susceptibility P = 0.749, P < 0.001 for the remaining variables; model 3: CVD P = 0.002, CCI P = 0.011, susceptibility P = 0.399, SCysC P = 0.011, P < 0.001 for the remaining variables; model 4: CVD P = 0.010, CCI P = 0.002, susceptibility P = 0.779, pNGAL P = 0.809, P < 0.001 for the remaining variables. Biomarkers measured during the index admission were added, separately, to model 2 to explore their ability to improve prediction of CKD stage ≥3. In model 3, addition of SCysC yielded a HR of 1.5 (95% CI, 1.1–2.0; P = 0.011). In model 4, addition of pNGAL did not increase the risk of developing CKD, with a HR of 1.0 (95% CI, 0.7–1.5; P = 0.809). In order to quantify the improvement resulting from adding clinical classification (model 2) to model 1, and from adding SCysC to model 2 (model 3), or pNGAL to model 2 (model 4), continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) measures for censored data were calculated. The NRI quantifies the correctness of upward and downward movement of predicted probabilities as a result of adding a new marker to an existing baseline model. The IDI quantifies the magnitudes of changes in those probabilities. Adding clinical classification to model 1 resulted in an improvement in predicting CKD stage ≥3 according to NRI events and nonevents (40.2%; 95% CI, 25.8–54.7; and 34.5%; 95% CI, 24.8–44.1, respectively), and to IDI events and nonevents (0.07; 95% CI, 0.04–0.09; and 0.03; 95% CI, 0.02–0.05, respectively). The change in predicting CKD stage ≥3 from adding SCysC to model 2 resulted in an improvement of NRI nonevents (41.0%; 95% CI, 31.3–50.7), and in a negligible change of NRI events and IDI (events and nonevents) (Table 4). The change in predicting CKD stage ≥3 from adding pNGAL to model 2 resulted in a worsening of NRI nonevents (−31.5%; 95% CI, −41.3 to −21.8), and in a negligible change in NRI events and IDI (events and nonevents) (Table 4). Additional measures of the performance of each of the 4 multivariable models are showed in Table 4. For all 4 models, the C-statistic value for predicting CKD stage ≥3 was in the 0.74 to 0.81 range.Table 4Performance of the multivariable Cox regression models for CKDPerformance measureModel 1clinicalModel 2clinical + classificationModel 3model 2 + SCysCModel 4model 2 + pNGALOverallLR statisticaFor all likelihood-ratio tests: model 1 nested in model 2, and model 2 nested in model 3 and model 4, P < 0.001 was obtained.70.56123.8569.29Explained variationbNagelkerke R2. (%)20.028.630.128.5Discrimination C-statistic0.7390.7940.8100.794Calibration Slope shrinkage estimate0.9650.9560.9370.952Added valuecCorresponds to the added value to the clinical model attained by classification, on the developed sample.dCorresponds to the added value to the clinical + classification model attained by SCysC on the developed sample.eCorresponds to the added value to the clinical + classification model attained by pNGAL on the developed sample. NRI events (% [95% CI])40.2 (25.8–54.7)9.2 (−5.7 to 24.1)12.4 (−2.3 to 27.1) NRI nonevents (% [95% CI])34.5 (24.8 to 44.1)41.0 (31.3 to 50.7)−31.5 (−41.3 to −21.8) IDI0.10 (0.07–0.13)0.009 (0.001–0.017)0.001 (−0.001 to 0.002) IDI events0.07 (0.04–0.09)0.005 (−0.002 to 0.013)0.002 (0.000–0.003) IDI nonevents0.03 (0.02–0.05)0.004 (0.000–0.007)−0.001 (−0.002 to 0.000)CI, confidence interval; CKD, chronic kidney disease; IDI, integrated discrimination improvement; LR, likelihood ratio; NRI, net reclassification improvement; pNGAL, plasma neutrophil gelatinase-associated lipocalin; SCysC, serum cystatin C.a For all likelihood-ratio tests: model 1 nested in model 2, and model 2 nested in model 3 and model 4, P < 0.001 was obtained.b Nagelkerke R2.c Corresponds to the added value to the clinical model attained by classification, on the developed sample.d Corresponds to the added value to the clinical + classification model attained by SCysC on the developed s

Referência(s)