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Featured Articles: Original Clinical Research Report

Prevention of Cardiac Surgery–Associated Acute Kidney Injury by Implementing the KDIGO Guidelines in High-Risk Patients Identified by Biomarkers: The PrevAKI-Multicenter Randomized Controlled Trial

Zarbock, Alexander MD*; Küllmar, Mira MD*; Ostermann, Marlies MD; Lucchese, Gianluca MD; Baig, Kamran MD; Cennamo, Armando MD; Rajani, Ronak MD; McCorkell, Stuart MD; Arndt, Christian MD; Wulf, Hinnerk MD; Irqsusi, Marc MD§; Monaco, Fabrizio MD; Di Prima, Ambra Licia MD; García Alvarez, Mercedes MD; Italiano, Stefano MD; Miralles Bagan, Jordi MD; Kunst, Gudrun MD#; Nair, Shrijit MD#; L’Acqua, Camilla MD**; Hoste, Eric MD††; Vandenberghe, Wim MD††; Honore, Patrick M. MD‡‡; Kellum, John A. MD§§; Forni, Lui G. MD‖‖; Grieshaber, Philippe MD¶¶; Massoth, Christina MD*; Weiss, Raphael MD*; Gerss, Joachim PhD##; Wempe, Carola PhD*; Meersch, Melanie MD*

Author Information
doi: 10.1213/ANE.0000000000005458

Abstract

KEY POINTS

  • Question: Is it feasible to implement a bundle of supportive measures in high-risk patients undergoing cardiac surgery in a multinational setting?
  • Findings: In this multicenter randomized clinical trial, we found that the implementation of the Kidney Disease: Improving Global Outcomes (KDIGO) bundle is feasible in a multinational setting and that these supportive measures significantly reduced the occurrence of moderate and severe acute kidney injury (AKI).
  • Meaning: The findings underpin the need for a definitive trial to evaluate whether the KDIGO bundle reduces the occurrence of AKI in high-risk patients after cardiac surgery.

Acute kidney injury (AKI) is one of the most common and challenging complications in the perioperative period with significant effects on both short- and long-term outcomes. In particular, patients undergoing cardiac surgery are predisposed to develop this clinical syndrome with reported rates approaching 80%.1 Depending on the severity of AKI, hospital mortality can be as high as 46.7%.2 Furthermore, AKI is known to be associated with other complications, including infection, bleeding, delirium, chronic cardiovascular diseases, chronic kidney disease (CKD), and chronic dialysis dependency.3 The pathophysiology of cardiac surgery–associated AKI (CSA-AKI) is complex and not fully understood, although the use of cardiopulmonary bypass (CPB) contributes to the development of CSA-AKI through inducing ischemia-reperfusion-injury as well as inflammatory response.4 The lack of specific treatment options for CSA-AKI highlights the need for preventive strategies.5

The Kidney Disease: Improving Global Outcomes (KDIGO) guidelines for AKI recommend the initiation of a bundle of supportive measures in patients at high risk for AKI.6 This bundle consists of avoidance of all nephrotoxic agents, prevention of hyperglycemia, optimization of hemodynamics and perfusion pressure, functional hemodynamic monitoring if necessary, and close monitoring of renal function. Identification of patients at high risk for AKI can be achieved through the use of biomarkers, including the markers of renal stress associated with cell cycle arrest, tissue inhibitor of metalloproteinases-2 (TIMP-2), and insulin growth factor–binding protein 7 (IGFBP7).7,8 Their advantage is that they are able to detect kidney stress before damage or loss of function, allowing the initiation of therapy much earlier compared to management guided by the functional biomarkers serum creatinine and urine output.9,10 Two single-center randomized controlled trials using a biomarker-based approach demonstrated that the implementation of a bundle of supportive measures significantly reduced the occurrence of AKI in patients undergoing cardiac and major abdominal surgery.11,12 In addition, a quality improvement initiative supports the results of these small randomized controlled trials.13 Therefore, a large definitive multicenter randomized controlled trial is needed to confirm these findings. In preparation, we performed a multicenter, multinational, randomized controlled trial to investigate the adherence to the study protocol including biomarker-guided implementation of the KDIGO bundle.

METHODS

Study Design and Ethics

We conducted a randomized controlled, multicenter, multinational, parallel-group trial in 12 centers across Europe. Institutional review board approval was obtained from the Research Ethics Committee of the Chamber of Physicians Westfalen-Lippe and the Westfalian Wilhelms University Muenster (2017-291-f-S) and the corresponding institutional review boards of all participating centers. The trial was registered before patient enrolment at clinicaltrials.gov (NCT03244514, principal investigator: A. Zarbock, date of registration: September 8, 2017). The study was conducted in accordance with the Declaration of Helsinki (version Fortaleza, 2013). Written informed consent was obtained from all participating patients according to local requirements and legislation.

Participants

Eligible patients included adults undergoing cardiac surgery involving the use of CPB. High risk for AKI was defined as having urinary [TIMP-2]•[IGFBP7] ≥ 0.3 (Nephrocheck Test, Biomérieux, France) 4 hours after CPB.7 Patients were excluded if any of the following criteria were present: preexisting AKI (≥KDIGO stage 1 defined by creatinine as well as urine output), need for cardiac assist devices (extracorporeal membrane oxygenation, ventricular assist devices, intraaortic balloon pump), pregnancy or breastfeeding, known glomerulonephritis/interstitial nephritis/vasculitis, CKD with an estimated glomerular filtration rate (eGFR) <20 mL/min/1.73 m2, chronic dialysis dependency, prior kidney transplant within the last 12 months, and participation in another interventional trial within the last 3 months. In addition, patients with any type of relationship with the investigator or used by the sponsor/investigator and patients held in an institution by legal or official order were excluded.14

Randomization and Masking

Eligible patients were randomized in a 1:1 ratio to 1 of the 2 treatment arms. Randomization was performed centrally using a web-based randomization system provided by the European Society of Intensive Care Medicine (ESICM). Randomization lists were stratified by center and generated using block randomization with randomly varying block sizes.

Blinding of the intervention was not possible. Study intervention was performed by clinical staff involved in anesthesia and perioperative care and supervised by member of the research team on a daily basis. End point assessment was performed by a blinded researcher not involved in providing anesthesia and perioperative care. Patients remained blinded to allocation as they were unaware of the study-related procedures.

Procedures

Patients scheduled for cardiac surgery were approached by a member of the research team before surgery and invited to give informed consent for participation in the study. The surgical procedure and perioperative care were performed according to the standard of care at each center. With informed consent in place, urinary [TIMP-2]•[IGFBP7] was measured 4 hours after CPB. Patients with urinary [TIMP-2]•[IGFBP7] ≥0.3 were eligible to be randomized. Patients assigned to the control group were managed according to the standard of care at each center, including the specification to keep mean arterial pressure (MAP) >65 mm Hg and central venous pressure (CVP) between 8 and 10 mm Hg. Patients assigned to the intervention group received treatment according to the KDIGO bundle consisting of the following measures: avoidance of nephrotoxic agents, discontinuation of angiotensin-converting enzyme inhibitor (ACEi), and angiotensin-II receptor blocker (ARB) during the first 48 hours after surgery, avoidance of hydroxyethyl starch (HES), gelatine, and chloride-rich solutions (including 0.9% saline), close monitoring of serum creatinine (every 12 hours), fluid balance and urine output (hourly), avoidance of hyperglycemia in the first 72 hours after surgery (defined as blood glucose levels >150 mg/dL for >3 hours), consideration of alternatives to radiocontrast agents, close hemodynamic monitoring by using a functional hemodynamic monitoring with an optimization of the volume status, and hemodynamic parameters according to a prespecified algorithm (Supplemental Digital Content, Figure S1, https://links.lww.com/AA/D416).

Outcomes

The primary end point was the compliance rate to the KDIGO bundle defined as proportion of patients who were treated according to the trial protocol (bundle fulfilled at all times). Adherence to the bundle was achieved if all elements of the bundle were met (Supplemental Digital Content, Table S1, https://links.lww.com/AA/D416). Secondary end points were the occurrence and severity of AKI within 72 hours (as defined by the KDIGO criteria), free days of vasoactive medications and mechanical ventilation through day 28, renal recovery (defined as serum creatinine <0.5 mg/dL higher than baseline)13,15 at days 30, 60, and 90, all-cause mortality at days 30, 60, and 90, length of stay in intensive care unit (ICU) and hospital, use of renal replacement therapy (RRT) at days 30, 60, and 90, and major adverse kidney events consisting of mortality, dialysis dependency or persistent renal dysfunction at day 90 (MAKE90).

Clinical variables were extracted from medical records. Adherence with the hemodynamic protocol was documented every 3 hours. Initiation of RRT was at the discretion of the ICU clinicians. Specific criteria for initiation of RRT were not included in the protocol.

Statistical Analysis

Descriptive statistical analyses were performed reporting frequency of categorical variables as well as location and scale statistics of quantitative variables (mean and standard deviation or median and quartiles). Categorical variables were compared between the randomized groups using χ2 test, and continuous variables were compared using Student t test or Wilcoxon test, as appropriate. The primary statistical analysis of the primary outcome adherence to the trial protocol provided confirmatory statistical evidence on a 5% significance level. Secondary end points were planned a priori. All other statistical analyses were considered exploratory. P values were regarded as noticeable (“significant”) in case P ≤ .05 without adjustment for multiplicity. Statistical analyses were performed using SAS software (Version 9.4 for Windows, SAS Institute Inc, Cary, NC) and IBM SPSS Statistics 26 for Windows (IBM Corporation, Somers, NY).

Sample Size

In the primary statistical analysis, the compliance rate of the study patients is estimated. As the compliance rate could not be quantified in advance, we pursued a worst-case approach and assumed a compliance rate of 50%. In this case, the 95% confidence interval (CI) has maximal width. Based on this assumption, with a sample size of n = 140 patients per group, the calculated 95% CI according to Clopper-Pearson ranges from 41% to 59%. Therefore, in case of any observed compliance rate apart from the worst-case scenario, the 95% CI is never wider than 59 − 41 = 18. This corresponds to an estimation of the compliance rate with a precision of ±9%.

RESULTS

Enrolment and Patients

Table 1. - Baseline and Operative Characteristics
Control (n = 142) Intervention (n = 136) Standardized effect size
Age, mean (±SD), y 66.0 (10.3) 66.9 (10.6) 0.73a
Male sex, no. (%) 101 (71.1) 94 (69.1) 0.37b
Weight, mean (±SD), kg 85.6 (17.2) 86.6 (20.3) 0.46a
SOFA score, mean (±SD) 9.6 (3.4) 10.2 (3.1) 1.58a
APACHE score, median (Q1, Q3) 20.0 (12.5, 22.0) 20.0 (17.5, 23.0) 1.72c
Preoperative creatinine, mean (±SD), mg/dL 1.0 (0.3) 1.0 (0.3) 0.29a
eGFR, median (Q1, Q3), mL/min/m2 83.1 (64.3, 90.0) 78.3 (60.0, 90.0) 1.01c
Comorbidities, no. (%)
 Hypertension 91 (64.1) 95 (69.9) 1.02b
 Diabetes 45 (31.7) 35 (25.7) 1.10b
 COPD 13 (9.2) 12 (8.8) 0.10b
 CKD 19 (13.4) 15 (11.0) 0.60b
 Previous heart surgery 15 (10.6) 11 (8.1) 0.69b
 Left ventricular ejection fraction <35% 7/78 (9.0) 3/66 (4.5) 1.04b
 Myocardial infarction 23 (16.2) 14 (10.3) 1.45b
 Atrial fibrillation 25 (17.6) 24 (17.6) 0.01b
Medication, no. (%)
 Aspirin 73 (51.4) 59 (43.4) 1.34b
 Clopidogrel 19 (13.4) 17 (12.5) 0.22b
 β-blockers 85 (59.9) 81 (59.6) 0.05b
 Statins 65 (45.8) 62 (45.6) 0.03b
 Diuretics 60 (42.3) 59 (43.4) 0.19b
 ACEi/ARBs 68 (47.9) 77 (56.6) 1.46b
Intraoperative times, median (Q1, Q3), min
 Aortic cross-clamp 79.0 (60.5, 110.5) 76.0 (55.8, 97.8) 0.10c
 CPB 111.0 (87.0, 154.5) 112.5 (82.3, 137.8) 0.18c
Procedure, no. (%)
 CABG only 46 (34.3) 41 (33.1)
 Valve only 46 (34.3) 53 (42.7)
 Combined 25 (18.7) 18 (14.5)
 Other 17 (12.7) 12 (9.7)
Urine biomarkers at 4 h after CPB, median (Q1, Q3)
 [TIMP-2]•[IGFBP7], ng/mL2/1000 0.50 (0.37, 0.76) 0.48 (0.37, 0.88) 0.17c
Data presented as mean (±standard deviation), median (Q1, Q3), or number (percentages).
Abbreviations: ACEi, angiotensin-converting enzyme inhibitors; APACHE II, Acute Physiology And Chronic Health Evaluation Score; ARBs, angiotensin-II receptor blockers; CABG, coronary artery bypass graft; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; CPB, cardiopulmonary bypass; eGFR, estimated glomerular filtration rate; SD, standard deviation; SOFA score, sequential organ failure assessment score; [TIMP-2]•[IGFBP7], tissue inhibitor of metalloproteinases-2 and insulin growth factor–binding protein 7.
aStandardized mean difference (t test statistic).
bSquare root of the χ2 statistic.
cWilcoxon Z statistic.

F1
Figure.:
Flow chart. Of 170 patients excluded for other reasons, 64 declined study participation, 56 had no written informed consent, no test was available in 15 cases, 12 could not be informed because of language barriers, 11 were based on clinical decision (eg, hematuria, methylene blue), 6 underwent emergency surgery, and 6 underwent reoperation before NC measurement. AKI indicates acute kidney injury; CKD, chronic kidney disease; GFR, glomerular filtration rate; NC, Nephrocheck; RRT, renal replacement therapy.

The trial was conducted between November 2017 and November 2019 in 12 centers in Germany, Belgium, Spain, Italy, and the United Kingdom. A total of 1447 patients were screened of whom 280 patients were enrolled and randomized to receive management according to the KDIGO bundle (n = 138) versus standard of care (n = 142). Two patients withdrew consent so that 278 patients were included in the final analysis (n = 136 intervention and n = 142 control group; Figure). The baseline and intraoperative characteristics did not differ between both groups (Table 1, Supplemental Digital Content, Table S2, https://links.lww.com/AA/D416). Clinical care outside the trial intervention was also not different (Table 1, Supplemental Digital Content, Table S2, https://links.lww.com/AA/D416). There were no significant differences in [TIMP-2]•[IGFBP7] values 4 hours after cardiac surgery.

Primary Outcome

Table 2. - Primary Outcome
Control (n = 142) Intervention (n = 136) P value OR (intervention versus control) (95% CI) RRRa (%) (95% CI) ARRb (%) (95% CI)
Primary outcome
 Patients treated according to KDIGO bundle, no./total no. (%) 6/142 (4.2) 89/136 (65.4) <.001 42.92 (17.61-104.60) 63.9 (54.4-71.4) 61.2 (52.6-69.9)
 Discontinuation of nephrotoxic agents, no. (%) 120 (84.5) 134 (98.5) <.001 12.28 (2.83-53.33) 90.5 (60.4-97.7) 14.0 (7.7-20.3)
 Optimization of hemodynamic and perfusion pressure, no. (%) 76 (53.5) 97 (71.3) .002 2.16 (1.31-3.55) 38.3 (15.2-55.1) 17.8 (6.6-29.0)
 Close monitoring of serum creatinine and urine output, no. (%) 63 (44.4) 124 (91.2) <.001 12.96 (6.57-25.54) 84.1 (72.2-90.9) 46.8 (37.4-56.3)
 Avoidance of hyperglycemia, no. (%) 35 (24.6) 106 (77.9) <.001 10.80 (6.19-18.85) 70.7 (59.3-78.9) 53.3 (43.4-63.2)
 Consideration of alternatives to radio contrast agents, no. (%) 138 (97.2) 134 (98.5) .440 1.94 (0.35-10.78) 47.8 (−180.4 to 90.3) 1.3 (−2.0 to 4.7)
 Discontinuation of ACEi/ARBs, no. (%) 129 (90.8) 131 (96.3) .064 2.64 (0.92-7.62) 59.8 (−9.6 to 85.3) 5.5 (−0.2 to 11.2)
 Avoidance of HES, gelatine, chloride- rich solutions, no. (%) 124 (87.3) 130 (95.6) .014 3.15 (1.21-8.18) 65.2 (14.9-85.8) 8.3 (1.8-14.7)
 Median number of preventive measures, median (Q1, Q3) 5 (4, 6) 7 (6, 7) <.001 2 (1-2)
 Number of preventive measures, no. (%) <.001
  1 0 (0) 0 (0)
  2 6 (4.2) 0 (0)
  3 9 (6.3) 2 (1.5)
  4 36 (25.4) 14 (10.3)
  5 50 (35.2) 15 (11.0)
  6 35 (24.6) 16 (11.8)
  7 6 (4.2) 89 (65.4)
P value of the χ2 test.
Abbreviations: ACEi, angiotensin-converting enzyme inhibitors; ARBs, angiotensin-II receptor blockers; ARR, absolute risk reduction; CI, confidence interval; HES, hydroxyethyl starch; KDIGO, Kidney Disease: Improving Global Outcomes; OR, odds ratio; RRR, relative risk reduction with asymptotic Wald confidence limits.
aRRR > 0 indicates treatment effects in favor of the interventional treatment group.
bARR > 0 indicates treatment effects in favor of the interventional treatment group.

Compliance with the KDIGO recommendations was 65.4% in the intervention group (95% CI, 56.8-73.4) and 4.2% in the control group (95% CI, 1.6-9.0). Compliance was significantly better in the intervention group compared to the control group (absolute risk reduction [ARR], 61.2% [95% CI, 52.6-69.9]; P < .001; Table 2). Components of the KDIGO bundle were implemented more commonly in the intervention group compared to the control group (Supplemental Digital Content, Table S1, https://links.lww.com/AA/D416, discontinuation of nephrotoxic drugs: 98.5% vs 84.5%, P < .001; optimization of hemodynamic monitoring: 71.3% vs 53.5%, P = .002; close monitoring of kidney function: 91.2% vs 44.4%, P < .001; avoidance of hyperglycemia: 77.9% vs 24.6%, P < .001). However, the use of alternatives to radiocontrast agents (98.5% vs 97.2%; P = .440) and discontinuation of ACEi or ARBs (96.3% vs 90.5%; P = .064) were statistically not different between the groups (Table 2).

Measures During the Intervention Period

Table 3. - Measures During the Intervention Period
Control (n = 142) Intervention (n = 136) Intervention versus control (95% CI)
Patients with catecholamines during intervention period, no. (%)
 Dobutamine 26 (18.8) 44 (32.6) OR = 2.08 (1.19-3.64)
 Epinephrine 16 (11.6) 21 (15.6) OR = 1.40 (0.70-2.82)
 Norepinephrine 104 (74.8) 99 (72.8) OR = 0.90 (0.53-1.54)
Catecholamines during intervention period, median (Q1, Q3), µga
 Dobutamine 0.0 (0.0, 0.0) 0.0 (0.0, 5.7) LS = 0.0 (0.0-0.0)
 Epinephrine 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) LS = 0.0 (0.0-0.0)
 Norepinephrine 0.1 (0.0, 0.4) 0.1 (0.0, 0.4) LS = 0.0 (−0.0 to 0.03)
Volume therapy during intervention period, median (Q1, Q3), mL
 Crystalloids 1340.0 (515.0, 2026.0) 1566.0 (762.5, 2457.0) LS = 237.5 (0.0-500.0)
 Colloids 0 (0, 0) 0 (0, 0) LS = 0.0 (0.0-0.0)
 Red blood cells 0 (0, 0) 0 (0, 0) LS = 0.0 (0.0-0.0)
 Fresh frozen plasma 0 (0, 0) 0 (0, 0) LS = 0.0 (0.0-0.0)
 Thrombocyte concentrates 0 (0, 0) 0 (0, 0) LS = 0.0 (0.0-0.0)
Others
 Atrial fibrillation within 12 h, no. (%) 29 (20.4) 26 (19.1) OR = 0.92 (0.51-1.66)
 Hyperglycemia,b no. (%) 107 (77.0) 107 (81.7) OR = 1.33 (0.74-2.41)
 ACEi/ARBs,c no. (%) 13 (9.2) 18 (13.2) OR = 1.51 (0.71-3.22)
 Nephrotoxic agents,d, no. (%)
  Contrast agents 4/140 (2.9) 5/136 (3.7) OR = 1.30 (0.34-4.94)
 Diuretics,e no. (%) 71 (50.0) 83 (61.0) OR = 1.57 (0.97-2.52)
Abbreviations: ACEi, angiotensin-converting enzyme inhibitors; ARBs, angiotensin-II receptor blockers; CI, confidence interval; LS, location shift (Hodges-Lehmann estimator with confidence limits); OR, odds ratio with asymptotic Wald confidence limits.
aFor patients who did not receive medication, a dose of 0 µg was accounted.
bDefined as prolonged hyperglycemia (blood glucose level ≥150 mg/dL) >3 h within the first 72 h after cardiac surgery.
cACEi and ARBs within 48 h after cardiac surgery.
dWithin 72 h after cardiac surgery.
eDiuretics within 72 h after cardiac surgery.

During the study period, significantly more patients in the intervention group received dobutamine compared to controls (32.6% vs 18.8%; P = .009) resulting in a significantly higher MAP at different time points (Table 3, Supplemental Digital Content, Table S3, https://links.lww.com/AA/D416). In addition, patients assigned to the intervention group received significantly more crystalloids (P = .044; Table 3). However, transfusion rates were statistically not different in the 2 groups. Although the use of inotropes was significantly higher in the intervention group, there was no difference in the incidence of atrial fibrillation (Table 3).

Secondary Outcomes

Table 4. - Secondary Outcomes
Control (n = 142) Intervention (n = 136) OR (intervention versus control) (95% CI) RRRa (%) (95% CI) ARRb (%) (95% CI)
AKI within 72 h, no./total no. (%) 59/142 (41.5) 63/136 (46.3) 1.21 (0.76-1.95) −11.5 (−45.5 to 14.6) −4.8 (−16.4 to 6.9)
Diagnosis based on, no. (%)
 Creatinine 22 (37.3) 24 (38.1)
 Urine output 27 (45.8) 26 (41.3)
 Both 10 (16.9) 13 (20.6)
Moderate to severe AKI, no./total no. (%) 34/142 (23.9) 19/136 (14.0) 0.52 (0.28-0.96) 41.7 (2.9-65.0) 10.0 (0.9-19.1)
Renal recovery at 90 d, no./total no. (%) 118/142 (83.1) 106/136 (77.9) 0.72 (0.40-1.31) −30.5 (−111.5 to 19.4) −5.2 (−14.5 to 4.1)
RRT during hospital stay, no./total no.(%) 9/142 (6.3) 6/136 (4.4) 0.68 (0.24-1.97) 30.4 (−90.3 to 74.5) 1.9 (−3.4 to 7.2)
RRT at day 90, no./total no. (%) 0 (0) 4 (3.3) 10.24 (0.55-192.14) −3.3 (−6.5 to −0.1)
90-d all-cause mortality, no./total no. (%) 4/132 (3.0) 4/121 (3.3) 1.09 (0.27-4.47) −9.1 (−326.6 to 72.1) −0.3 (−4.6 to 4.0)
ICU stay, median (Q1, Q3), d 2.0 (1.0, 5.0) 2.0 (1.0, 5.0) 0.0 (−1.0 to 0.0)
Hospital stay, median (Q1, Q3), d 11.0 (9.0, 15.0) 11.0 (9.0, 17.0) 0.0 (−1.0 to 1.0)
Free days through day 28 of mechanical ventilation, median (Q1, Q3), d 27.7 (27.3, 27.8) 27.7 (27.3, 27.8) −0.0 (−0.1 to 0.0)
Free days through day 28 of vasoactive medication, median (Q1, Q3), d 28.0 (27.0, 28.0) 28.0 (27.0, 28.0) 0.0 (0.0-0.0)
Abbreviations: AKI, acute kidney injury; ARR, absolute risk reduction; CI, confidence interval; ICU, intensive care unit; OR, odds ratio; RRR, relative risk reduction with asymptotic Wald confidence limits; RRT, renal replacement therapy.
aRRR > 0 indicates treatment effects in favor of the interventional treatment group.
bARR > 0 indicates treatment effects in favor of the interventional treatment group.

The overall rate of AKI within 72 hours after cardiac surgery was statistically not different between the groups (46.3% intervention versus 41.5% control group; ARR −4.8% [95% CI, −16.4 to 6.9]; P = .423; Table 4, Supplemental Digital Content, Table S4, https://links.lww.com/AA/D416). However, the occurrence of moderate to severe AKI (stage 2/3) was significantly lower in the intervention group as compared to the control group (14.0% vs 23.9%; ARR 10.0% [95% CI, 0.9-19.1]; P = .034). There were no significant differences between the 2 groups in the secondary outcomes, including free days through day 28 of vasoactive medications and mechanical ventilation, renal recovery at days 30, 60, and 90, all-cause mortality at days 30, 60, and 90, length of stay in ICU and hospital, use of RRT at days 30 and 60, and MAKE90 (Table 4, Supplemental Digital Content, Table S5, https://links.lww.com/AA/D416). However, use of RRT was higher in the intervention group at day 90 (3.3% intervention versus 0% control group; ARR, −3.3% [95% CI, −6.5 to −0.1]; P = .035).

Subgroup Analysis

Of 280 patients, 20 had a [TIMP-2]•[IGFBP7] concentration >2.0. In this subgroup, adherence with all components of the bundle was 84.6% in the intervention versus 0% in the control group. Moderate and severe AKI occurred in 23.1% randomized to the intervention compared to 71.4% in the control group. In contrast, in the low biomarker level subgroup, moderate and severe AKI developed in 13.0% of patients in the intervention and in 21.5% of the control group (Supplemental Digital Content, Tables S6, S7, https://links.lww.com/AA/D416).

DISCUSSION

In this multicenter, randomized controlled clinical trial of cardiac surgery patients at high risk for AKI, we demonstrated that the adherence to a protocol consisting of a bundle of supportive measures is feasible in a multinational setting. Additionally, we showed that the adherence to the KDIGO recommendations was low (<5%) for patients in the standard of care arm. Moreover, the occurrence of moderate to severe AKI (stages 2 and 3) was significantly lower in the intervention group compared to controls.

At present, based on international consensus, AKI is defined by a change of the functional biomarkers serum creatinine and urine output. These 2 functional biomarkers cannot be reliably used for early detection of AKI. In contrast, TIMP-2 and IGFBP7 may increase earlier following renal stress and allow the implementation of preventive measures well before clinical AKI becomes manifest.10,16 In other specialties, biomarkers are used for an early initiation of a therapy as well as the commencement of molecular-targeted therapies.17–19 Based on data in this study and in our previous single-center trial, it is reasonable to implement treatment strategies in high-risk patients to prevent AKI. As [TIMP-2]•[IGFBP7] has very good performance in predicting AKI7,8 and the biomarkers were used in 2 previous studies with a personalized approach, we used these biomarkers to identify cardiac surgery patients at high risk for CSA-AKI.

Although it has been shown that the adherence to guidelines is associated with better patient outcomes,20 the adherence to guidelines is generally very low. It tends to be lower if multiple bundles have to be used at the same time.21,22 Importantly, compliance rate is not influenced by the complexity of the implemented measures. This was highlighted by an observational study showing that only one-third of septic patients received the multifaceted sepsis bundle and a similar proportion of acute respiratory distress syndrome (ARDS) patients was exposed to low tidal volume ventilation.21 In line with these data, we recently reported the results of an observational study and showed that compliance with all elements of the KDIGO bundle was low in routine clinical practice in patients undergoing cardiac surgery with CPB.22 In patients randomized to standard care, all 6 aspects of the bundle were applied to only 5.3% of patients, and in 37.9% of patients, only 3 elements were applied. Furthermore, there was no difference in adherence with the KDIGO bundle between patients with and without AKI after cardiac surgery. These results were replicated in the control arm of our study in which only 4.2% of patients received the bundle despite being high risk as defined by [TIMP-2]•[IGFBP7] >0.3. In contrast to the sepsis bundle which is recommended for patients with established sepsis, the KDIGO bundle is recommended only for patients at high risk for AKI, but not all patients in general. The rationale behind this recommendation is that the implementation of some of the measures might be associated with potential adverse events, including increased risk of thrombosis, hypoglycemia, arrhythmias, and ischemia.23,24 In addition, due to the interaction with other guidelines, time, and financial costs, it is not feasible to implement the KDIGO recommendations in all patients.

The pathophysiology of AKI after cardiac surgery is very complex and clinical trials using pharmacological and nonpharmacological strategies to prevent the development of AKI have failed,25,26 although some nonpharmacological treatments may be effective in high-risk patients.9 Based on the complex pathophysiology of CSA-AKI, we implemented the KDIGO bundle as a multifactorial approach that may reduce inflammation, improve renal perfusion, and decrease oxidative stress.4 Here, we showed that the implementation of the KDIGO guidelines resulted in an increased use of dobutamine and crystalloids, resulting in a significantly higher blood pressure at different time points and a shift toward less severe AKI (from KDIGO stage 2 to stage 1). It is possible that the frequent use of dobutamine in the intervention group had an effect on the development or progression of AKI. During CPB, blood is redistributed away from the kidneys which might lead to focal ischemia in some areas within the kidneys. Increasing blood flow in the perioperative period with dobutamine might lead to more homogeneous perfusion of the kidneys and reduce local ischemia. The results of our trial are in contrast to the findings of a recently published study by Osawa et al27 which showed that goal-directed therapy did not affect the incidence of AKI. However, the study included patients who were not at high risk for AKI, whereas our trial only randomized patients at high risk for AKI. Our results are in line with other studies, demonstrating that the implementation of a supportive bundle can reduce the occurrence of AKI.11,12,28

AKI is a heterogeneous condition consisting of distinct phenotypes based on its etiology, prognosis, and molecular pathways, and that may potentially require different therapies. Serum creatinine (SCr)-based AKI definitions provide no information on these AKI phenotypes. However, biomarkers can identify different phenotypes.29 We have recently shown that elevated renin levels could be used to identify high-risk patients for cardiovascular instability and AKI who would benefit from timely intervention that could improve their outcomes.30 Combining renin with TIMP-2•IGFBP7 identified the group with the highest risk to develop a moderate or severe AKI after cardiac surgery, suggesting that the combination of both biomarkers identify another AKI phenotype.30

AKI after cardiac surgery is associated with increased morbidity and mortality31 and any reduction in the AKI rate should have an impact on patient outcomes. Although we showed in this multicenter trial that implementing the KDIGO bundle significantly reduced the occurrence of moderate to severe CSA-AKI, this intervention had no impact on the secondary outcomes for several reasons. First, in contrast to our single-center trial,11 but similar to a trial in abdominal surgery,12 we did not impact on the rates of “any stage” AKI. Indeed, we increased the rates of stage 1 AKI but reduced those in stage 2. While it would have been desirable to reduce all AKI, stage 2 AKI is associated with significant greater short- and long-term complications compared to stage 1.2 Second, the complication rate was low but this trial was not powered to demonstrate differences of rare events. This might be the reason for the lack of association between AKI and prolonged renal dysfunction demonstrated in other studies. Finally, a follow-up duration of 90 days might be too short, especially since other studies demonstrated that even milder forms of AKI, including isolated stage 1 oliguria, are associated with adverse long-term consequences.2

The strengths of this study include a personalized treatment approach through the use of a commercially available biomarker, the easy adoption of our study based on the limited numbers of exclusion criteria and the fact that the study was conducted multinationally.

The study is not without limitations. First, although we demonstrated a reduced occurrence of moderate to severe AKI in the intervention group, we could not detect a difference in major adverse kidney events at day 90. The protective effect on AKI might be by chance. However, the findings are in accordance with already published results from single-center studies and likely reflect the beneficial effect of the bundle.11 The latter may be explained by the fact that the study was not powered to detect a difference in major adverse kidney events at day 90. Second, although randomized to the intervention group, a certain proportion of patients did not receive the bundles. This demonstrates that even under study conditions, it is difficult to successfully implement the bundle in every patient and underlines the importance of the findings to sensitize clinicians. Third, it remains unclear whether all components of the bundle are necessary to prevent AKI or whether some of the measures are more effective than others.

In conclusion, adherence to a bundle of supportive measures recommended by the KDIGO guidelines in patients at high risk identified by biomarkers is feasible in a multicenter and multinational setting, a finding that will underpin a definitive trial. In addition, in the intervention group (bundle of supportive measures), a reduced occurrence of moderate to severe AKI could be observed.

ACKNOWLEDGMENTS

The authors thank all the participating centers and corresponding staff members involved in this trial for their hard work. The authors also thank the ESICM Trials group and the ESICM AKI section for supporting this trial.

DISCLOSURES

Name: Alexander Zarbock, MD.

Contribution: This author helped conceive the study; design the trial; and draft, read, and approve the manuscript.

Conflicts of Interest: A. Zarbock has received lecture/consulting fees from Astute Medical/Biomerieux, Fresenius and Baxter, unrelated to the current study and has received grant support from Astute Medical/Biomerieux, unrelated to the current study.

Name: Mira Küllmar, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Marlies Ostermann, MD.

Contribution: This author helped design the trial; perform the study; and draft, read, and approve the manuscript.

Conflicts of Interest: M. Ostermann has received lecture fees from Biomerieux, Fresenius Medical and Baxter.

Name: Gianluca Lucchese, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Kamran Baig, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Armando Cennamo, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Ronak Rajani, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Stuart McCorkell, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Christian Arndt, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: C. Arndt has received lecture fees from Baxter.

Name: Hinnerk Wulf, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Marc Irqsusi, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Fabrizio Monaco, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Ambra Licia Di Prima, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Mercedes García Alvarez, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Stefano Italiano, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Jordi Miralles Bagan, MD.

Contribution: This author helped perform the study and draft, read, and approved the manuscript.

Conflicts of Interest: None.

Name: Gudrun Kunst, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Shrijit Nair, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Camilla L’Acqua, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Eric Hoste, MD.

Contribution: This author helped design the trial; perform the study; and draft, read, and approve the manuscript.

Conflicts of Interest: E. Hoste has received traveling and lecture fees from Astute Medical, Alexion, Sopachem, AM Pharma.

Name: Wim Vandenberghe, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Patrick M. Honore, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: John A. Kellum, MD.

Contribution: This author helped design the trial and draft, read, and approve the manuscript.

Conflicts of Interest: J. A. Kellum has received lecture/consulting fees from Astute Medical/Biomerieux, Fresenius and Baxter, unrelated to the current study and has received grant support from Astute Medical/Biomerieux, unrelated to the current study.

Name: Lui G. Forni, MD.

Contribution: This author helped design the trial and draft, read, and approve the manuscript.

Conflicts of Interest: L. G. Forni has received research funding from Baxter and Ortho Clinical Diagnostics, consultancy fees from Medibeacon/La Jolla Pharmaceuticals and honoraria from Biomerieux/Astute.

Name: Philippe Grieshaber, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Christina Massoth, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Raphael Weiss, MD.

Contribution: This author helped perform the study and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Joachim Gerss, PhD.

Contribution: This author helped perform statistical analyses and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Carola Wempe, PhD.

Contribution: This author helped design the trial; perform study coordination; and draft, read, and approve the manuscript.

Conflicts of Interest: None.

Name: Melanie Meersch, MD.

Contribution: This author helped conceive the study; design the trial; perform study coordination; and draft, read, and approve the manuscript.

Conflicts of Interest: M. Meersch has received lecture/consulting fees from Astute Medical/Biomerieux, Fresenius and Baxter, unrelated to the current study.

This manuscript was handled by: Jean-Francois Pittet, MD.

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