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Perioperative Medicine  |   May 2017
Long-term Effects of Remote Ischemic Preconditioning on Kidney Function in High-risk Cardiac Surgery Patients: Follow-up Results from the RenalRIP Trial
Author Notes
  • From the Department of Anaesthesiology, Intensive Care Medicine and Pain Medicine (A.Z., H.V.A., C.S., M.K., M.M.) and Department of Cardiac Surgery (S.M.), University Hospital Münster, Münster, Germany; Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania (J.A.K.); Department of Anaesthesiology and Intensive Care Medicine, University Hospital Tübingen, Tübingen, Germany (P.R.); and Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany (D.G.).
  • This article is featured in “This Month in Anesthesiology,” page 1A.
    This article is featured in “This Month in Anesthesiology,” page 1A.×
  • Corresponding article on page 763.
    Corresponding article on page 763.×
  • This article has a video abstract.
    This article has a video abstract.×
  • Submitted for publication October 7, 2016. Accepted for publication January 27, 2017.
    Submitted for publication October 7, 2016. Accepted for publication January 27, 2017.×
  • Address correspondence to Dr. Zarbock: Department of Anesthesiology, Critical Care Medicine and Pain Therapy, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149 Münster, Germany. zarbock@uni-muenster.de. This article may be accessed for personal use at no charge through the Journal Web site, www.anesthesiology.org.
Article Information
Perioperative Medicine / Clinical Science / Cardiovascular Anesthesia / Renal and Urinary Systems / Electrolyte Balance
Perioperative Medicine   |   May 2017
Long-term Effects of Remote Ischemic Preconditioning on Kidney Function in High-risk Cardiac Surgery Patients: Follow-up Results from the RenalRIP Trial
Anesthesiology 5 2017, Vol.126, 787-798. doi:10.1097/ALN.0000000000001598
Anesthesiology 5 2017, Vol.126, 787-798. doi:10.1097/ALN.0000000000001598
Abstract

Background: In a multicenter, randomized trial, the authors enrolled patients at high-risk for acute kidney injury as identified by a Cleveland Clinic Foundation score of 6 or more. The authors enrolled 240 patients at four hospitals and randomized them to remote ischemic preconditioning or control. The authors found that remote ischemic preconditioning reduced acute kidney injury in high-risk patients undergoing cardiac surgery. The authors now report on the effects of remote ischemic preconditioning on 90-day outcomes.

Methods: In this follow-up study of the RenalRIP trial, the authors examined the effect of remote ischemic preconditioning on the composite endpoint major adverse kidney events consisting of mortality, need for renal replacement therapy, and persistent renal dysfunction at 90 days. Secondary outcomes were persistent renal dysfunction and dialysis dependence in patients with acute kidney injury.

Results: Remote ischemic preconditioning significantly reduced the occurrence of major adverse kidney events at 90 days (17 of 120 [14.2%]) versus control (30 of 120 [25.0%]; absolute risk reduction, 10.8%; 95% CI, 0.9 to 20.8%; P = 0.034). In those patients who developed acute kidney injury after cardiac surgery, 2 of 38 subjects in the remote ischemic preconditioning group (5.3%) and 13 of 56 subjects in the control group (23.2%) failed to recover renal function at 90 days (absolute risk reduction, 17.9%; 95% CI, 4.8 to 31.1%; P = 0.020). Acute kidney injury biomarkers were also increased in patients reaching the major adverse kidney event endpoint compared to patients who did not.

Conclusions: Remote ischemic preconditioning significantly reduced the 3-month incidence of a composite endpoint major adverse kidney events consisting of mortality, need for renal replacement therapy, and persistent renal dysfunction in high-risk patients undergoing cardiac surgery. Furthermore, remote ischemic preconditioning enhanced renal recovery in patients with acute kidney injury.

What We Already Know about This Topic
  • Previous studies have demonstrated that acute kidney injury is associated with an increased risk of short-term adverse outcomes after cardiac surgery

  • This study is a follow-up study from the RenalRIP cohort to determine the effects of remote ischemic preconditioning on the 90-day composite endpoint major adverse kidney events consisting of all-cause mortality, the receipt of renal replacement therapy, and persistent renal dysfunction without dialysis

What This Article Tells Us That Is New
  • Remote ischemic preconditioning significantly reduced the 3-month incidence of a composite endpoint major adverse kidney events consisting of mortality, need for renal replacement therapy, and persistent renal dysfunction in high-risk patients undergoing cardiac surgery

IN cardiac surgery patients, acute kidney injury (AKI) is associated with an increased risk of short-term adverse outcomes.1,2  However, longer-term outcomes for patients with AKI need further attention.3,4  Moreover, interventions that reduce AKI after cardiac surgery may not impact long-term outcomes.5  Thus, after an episode of AKI, the risks of mortality and of subsequent chronic kidney disease with or without the need for renal replacement therapy remain uncertain. A recently published meta-analysis by Coca et al.6  reported absolute rates of chronic kidney disease after AKI approximately 50% higher than that of mortality, but the analysis was limited due to high statistical heterogeneity.
 
The effect of remote ischemic preconditioning (RIPC) on renal function after cardiac surgery has been investigated in the last years, offering conflicting results.7–9  We have recently published the results of a multicenter, randomized controlled trial investigating the effects of RIPC on the occurrence of AKI in high-risk patients undergoing cardiac surgery and demonstrated that this intervention significantly reduced the rate of AKI and need for renal replacement therapy.9  Moreover, we have shown that the effectiveness of this intervention was strongly associated with the release of cell cycle arrest biomarkers into the urine. Patients who responded to RIPC with an increase in urinary tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7; [TIMP-2]·[IGFBP7]) greater than or equal to 0.5 (ng/ml)2/1,000 before surgery had a significantly reduced rate of AKI compared to patients with lower urinary [TIMP-2]·[IGFBP7]. These same biomarkers predicted AKI when they increased as a result of surgery, as shown previously.10–12  However, longer-term outcomes of patients treated with RIPC are unknown.
Here, we report a follow-up study from the RenalRIP cohort to determine the effects of RIPC on the 90-day composite endpoint major adverse kidney events (MAKE) consisting of all-cause mortality, the receipt of renal replacement therapy, and persistent renal dysfunction without dialysis. Documentation of long-term effects of RIPC on renal recovery and other outcomes is important for understanding the biology of this intervention and for patient care.
Materials and Methods
The RenalRIP trial has been described in detail elsewhere.9  Briefly, 240 patients at high risk for AKI who underwent cardiac surgery with the use of cardiopulmonary bypass (CPB) were enrolled at four sites in Germany from August 2013 to June 2014. We used a Cleveland Clinic Foundation score of 6 or more to define patients at high risk for AKI.13  The score is composed of different risk factors, including patient characteristics, comorbidities, and type of surgery. Patients were randomized on a 1:1 basis stratified by center. On the day of surgery, patients were assigned to undergo either RIPC or sham-RIPC (control) and the intervention was provided by an investigator not involved in the care of the patient. Patients, anesthesiologists and staff providing care of the patient, cardiac surgeons, and intensive care physicians were blinded for treatment assignment. For induction of anesthesia, all patients received sufentanil with either benzodiazepines (Bochum, Freiburg, and Tübingen; 100% in these centers) alone or in combination with barbiturates (Münster; 100% in this center). To maintain anesthesia, a combination of sufentanil and volatile anesthetics was used (sufentanil: all centers, 100% in these centers; isoflurane: Bochum, 100% in this center; sevoflurane: Freiburg, Münster, and Tübingen, 100% in these centers). Propofol was not used due to potential interference with RIPC.14  None of the patients received regional anesthesia. After induction of anesthesia and before skin incision, we performed RIPC consisting of three cycles of 5-min inflation of a blood pressure cuff to 200 mmHg (or at least to a pressure 50 mmHg higher than the systolic arterial pressure) to one upper arm followed by 5-min reperfusion with the cuff deflated. In patients assigned to the control group, sham-RIPC intervention was induced by three cycles of upper-limb pseudo-ischemia (low pressure: 5-min blood pressure cuff inflation to a pressure of 20 mmHg and 5-min cuff deflation). The surgical procedure and perioperative care were performed according to the standard at each center. According to the recommendations of the American College of Cardiology Foundation (Washington, D.C.) and the Kidney Disease: Improving Global Outcomes (KDIGO; Brussels, Belgium) guidelines, angiotensin-converting enzyme inhibitors (ACEi) and angiotensin-II receptor blockers (ARBs) were discontinued before surgery. Medication was reconvened once the patient was hemodynamically stable. The RenalRIP trial was approved by the institutional review board at each site. All subjects (or legally authorized representatives) provided written informed consent. The trial is registered at http://www.drks.de (identifier: DRKS00005333; principal investigator: Dr. Zarbock; registration date: July 11, 2013).
Sample and Data Collection
Blood samples were collected by standard methods before surgery and at prespecified time points after surgery for measurement of serum creatinine concentrations (4 h after cardiac surgery and on every morning for at least 3 days after cardiac surgery). Urine samples for biomarkers were collected before RIPC/sham-RIPC, after inducing RIPC/sham-RIPC, and at 4, 12, and 24 h after surgery. The samples were centrifuged, and urine supernatants and serum were frozen within 2 h after collection and thawed immediately before analysis. All clinical data, including patient demographics, need for renal replacement therapy, length of stay in the intensive care unit, length of stay in the hospital, 30- and 90-day mortality, previous health history, serum creatinine, concentrations of various biomarkers, and hourly urine output, were collected and stored in a password-protected data set.
Clinical Endpoints
The key endpoint of this follow-up analysis MAKE consisting of the composite of death, dialysis, or persistent renal dysfunction at day 90 was determined from hospital records or for patients discharged alive and not on dialysis, telephone calls to the general practitioner, the subject or family members at 3 months after enrollment. The individual components were defined as follows: if the patient died, the mortality endpoint was met but not dialysis dependency or persistent renal dysfunction. If the patient met the dialysis endpoint, the subject was also defined as persistent renal dysfunction. If the persistent renal dysfunction endpoint was met but not the dialysis endpoint, then only persistent renal dysfunction was met. If the subject was determined to have died or been on dialysis at the time of telephone assessment, the date of death or dialysis was recorded. We defined persistent renal dysfunction as serum creatinine levels greater than or equal to 0.5 mg/dl higher than baseline serum creatinine15,16  in patients not receiving dialysis or dialysis dependency. Patients who died within 90 days could not be evaluated for persistent renal dysfunction. This composite endpoint has been recommended because death is a competing endpoint otherwise.17  AKI status during the first 72 h after enrollment was classified using the KDIGO guidelines on the basis of serum creatinine and urine output.18  The reference values for serum creatinine were obtained as described previously.9 
Laboratory Methods
As described previously, urinary TIMP-2 and IGFBP7 were analyzed by investigators not involved in the care of the patient and blinded to clinical data using a clinical immunoassay (NephroCheck Test and ASTUTE140 Meter; Astute Medical, USA).9  The ASTUTE140 Meter automatically multiplies the concentrations of the two biomarkers together and divides this product by 1,000 to report a single numerical test result with units of (nanograms per milliliter)2/1,000 (the units for all [TIMP-2]·[IGFBP7] test values in this report). Urine neutrophil gelatinase–associated lipocalin (NGAL) was measured with a commercially available assay (Dianova, Germany) according to the manufacturer’s protocol.
Statistical Analysis
For the parental analysis, we calculated a necessary sample size based on the primary endpoint (occurrence of AKI within 72 h after cardiac surgery) using nQuery Advisor software (Statistical Solutions; version 7). The primary efficacy analysis was intended to show superiority of RIPC in high-risk cardiac surgery patients, applying a two-sided chi-square test on significance level α=0.05. Based on an observational study, we performed in a similar patient population12  the expected AKI rate in the control group treated with sham-RIPC was 50%. The expected absolute risk reduction (ARR) for AKI was 18% based on a published single-center study investigating the effect of RIPC on AKI after cardiac surgery.19  Resulting from these considerations and a power of 80%, the required sample size was calculated to be 117 evaluable patients per treatment group, i.e., 234 in total. An additional six patients were recruited in order to account for loss to follow-up or nonevaluable data.
Here, we describe the statistical methods that were selected to analyze the secondary outcomes of the RenalRIP trial (preplanned analyses) and the composite outcome variable MAKE at day 90. Continuous variables are described by mean ± SD in case of normally distributed data and as median (Q1 to Q3) in case of skewed data. Categorical variables are described by absolute and relative frequencies. Differences between groups are reported as ARR and its corresponding 95% CI. The key outcome parameter of this analysis (MAKE at day 90) as well as the other secondary outcomes (persistent renal dysfunction, dialysis, and mortality) were analyzed by chi-square test to test for association with the treatment group (RIPC vs. Sham-RIPC). If assumptions for the chi-square test were not fulfilled, Fisher exact test was applied. For theses analyses, odds ratios (OR) and 95% CI are reported. To assess the sensitivity of the results, we defined persistent renal dysfunction as serum creatinine increase of 50% or more compared to baseline value or dialysis dependency. Additionally, to evaluate the treatment effect under consideration of the recruiting centers, we applied the Cochran–Mantel–Haenszel (CMH) test. Mortality was also analyzed by time-to-event methods, i.e., Kaplan–Meier plots and Cox proportional hazards model.
For all outcomes, adjusted ORs and corresponding 95% CIs were estimated in a logistic regression model containing the treatment group, age, gender, and chronic kidney disease status. The variables examined in the regression model were selected because these are known to be associated with the development of AKI.13  Biomarker measurements were analyzed at each time point individually using Mann–Whitney U tests to compare the treatment groups. Receiver operator characteristic (ROC) curve analysis was applied to assess a biomarker’s predictive performance with respect to MAKE prediction at day 90. As a result, the area under the ROC curve (AUC) and its 95% CI were reported. AUCs were tested against the null hypothesis H0: AUC = 0.5. Cutpoints for selected time points were determined by maximizing the Youden index: max (sensitivity + specificity – 1).20,21 
For all statistical tests, a significance level of 5% was assumed and no correction for the multiple testing problem was applied. The results are thus interpreted as to generate new hypotheses. Analyses were performed using SPSS 22 (IBM Corp., USA, Released 2013; IBM SPSS Statistics for Windows, version 22.0) and the SAS software 9.4 (SAS Institute Inc., USA).
Results
In total, 240 subjects were enrolled in the RenalRIP study, and for this analysis, no subjects were excluded as illustrated in figure 1. In our previous article,9  we have shown the demographic and operative characteristics of the patients in the control and intervention groups. Table 1 describes the demographic and operative data of MAKE-positive patients versus MAKE-negative patients. Full 3-month outcome was known for 240 (100% of the initial cohort) patients, with 47 (19.6%) patients meeting the endpoint of MAKE and 193 (80.4%) patients not meeting the endpoint MAKE at day 90. Patients with MAKE at day 90 were more likely to be older (P = 0.002); the number of patients with congestive heart failure and ACEi or ARBs was higher in the group (P = 0.018 and P = 0.024, respectively) and showed higher rates of major postoperative bleeding (P = 0.009). In addition, appendix A1 shows the demographic and operative data stratified by Sham-RIPC and RIPC.
Table 1.
Characteristics of Patient Cohort
Characteristics of Patient Cohort×
Characteristics of Patient Cohort
Table 1.
Characteristics of Patient Cohort
Characteristics of Patient Cohort×
×
Fig. 1.
Patient enrollment and allocation to the remote ischemic preconditioning (RIPC) and control (Sham-RIPC) arms. The first part (light blue box) shows the recruitment of the initial RenalRIP trial,9  while the second part (dark blue box) shows the analyzed cohort of this follow-up analysis. MAKE90+ = patients meeting the endpoint major adverse kidney events; MAKE90 = patients not meeting the endpoint major adverse kidney events.
Patient enrollment and allocation to the remote ischemic preconditioning (RIPC) and control (Sham-RIPC) arms. The first part (light blue box) shows the recruitment of the initial RenalRIP trial,9 while the second part (dark blue box) shows the analyzed cohort of this follow-up analysis. MAKE90+ = patients meeting the endpoint major adverse kidney events; MAKE90− = patients not meeting the endpoint major adverse kidney events.
Fig. 1.
Patient enrollment and allocation to the remote ischemic preconditioning (RIPC) and control (Sham-RIPC) arms. The first part (light blue box) shows the recruitment of the initial RenalRIP trial,9  while the second part (dark blue box) shows the analyzed cohort of this follow-up analysis. MAKE90+ = patients meeting the endpoint major adverse kidney events; MAKE90 = patients not meeting the endpoint major adverse kidney events.
×
RIPC significantly reduced the occurrence of MAKE at day 90 (17 of 120 [14.2%]) compared with Sham-RIPC (30 of 120 [25.0%]; ARR, 10.8%; 95% CI, 0.9 to 20.8%; P = 0.034; table 2). Considering the different components of the composite endpoint, persistent renal dysfunction (ARR, 12.7%; OR, 0.262; 95% CI, 0.101 to 0.681; P = 0.004; table 2) and renal replacement therapy (ARR, 7.2%; OR, 0.311; 95% CI, 0.097 to 0.997; P = 0.040; table 2; fig. 2A) were significantly higher in the Sham-RIPC group than in the RIPC group. Sensitivity analyses using the modified definition of persistent renal dysfunction (persistent elevation of serum creatinine greater than or equal to 50% of baseline) also demonstrated significant results (ARR, 9.8%; 95% CI, 1.40 to 18.21%; P = 0.024). Mortality was similar in both groups (RIPC 9.2% vs. Sham-RIPC 8.3%; P = 0.819; table 2; fig. 2B). We repeated the analysis of outcomes stratified by center and found no differences compared to the unstratified analyses (MAKE at day 90: OR, 0.502; 95% CI, 0.261 to 0964; PCMH = 0.035; persistent renal dysfunction at day 90: OR, 0.263; 95% CI, 0.101 to 0.682; PCMH = 0.003; renal replacement therapy at day 90: OR, 0.297; 95% CI, 0.092 to 0.961; PCMH = 0.034) demonstrating homogeneous treatment outcome.
Fig. 2.
Kaplan–Meier curves for dialysis (A) and death (B) within 90 days after study enrollment. Cumulative dialysis or death within 90 days for control (Sham-RIPC; blue line) or remote ischemic preconditioning (RIPC) patients (green line). (A) log-rank P = 0.040, (B) log-rank P = 0.849.
Kaplan–Meier curves for dialysis (A) and death (B) within 90 days after study enrollment. Cumulative dialysis or death within 90 days for control (Sham-RIPC; blue line) or remote ischemic preconditioning (RIPC) patients (green line). (A) log-rank P = 0.040, (B) log-rank P = 0.849.
Fig. 2.
Kaplan–Meier curves for dialysis (A) and death (B) within 90 days after study enrollment. Cumulative dialysis or death within 90 days for control (Sham-RIPC; blue line) or remote ischemic preconditioning (RIPC) patients (green line). (A) log-rank P = 0.040, (B) log-rank P = 0.849.
×
In the 108 of 240 (45%) patients who developed AKI after cardiac surgery (RIPC 45 [37.5%] vs. Sham-RIPC 63 [52.5%]), 2 of 38 subjects in the RIPC group (5.3%) and 13 of 56 subjects in the Sham-RIPC group (23.2%) failed to recover renal function by day 90 (ARR, 17.9%; 95% CI, 4.8 to 31.1%; P = 0.020; table 2). We observed a reduced dependence on renal replacement therapy in the RIPC group (one patient [2.6%]) compared to that in the Sham-RIPC group (eight patients [14.3%]) within 90 days after randomization (ARR, 11.7%; 95% CI, 1.2 to 22.1%; P = 0.079). Mortality was similar in both groups (RIPC: 15.6% vs. Sham-RIPC: 11.1%; P = 0.498).
Table 2.
Outcomes of Patients by the Treatment Group
Outcomes of Patients by the Treatment Group×
Outcomes of Patients by the Treatment Group
Table 2.
Outcomes of Patients by the Treatment Group
Outcomes of Patients by the Treatment Group×
×
The effect of RIPC versus Sham-RIPC was confirmed in a logistic regression analysis adjusted for age, gender, and chronic kidney disease (MAKE at day 90: OR, 0.505; 95% CI, 0.258 to 0.988; P = 0.046; persistent renal dysfunction at day 90: OR, 0.258; 95% CI, 0.098 to 0.678; P = 0.006; renal replacement therapy at day 90: OR, 0.306; 95% CI, 0.094 to 0.991; P = 0.048; and mortality at day 90: OR, 1.179; 95% CI, 0.472 to 2.948; P = 0.724; table 3).
Table 3.
Logistic Regression Analysis
Logistic Regression Analysis×
Logistic Regression Analysis
Table 3.
Logistic Regression Analysis
Logistic Regression Analysis×
×
As shown previously,9  baseline urinary [TIMP-2]·[IGFBP7] tested immediately before RIPC or Sham-RIPC did not differ between the 2 groups (P = 0.33). Urinary [TIMP-2]·[IGFBP7] tested 4 h after CPB was significantly higher in patients meeting the endpoint (MAKE-negative patients: 0.57 [ng/ml]2/1,000; MAKE-positive patients: 1.01 [ng/ml]2/1,000; P = 0.021). A ROC analysis for MAKE at day 90 demonstrated best performance for [TIMP-2]·[IGFBP7] at 4 h (AUC, 0.641; 95% CI, 0.546 to 0.736; P = 0.004) and 12 h (AUC, 0.623; 95% CI, 0.515 to 0.731; P = 0.026) after cardiac surgery (tables 4 and 5). The same was true for urinary NGAL levels (tables 4 and 5). The AUCs for the combination of [TIMP-2]·[IGFBP7] with NGAL at the different time points were not different (P > 0.05). The sensitivity, specificity, Youden index, and cutoff points for the 4- and 12-h time points after cardiac surgery were calculated, as shown in tables 4 and 5. Combining [TIMP-2]·[IGFBP7] with NGAL did not demonstrate superior predictive value for MAKE at day 90 compared to one marker alone (appendix A2).
Table 4.
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90×
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90
Table 4.
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90×
×
Table 5.
Evaluation of Cutoff Values for Urinary [TIMP-2]·[IGFBP7] and Urinary NGAL
Evaluation of Cutoff Values for Urinary [TIMP-2]·[IGFBP7] and Urinary NGAL×
Evaluation of Cutoff Values for Urinary [TIMP-2]·[IGFBP7] and Urinary NGAL
Table 5.
Evaluation of Cutoff Values for Urinary [TIMP-2]·[IGFBP7] and Urinary NGAL
Evaluation of Cutoff Values for Urinary [TIMP-2]·[IGFBP7] and Urinary NGAL×
×
Next, we assessed the relationship between urinary biomarker concentrations 4, 12, and 24 h after CPB and persistent renal dysfunction at day 90 (table 6). For [TIMP-2]·[IGFBP7], the AUC for the 4- and 12-h time points were 0.692 (95% CI, 0.563 to 0.821; P = 0.004) and 0.677 (95% CI, 0.523 to 0.831; P = 0.024), respectively (table 6). The AUC for the composite time point was 0.696 (95% CI, 0.555 to 0.836; P = 0.006). Similar results were obtained for NGAL (table 6). The ROC curves for the combination of [TIMP-2]·[IGFBP7] with NGAL at the different time points were not different (P > 0.05). Combining [TIMP-2]·[IGFBP7] with NGAL did not improve the precision for persistent renal dysfunction at day 90 compared to one marker alone (appendix A3).
Table 6.
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90×
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90
Table 6.
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90×
×
Discussion
The results of this follow-up of the randomized controlled clinical RenalRIP trial9  show that RIPC improves short- as well as long-term outcomes of high-risk patients undergoing cardiac surgery.
In recent years, several studies investigating the effects of RIPC have been published with variable results. In contrast to studies showing a positive effect of RIPC on the heart and kidney,9,19,22  several studies were unable to demonstrate that RIPC can affect organ function, complications, or mortality.7,8,23,24  Two recently published large multicenter trials investigating more than 3,000 patients demonstrated that RIPC did not affect either composite endpoints or mortality.7,8  These results might be explained by differences in study design. Applying RIPC in high-risk patients reduced AKI,9  whereas the use of this same intervention in low-risk patients had no effect on myocardial infarction, occurrence of AKI, or mortality.7,8  Another very important difference between the studies, which might explain the different results, is the anesthetic regime during surgery. It has been shown that propofol can affect the effects of RIPC.14  In the two recently published multicenter trials, the vast majority of patients received propofol for anesthesia, which could have diminished or abrogated the effect of RIPC. In our trial, we specifically avoided propofol and found that RIPC significantly reduced the occurrence of MAKE at day 90 compared to sham. Furthermore, in patients who nevertheless developed AKI after surgery, RIPC significantly improved renal recovery as seen by lower rates of renal replacement therapy and persistent renal dysfunction.
Surgical patients commonly experience postoperative increases in creatinine levels. A recently published trial suggests that even small increases (Δcreatinine 25 to 49% above baseline but less than 0.3 mg/dl) in postoperative creatinine levels are associated with adverse outcomes.25  This was more pronounced in noncardiac surgery patients. These results suggest that RIPC might be an effective therapeutic approach to prevent even mild forms of postoperative kidney dysfunction and to improve surgical outcomes. Moreover, one could raise the concern of ACEi and ARBs influencing the effects of RIPC. The same number of patients in the control and RIPC groups received ACEi or ARBs (60.8 vs. 59.2%, respectively; P > 0.05). ACEi and ARBs were discontinued before surgery, and according to the recommendations of the American College of Cardiology Foundation26  and the KDIGO guidelines,18  patients received ACEi or ARBs after they became hemodynamically stable. Due to the preoperative discontinuation of the ACEi and ARBs, it is unlikely that these drugs potentiated the effects of RIPC.
The mechanisms responsible for the benefit of RIPC are not completely understood. Recent evidence suggests that in situ preconditioning induces activation of cardiac hypoxia-inducible factor (HIF)-1α. In vivo small interfering RNA repression of cardiac HIF-1α resulted in abolished cardioprotection by ischemic preconditioning.27  Another study demonstrated that HIF-1 activates interleukin (IL)-10 gene transcription and is required for RIPC.28  Our recently published data suggest that RIPC induces the release of damage-associated molecular patterns from the ischemic tissue and that these molecules may engage self-protective mechanisms in the kidney such as cell cycle arrest.9 
Biomarkers may further aid in the interpretation of results from interventional trials. Coca et al.29  have recently shown that several AKI biomarkers measured in the perioperative period after cardiac surgery correlated with long-term mortality. The authors of the Translational Research Investigating Biomarker Endpoints in AKI study demonstrated that, compared with the first tertiles, the third tertiles of peak biomarker percentages of urinary kidney injury molecule-1, NGAL, liver fatty acid binding protein, IL-18, and albumin were all associated with a significantly increased risk of mortality in those subjects who developed AKI after surgery. This effect was also significant for IL-18 and kidney injury molecule-1 in patients without AKI. Both IGFBP7 and TIMP-2 are involved with the phenomenon of G1 cell cycle arrest during the very early phases of cell injury. Importantly, both TIMP-2 and IGFBP7 may increase in response to a wide variety of insults (inflammation, oxidative stress, ultraviolet radiation, drugs, and toxins).30–32  The results of several studies of urinary [TIMP-2]·[IGFBP7] demonstrated that these markers enable early diagnosis and risk stratification of AKI in a wide range of critically ill patients.10,12  In terms of timing, this signal represents an early point of cellular stress. Biomarkers that can detect cellular stress may be more useful than markers of injury or cell death. These elevations are independent of the presence of other chronic conditions such as chronic kidney disease.33  Moreover, there is growing evidence that damage markers may play an important role in the progression of AKI to maladaptive repair resulting in progression of fibrosis to chronic kidney disease.34,35  A recently published study showed that [TIMP-2]·[IGFBP7] measured in critically ill patients after intensive care unit admission similarly correlated with long-term outcomes.36  However, this subgroup analysis clearly demonstrated that the signal detected by [TIMP-2]·[IGFBP7] is highly specific to AKI.36  In line with these results, we here demonstrated that [TIMP-2]·[IGFBP7] in the urine immediately after surgery correlates with long-term outcomes but larger randomized controlled trials will have to confirm these findings. However, unlike previous studies, we found that the effects on MAKE at day 90 were not limited to patients manifesting AKI.
Our study has a number of strengths, including the prospective nature of the study together with the scale and completeness of long-term follow-up. Our design allows for greater precision in estimates of absolute risk and increases the clinical applicability of the findings while avoiding potential bias from retrospective selection of the study cohort. In addition, clinical and biochemical outcomes were available for all patients, and data linkage has allowed a complete follow-up of mortality, need for renal replacement therapy, and persistent renal dysfunction. However, a limitation of such a study design is that the findings from a randomized trial are not always generalizable to other patient populations, so caution should be applied in translating these findings from high-risk patients undergoing cardiac surgery to patients undergoing another procedure. Moreover, our measure of kidney dysfunction is limited to serum creatinine at day 90 after surgery. More precise and repeated measures of glomerular filtration rate and possibly biomarkers of kidney damage or assessment of renal reserve may have unmasked further evidence of kidney pathology.
In addition, future studies will need to address the optimal methods for RIPC and whether benefits are consistent across patients with varying risks for AKI defined by either clinical criteria (such as those with preexisting chronic kidney disease or with lower Cleveland Clinical Foundation score) or use of biomarkers. Although we demonstrated that RIPC significantly reduced MAKE, we did not detect a reduction in mortality between the two groups at days 30 and 90 after cardiac surgery. Given the low mortality rates, however, in order to detect a difference in long-term mortality between groups, we would need to analyze more than 10,000 patients. It remains to be determined whether preventing cardiac surgery–associated AKI using RIPC will improve long-term kidney function and outcome, but the effects shown here, especially on dialysis use at 90 days, are encouraging. In conclusion, RIPC significantly improved renal function in high-risk patients undergoing cardiac surgery by reducing prolonged renal dysfunction and need for renal replacement therapy at day 90. Furthermore, the intervention also reduced the 3-month incidence of the composite endpoint MAKE at day 90 without affecting the all-cause mortality. In addition, RIPC not only reduced the severity of AKI but also enhanced renal recovery in those patients who developed AKI.
Research Support
Supported by grant Nos. ZA428/6-1 and ZA428/10-1 from the German Research Foundation (Bonn, Germany; to Dr. Zarbock) and by grant No. 2016_A97 from the Else Kröner Fresenius Stiftung (Bad Homburg, Germany).
Competing Interests
Dr. Zarbock and Dr. Kellum have received grant support and lecture fees from Astute Medical (San Diego, California), unrelated to the current study. Dr. Zarbock and Dr. Kellum have filed a patent application on the use of the biomarkers together with remote ischemic preconditioning. Dr. Meersch has received lecture fees from Astute Medical (San Diego, California), unrelated to the current study. The other authors declare no competing interests.
Reproducible Science
Full protocol available at: zarbock@uni-muenster.de. Raw data available at: zarbock@uni-muenster.de.
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Appendix A1.
Demographic and Operative Data Further Stratified by Sham-RIPC and RIPC
Demographic and Operative Data Further Stratified by Sham-RIPC and RIPC×
Demographic and Operative Data Further Stratified by Sham-RIPC and RIPC
Appendix A1.
Demographic and Operative Data Further Stratified by Sham-RIPC and RIPC
Demographic and Operative Data Further Stratified by Sham-RIPC and RIPC×
×
Appendix A2.
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)×
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)
Appendix A2.
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)×
×
Appendix A3.
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)×
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)
Appendix A3.
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)×
×
Fig. 1.
Patient enrollment and allocation to the remote ischemic preconditioning (RIPC) and control (Sham-RIPC) arms. The first part (light blue box) shows the recruitment of the initial RenalRIP trial,9  while the second part (dark blue box) shows the analyzed cohort of this follow-up analysis. MAKE90+ = patients meeting the endpoint major adverse kidney events; MAKE90 = patients not meeting the endpoint major adverse kidney events.
Patient enrollment and allocation to the remote ischemic preconditioning (RIPC) and control (Sham-RIPC) arms. The first part (light blue box) shows the recruitment of the initial RenalRIP trial,9 while the second part (dark blue box) shows the analyzed cohort of this follow-up analysis. MAKE90+ = patients meeting the endpoint major adverse kidney events; MAKE90− = patients not meeting the endpoint major adverse kidney events.
Fig. 1.
Patient enrollment and allocation to the remote ischemic preconditioning (RIPC) and control (Sham-RIPC) arms. The first part (light blue box) shows the recruitment of the initial RenalRIP trial,9  while the second part (dark blue box) shows the analyzed cohort of this follow-up analysis. MAKE90+ = patients meeting the endpoint major adverse kidney events; MAKE90 = patients not meeting the endpoint major adverse kidney events.
×
Fig. 2.
Kaplan–Meier curves for dialysis (A) and death (B) within 90 days after study enrollment. Cumulative dialysis or death within 90 days for control (Sham-RIPC; blue line) or remote ischemic preconditioning (RIPC) patients (green line). (A) log-rank P = 0.040, (B) log-rank P = 0.849.
Kaplan–Meier curves for dialysis (A) and death (B) within 90 days after study enrollment. Cumulative dialysis or death within 90 days for control (Sham-RIPC; blue line) or remote ischemic preconditioning (RIPC) patients (green line). (A) log-rank P = 0.040, (B) log-rank P = 0.849.
Fig. 2.
Kaplan–Meier curves for dialysis (A) and death (B) within 90 days after study enrollment. Cumulative dialysis or death within 90 days for control (Sham-RIPC; blue line) or remote ischemic preconditioning (RIPC) patients (green line). (A) log-rank P = 0.040, (B) log-rank P = 0.849.
×
Table 1.
Characteristics of Patient Cohort
Characteristics of Patient Cohort×
Characteristics of Patient Cohort
Table 1.
Characteristics of Patient Cohort
Characteristics of Patient Cohort×
×
Table 2.
Outcomes of Patients by the Treatment Group
Outcomes of Patients by the Treatment Group×
Outcomes of Patients by the Treatment Group
Table 2.
Outcomes of Patients by the Treatment Group
Outcomes of Patients by the Treatment Group×
×
Table 3.
Logistic Regression Analysis
Logistic Regression Analysis×
Logistic Regression Analysis
Table 3.
Logistic Regression Analysis
Logistic Regression Analysis×
×
Table 4.
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90×
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90
Table 4.
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90×
×
Table 5.
Evaluation of Cutoff Values for Urinary [TIMP-2]·[IGFBP7] and Urinary NGAL
Evaluation of Cutoff Values for Urinary [TIMP-2]·[IGFBP7] and Urinary NGAL×
Evaluation of Cutoff Values for Urinary [TIMP-2]·[IGFBP7] and Urinary NGAL
Table 5.
Evaluation of Cutoff Values for Urinary [TIMP-2]·[IGFBP7] and Urinary NGAL
Evaluation of Cutoff Values for Urinary [TIMP-2]·[IGFBP7] and Urinary NGAL×
×
Table 6.
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90×
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90
Table 6.
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90×
×
Appendix A1.
Demographic and Operative Data Further Stratified by Sham-RIPC and RIPC
Demographic and Operative Data Further Stratified by Sham-RIPC and RIPC×
Demographic and Operative Data Further Stratified by Sham-RIPC and RIPC
Appendix A1.
Demographic and Operative Data Further Stratified by Sham-RIPC and RIPC
Demographic and Operative Data Further Stratified by Sham-RIPC and RIPC×
×
Appendix A2.
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)×
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)
Appendix A2.
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)
Receiver Operating Characteristic Analysis for Major Adverse Kidney Events at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)×
×
Appendix A3.
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)×
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)
Appendix A3.
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)
Receiver Operating Characteristic Analysis for Persistent Renal Dysfunction at Day 90 (Combination of [TIMP-2]·[IGFBP7] and NGAL)×
×