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NDT Advance Access originally published online on June 24, 2006
Nephrology Dialysis Transplantation 2006 21(10):2867-2873; doi:10.1093/ndt/gfl326
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© The Author [2006]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

RIFLE classification is predictive of short-term prognosis in critically ill patients with acute renal failure supported by extracorporeal membrane oxygenation

Chan-Yu Lin1, Yung-Chang Chen1, Feng-Chun Tsai2, Ya-Chung Tian1, Chang-Chyi Jenq1, Ji-Tseng Fang1, and Chin-Wei Yang1

1Department of Nephrology and 2Division of Cardiovascular Surgery, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taipei, Taiwan

Correspondence and offprint requests to: Ji-Tseng Fang, MD, Division of Critical Care Nephrology, Chang Gung Memorial Hospital, 199 Tung Hwa North Road, 105, Taipei, Taiwan. Email: fangjits{at}adm.cgmh.org.tw



   Abstract
Top
 Abstract
Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Background. Extracorporeal membrane oxygenation (ECMO) has been utilized for critically ill patients, such as those with post-cardiotomy cardiogenic shock or life-threatening respiratory failure. Acute renal failure following ECMO support has an extremely elevated mortality rate. This study examined the outcomes of patients treated with ECMO and characterized the association between mortality and RIFLE (risk of renal failure, injury to the kidney, failure of kidney function, loss of kidney function and end-stage renal failure) classification.

Methods. This retrospective study analysed the medical records of 46 critically ill patients—most had post-cardiotomy cardiogenic shock—treated by ECMO. Sixteen patients (34.8%) were treated with both ECMO and continuous renal replacement therapies.

Results. The overall mortality rate was 65.2% (30/46). A progressive and significant increase ({chi}2 for trend, P < 0.001) was observed for mortality based on RIFLE classification severity. The Hosmer and Lemeshow goodness-of-fit test demonstrated that the RIFLE category has a good fit. By applying the area under the receiver operating characteristic curve (AUROC), the RIFLE classification tool had good discriminative power (AUROC 0.868 ± 0.068, P < 0.001). Cumulative survival rates at 6 months follow-up following hospital discharge differed significantly (P < 0.05) for non-ARF vs RIFLE-I and RIFLE-F, and RIFLE-R vs RIFLE-F.

Conclusion. This investigation confirms that the prognosis for critically ill patients supported by ECMO is grave. The RIFLE category is a simple, reproducible and easily applied evaluation tool with good prognostic capability that might generate objective information for patient families and physicians and supplements the clinical judgment of prognosis.

Keywords: acute renal failure; continuous renal replacement therapy (CRRT); extracorporeal membrane oxygenation (ECMO); post-cardiotomy cardiogenic shock; prognosis



   Introduction
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 Abstract
 Introduction
Subjects and methods
 Results
 Discussion
 References
 
Critically ill patients frequently require mechanical ventilation and circulatory support among other assistive devices. Extracorporeal membrane oxygenation (ECMO) has been recommended for patients with acute, potentially reversible, life-threatening respiratory failure unresponsive to conventional therapy. ECMO is effective for treating patients with severe, reversible myocardial dysfunction (e.g. myocarditis, cardiomyopathy or post-operative cardiogenic shock) or as a bridge to another treatment modality [1–4].

Reports suggested that acute renal failure (ARF) is common in critical patients placed on ECMO [1,2,4]. Acute renal failure during ECMO support is associated with increased mortality rates in excess of 60% [5–11]. However, no consensus exists for the definition of ARF in critically ill patients treated by ECMO. The wide range of definitions for ARF generate clinical confusion and make study comparisons difficult [12–14]. The RIFLE (risk of renal failure, injury to the kidney, failure of kidney function, loss of kidney function and end-stage renal failure) classification was first proposed by the Acute Dialysis Quality Initiative (ADQI) group in an attempt to standardize ARF study [15].

In view of promising new treatments and limited medical resources, investigators and physicians require a reliable and effective tool to stratify risk and monitor patients during practice and clinical trials. This study retrospectively utilized RIFLE criteria for critically ill patients treated with ECMO to determine hospital mortality and short-term prognosis for a homogeneous patient subset.



   Subjects and methods
Top
 Abstract
 Introduction
 Subjects and methods
Results
 Discussion
 References
 
Patient information and data collection
The medical records of 46 patients on ECMO support in the cardiovascular surgery department in a specialized intensive care unit (CVSICU) between April 2002 and September 2004 were examined. Indications for ECMO were as follows: (i) failure to wean off cardiopulmonary bypass after cardiac or lung surgery; (ii) cardiac arrest or cardiogenic shock with persistent hypotension despite the administration of maximal doses of inotropes and (iii) life-threatening respiratory failure unresponsive to conventional therapy. Retrospective analysis also assessed available clinical and laboratory data. Case records were reviewed to determine patient gender, age, weight, diagnosis, duration of ECMO support, laboratory results and outcome.

The primary study outcome was hospital mortality. Follow-up at 6 months after hospital discharge was via a telephone interview. When necessary, the hospital registry office provided information regarding patient survival or date of death.

Definitions
The ADQI group first proposed the RIFLE system (Table 1) at the second ADQI conference held in Vicenza, Italy, in May 2002. This classification system comprises individual criteria for serum creatinine (SCr) levels and urine output (UO). Patients fulfill the criteria via changes in SCr concentrations or changes to UO, or both [15]. The criteria indicative of the worst outcome should be adopted. Patients are classified into three severity categories (risk, injury and failure) and two clinical outcome categories (loss and end-stage renal disease). Note that the component F in the RIFLE acronym is present even when increased SCr level is <3-fold as long as the subsequent SCr level is >4.0 mg/dl (350 µmol/l) in the setting of an acute increase of at minimum 0.5 mg/dl (44 µmol/l) [15]. Baseline SCr concentration was the first value obtained during hospitalization. The modification of diet in renal disease formula was applied for two patients to estimate the baseline SCr concentrations as their initial SCr levels on admission were unknown [15]. No patient met the criteria for loss or end-stage renal disease. A simple model for mortality—based on the non-ARF (0 point), RIFLE-R (1 point), RIFLE-I (2 points) and RIFLE-F (3 points) criteria for day 1 on ECMO—was constructed (Table 1). Patients were treated with continuous renal replacement therapy (CRRT), which was incorporated into the ECMO circuit, when their fluid overloads were inadequately controlled with diuretic therapy, had severe metabolic acidosis, a need for hyperalimentation with insufficient urinary output or a sign or symptom, such as encephalopathy, for which uraemia could not be ruled out as a precipitating cause.


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  		Table 1. RIFLE classification for acute renal failure

 
Statistical analysis
Descriptive statistics are expressed as means ± SD. Primary analysis compared hospital survivors with non-survivors. All variables were tested for normal distribution using the Kolmogorov–Smirnov test. The Student's t-test was applied to compare means of continuous variables and normal distribution data. Otherwise, the Mann–Whitney U-test was employed. Categorical data were tested using the chi-square test. Analysis of variance (ANOVA) test with the Tukey test post hoc for numerical values and the chi-square test for trends were applied to assess categorical data associated with RIFLE classifications.

This study adopted two methods for test calibration (i.e. the degree of correspondence between predicted and observed mortality over the entire range of risk). First, calibration was graphically displayed by plotting observed and predicted mortality for all patients across all risk ranges. Second, goodness-of-fit testing was applied to determine the accurate calibration using the Hosmer–Lemeshow test.

Discriminative power (i.e. the model's ability to differentiate between patients who died and those who lived) was assessed using the area under a receiver operating characteristic (AUROC) curve [16]. When the model performance approximates that of flipping a coin, the AUROC is close to 0.5. However, as the area nears 1.0, the model nears 100% sensitivity and specificity regardless of any cutoff point. Sensitivity and specificity for the RIFLE classification and serum lactate levels were determined. Finally, cutoff points were calculated by obtaining the best Youden index (sensitivity + specificity –1) [17].

Cumulative survival curves as a function of time were produced with the Kaplan–Meier approach and compared using the log-rank test. All statistical tests were two-tailed; P ≤ 0.05 was considered statistically significant. Data were analysed using the SPSS version 12.0 for Windows 95 (SPSS, Inc., Chicago, IL, USA).



   Results
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 Abstract
 Introduction
 Subjects and methods
 Results
Discussion
 References
 
Subject characteristics
Between April 2002 and September 2004, 46 patients on ECMO support at the CVSICU were enrolled in this study. Patient median age was 52 years; 29 were male (63%) and 17 were female (37%). Overall, in-hospital mortality for the entire group was 65.2% (30/46). Table 2 shows patient demographic data and clinical characteristics of both the survivors and non-survivors. Table 3 lists the number of patients, age, gender and clinical parameters for the different study groups based on RIFLE classifications. Table 4 describes the reasons for ECMO support. Post-cardiotomy cardiogenic shock was the most frequent reason for ECMO support.


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  		Table 2. Patient demographic data and clinical characteristics

 

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  		Table 3. Clinical parameters of different study groups of RIFLE classification

 

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  		Table 4. Reasons for extracorporeal membrane oxygenation support

 
Hospital mortality and severity of illness scoring systems
The data required for calculating RIFLE components on day 1 of ECMO support were available for all the 46 patients. Medium and mode RIFLE classification for all patients were RIFLE-I. Hospital mortality was 20% (2/10) for non-ARF patients, 57.1% (4/7) for RIFLE-R patients, 72.2% (13/18) for RIFLE-I patients and 100% (11/11) for RIFLE-F patients ({chi}2 for trend, P < 0.001) (Table 3). A progressive and significant increase in mortality was associated with increasing RIFLE classification among all patients. Odds ratio of RIFLE criteria were 5.33 (P = 0.128) for RIFLE-R vs non-ARF, 10.40 (P = 0.014) for RIFLE-I vs non-ARF and infinity (P < 0.001) for RIFLE-F vs non-ARF (Table 1).

Figure 1 presents the calibration curve for RIFLE classification. This curve indicates that, generally, the proportion of patients who died increased with increased risk of hospital mortality predicted utilizing the prognostic approaches. Calibration for RIFLE classification [Hosmer–Lemeshow chi-square = 1.463, 2 degree of freedom (df), P = 0.481] and the RIFLE classification calibration curve were close to the line of perfect predictive ability. The Acute Physiology and Chronic Health Evalution (APACHE) II (Hosmer–Lemeshow chi-square = 4.857, 7 df, P = 0.677) and the blood lactate level (Hosmer–Lemeshow chi-square = 6.262, 7 df, P = 0.509) also had good calibration, estimated by the Hosmer–Lemeshow goodness-of-fit test.


Figure 1
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  		Fig. 1. Calibration curves for RIFLE classification. The diagonal line is the line of ideal prediction (predicted mortality = observed mortality) for RIFLE classification (open circle). Calibration curves below the diagonal line indicate that actual mortality was greater than that predicted (i.e. underestimated by the predictive model).

 
The ROC curve model represents the true-positive and false-positive rates for vertical and horizontal axes, respectively (Figure 2). Computation for the AUROC confirmed the good discriminatory power of the RIFLE classification system [AUROC = 0.868 ± 0.068 (95% CI: 0.734–1.000), P < 0.05] and APACHE II [AUROC = 0.850 ± 0.074 (95% CI: 0.705–0.996), P < 0.05] compared with blood lactate level [AUROC = 0.676 ± 0.102 (95% CI: 0.477–0.876), P > 0.05].


Figure 2
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  		Fig. 2. ROC curves for RIFLE classification (AUROC curve is 0.868, P = 0.001), APACHE II (AUROC curve is 0.850, P = 0.002) and blood lactate level (AUROC curve is 0.676, P = 0.111).

 
To assess the extent to which the applied scoring systems were valid for sensitivity and specificity, to assess overall correctness of prediction and to determine their ability to predict hospital mortality, positive and negative predictive values were determined for the applied scoring systems. Table 5 presents these data calculated using the cutoff point that provides the best Youden index. The RIFLE-R classification—not serum lactate concentration—had the best Youden index and highest overall correctness of prediction. Hospital mortality rates below and above the cutoff for RIFLE-R were 35.3 and 82.8%, respectively; the cutoff values for 136 blood lactate levels were 40 and 100%. Cumulative survival rates differed significantly (P < 0.05) for non-ARF vs RIFLE-I and RIFLE-F, and RIFLE-R vs RIFLE-F (Figure 3).


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  		Table 5. Prediction of subsequent hospital mortality on the day 1 of ECMO support

 

Figure 3
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  		Fig. 3. Cumulative survival rate for 46 critically ill patients based on their RIFLE classification after day 1 of ECMO support.

 


   Discussion
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
References
 
Several studies have identified a mortality rate of 50–76% for patients on ECMO support [1,2,18]. The hospital mortality rate for patients in this study was 65.2% (30/46). Analytical results verify that prognosis is grave for this patient subgroup on ECMO support.

Acute renal failure, a manifestation of multiple organ system failure, is associated with underlying decompensated heart failure and sepsis and is aggravated by complications, such as surgical site bleeding, during ECMO support. Most cases of ARF developed acute pulmonary oedema—due to massive blood transfusion, advanced heart failure and retrograde non-pulsatile flow during ECMO support—and increased left ventricular afterload [19]. The definition of ARF remains controversial. The RIFLE criteria applied in this study utilized worst values on day 1 of ECMO support. A trend was observed towards a significant increase in mortality rates associated with increasing RIFLE scores for all patients (Table 3). Mortality rates increased substantially when RIFLE-I or RIFLE-F scores were included in the calculation (Tables 3 and 5). Such analytical results suggest that RIFLE classification is a good tool for measuring disease severity in an ECMO patient group; furthermore, when RIFLE classification is obtained early, prognosis can be made promptly in a clinical course. This classification scheme is also simple, easily performed, inexpensive and reproducible, such that a score can be obtained anywhere at any point of time (Figures 1 and 2). The RIFLE classification system assists in predicting prognosis for ARF patients, and may assist the decision-making process [20,21].

Blood lactate levels have been considered an effective single prognostic indicator for critically ill patients [22]. As lactate is easily measured and simpler to use than scoring systems, lactate levels were used as a reference in this study. The single value achieved an AUROC of 0.676, less than that achieved with RIFLE classification (Figure 2). However, a blood lactate concentration >136 mmol/l was invariably associated with death in this study (Table 5). A high lactate level is an early sign of tissue hypoxia. In such severely ill patients, tissue hypoxia is the principal cause of circulatory dysfunction, acute kidney injury and death [23,24].

Acute renal failure following cardiac surgery is a well-recognized complication that occurs in 1–10% of the patients. Patients who develop ARF have high rates of mortality and resource utilization, with the worst values seen for dialysis patients. Emerging evidence suggests that even small increases in creatinine levels following cardiac surgery are associated with significant increases in mortality. Whether ARF directly produces adverse outcomes remains unclear; however, an increase in infection and new-onset sepsis, congestive heart failure and fluid overload may contribute to ARF [25]. In this study, 16 patients treated by CRRT eventually died (Table 2). A combination of CRRT and ECMO should be applied as an alternative therapy bridging temporary replacement treatment and heart transplantation in advanced cardiac patients [19].

Despite the promising results obtained in this study, several important limitations should be recognized. First, this retrospective study was conducted at a single tertiary care medical centre, thus this study's findings are limited. Second, the RIFLE classification scheme does not consider important risk factors supported by ECMO, which may have been already present including advanced age, surgery type or a history of particular chronic diseases [26,27]. Finally, the number of patients and outcome events was insufficient to determine the independent risk factors for ARF and post-operative mortality using multivariate techniques.

In conclusion, 65.2% of critically ill patients on ECMO died in the hospital. Analytical results demonstrated that the risk of mortality is positively correlated with high RIFLE scores obtained on day 1 of ECMO support. Analytical data also confirmed the good discriminative power of the RIFLE classification system for predicting hospital mortality of critically ill patients treated with ECMO. Due to its practicality and low cost, we recommend that physicians use the RIFLE classification scheme when assessing short-term prognosis in this homogeneous subset of patients.

Conflict of interest statement. None declared.



   References
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 

  1. Doll N, Kiaii B, Borger M, et al. (2004) Five-year results of 219 consecutive patients treated with extracorporeal membrane oxygenation for refractory postoperative cardiogenic shock. Ann Thorac Surg 77:151–157.[Abstract/Free Full Text]
  2. Smedira NG, Moazami N, Golding CM, et al. (2001) Clinical experience with 202 adults receiving extracorporeal membrane oxygenation for cardiac failure: survival at five years. J Thorac Cardiovasc Surg 122:92–102.[Abstract/Free Full Text]
  3. Oto T, Rosenfeldt F, Rowland M, et al. (2004) Extracorporeal membrane oxygenation after lung transplantation: evolving technique improves outcomes. Ann Thorac Surg 78:1230–1235.[Abstract/Free Full Text]
  4. Kolla S, Lee WA, Hirschl RB, Bartlett RH. (1996) Extracorporeal life support for cardiovascular support in adults. ASAIO J 42:M809–M819.[Web of Science][Medline]
  5. Gajarski RJ, Mosca RS, Ohye RG, et al. (2003) Use of extracorporeal life support as a bridge to pediatric cardiac transplantation. J Heart Lung Transpl 22:28–34.[CrossRef][Web of Science][Medline]
  6. Mehta U, Laks H, Sadeghi A, et al. (2000) Extracorporeal membrane oxygenation for cardiac support in pediatric patients. Am Surg 66:879–886.[Web of Science][Medline]
  7. Ko WJ, Lin CY, Chen RJ, Wang SS, Lin FY, Chen YS. (2002) Extracorporeal membrane oxygenation support for adult postcardiotomy cardiogenic shock. Ann Thorac Surg 73:538–545.[Abstract/Free Full Text]
  8. Kolla S, Awad SS, Rich PB, Schreiner RJ, Hirschl RB, Bartlett RH. (1997) Extracorporeal life support for 100 adult patients with severe respiratory failure. Ann Surg 226:544–566.[CrossRef][Web of Science][Medline]
  9. Magovern GJ Jr and Simpson KA. (1999) Extracorporeal membrane oxygenation for adult cardiac support: the Allegheny experience. Ann Thorac Surg 68:655–661.[Abstract/Free Full Text]
  10. Mols G, Loop T, Geiger K, Farthmann E, Benzing A. (2000) Extracorporeal membrane oxygenation: a ten-year experience. Am J Surg 180:144–154.[CrossRef][Web of Science][Medline]
  11. Smedira NG and Blackstone EH. (2001) Postcardiotomy mechanical support: risk factors and outcomes. Ann Thorac Surg 71:Suppl 3, S60–S66.[Abstract/Free Full Text]
  12. Mehta RL and Chertow GM. (2003) Acute renal failure definitions and classification: time for change? J Am Soc Nephrol 14:2178–2187.[Free Full Text]
  13. Schrier RW, Wang W, Poole B, Mitra A. (2004) Acute renal failure: definitions, diagnosis, pathogenesis, and therapy. J Clin Invest 114:5–14.[CrossRef][Web of Science][Medline]
  14. Lameire N, Van Biesen W, Vanholder R. (2005) Acute renal failure. Lancet 365:417–430.[Web of Science][Medline]
  15. Bellomo R, Ronco C, Kellum JA, Mehta RL, Palevsky P. Acute Dialysis Quality Initiative workgroup. (2004) Acute renal failure – definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit Care 8:R204–R212.[CrossRef][Web of Science][Medline]
  16. Hanley J and McNeil B. (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36.[Abstract/Free Full Text]
  17. Youden WJ. (1950) Index for rating diagnosis tests. Cancer 3:32–35.[CrossRef][Web of Science][Medline]
  18. Doll N, Fabricius A, Borger MA, et al. (2003) Temporary extracorporeal membrane oxygenation in patients with refractory postoperative cardiogenic shock – a single center experience. J Card Surg 18:512–518.[CrossRef][Web of Science][Medline]
  19. Yap HJ, Chen YC, Fang JT, Huang CC. (2003) Combination of continuous renal replacement therapies (CRRT) and extracorporeal membrane oxygenation (ECMO) for advanced cardiac patients. Ren Fail 25:183–193.[CrossRef][Web of Science][Medline]
  20. Bell M, Liljestam E, Granath F, Fryckstedt J, Ekbom A, Martling CR. (2005) Optimal follow-up time after continuous renal replacement therapy in actual renal failure patients stratified with the RIFLE criteria. Nephrol Dial Transplant 20:354–360.[Abstract/Free Full Text]
  21. Abosaif NY, Tolba YA, Heap M, Russell J, El Nahas AM. (2005) The outcome of acute renal failure in the intensive care unit according to RIFLE: model application, sensitivity, and predictability. Am J Kidney Dis 46:1038–1048.[CrossRef][Web of Science][Medline]
  22. Marecaux G, Pinsky MR, Dupont E, Kahn RJ, Vincent JL. (1996) Blood lactate levels are better prognostic indicators than TNF and IL-6 levels in patients with septic shock. Intensive Care Med 22:404–408.[CrossRef][Web of Science][Medline]
  23. Schrier RW and Abraham WT. (1996) Hormones and hemodynamics in heart failure. N Engl J Med 341:577–585.
  24. Cadnapaphornchai MA, Gurevich AK, Weinberger HD, Schrier RW. (2001) Pathophysiology of sodium and water retention in heart failure. Cardiology 96:122–131.[CrossRef][Web of Science][Medline]
  25. Mehta RL. (2005) Acute renal failure and cardiac surgery: marching in place or moving ahead? J Am Soc Nephrol 16:12–14.[Free Full Text]
  26. Eriksen Bjørn O, Kristel RS Hoff, Solberg S. (2003) Prediction of acute renal failure after cardiac surgery: retrospective cross-validation of a clinical algorithm. Nephrol Dial Transplant 18:77–81.[Abstract/Free Full Text]
  27. Van Den Noortgate N, Mouton V, Lamot C, et al. (2003) Outcome in a post-cardiac surgery population with acute renal failure requiring dialysis: does age make a difference? Nephrol Dial Transplant 18:732–737.[Abstract/Free Full Text]
Received for publication: 12. 2.06
Accepted in revised form: 8. 3.06


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