Acute Kidney Injury Classification for Critically Ill Cirrhotic Patients: A Comparison of the KDIGO, AKIN, and RIFLE Classifications

Critically ill cirrhotic patients have high mortality rates, particularly when they present with acute kidney injury (AKI) on admission. The Kidney Disease: Improving Global Outcomes (KDIGO) group aimed to standardize the definition of AKI and recently published a new AKI classification. However, the efficacy of the KDIGO classification for predicting outcomes of critically ill cirrhotic patients is unclear. We prospectively enrolled 242 cirrhotic patients from a 10-bed specialized hepatogastroenterology intensive care unit (ICU) in a 2000-bed tertiary-care referral hospital. Demographic parameters and clinical variables on day 1 of admission were prospectively recorded. The overall in-hospital mortality rate was 62.8%. Liver diseases were usually attributed to hepatitis B viral infection (26.9%). The major cause of ICU admission was upper gastrointestinal bleeding (38.0%). Our result showed that the KDIGO classification had better discriminatory power than RIFLE and AKIN criteria in predicting in-hospital mortality. Cumulative survival rates at the 6-month after hospital discharge differed significantly between patients with and without AKI on ICU admission day. In summary, we identified that the outcome prediction performance of KDIGO classification is superior to that of AKIN or RIFLE classification in critically ill cirrhotic patients.


SCr criteria UO Criteria
(A) RIFLE Definition SCr changes over 1-7 days, sustained for more than 24 hrs UO < 0.5 ml/kg/h x 6 hrs Risk Increase in SCr ≥ 1.5 x baseline or decrease in GFR ≥ 25% UO < 0.5 ml/kg/h x 6 hrs Injury Increase in SCr ≥ 2.0 x baseline or decrease in GFR ≥ 50% UO < 0.5 ml/kg/h x 12 hrs  also shown in Table 6. The AUROC analysis verified that the KDIGO classification had the best discriminatory power for predicting in-hospital mortality.

Short-term prognosis of AKI and non-AKI patients defined according to the AKIN, RIFLE, and KDIGO classifications.
The number of patients and the in-hospital mortality rate calculated according to the stratification data of the three AKI criteria are listed in Table 7. A progressive and significant increase in the mortality rate was observed to correlate with the increasing AKI stage defined according to the three AKI criteria. The increase of odds ratio between the increasing AKI stages is greatest in the KDIGO classification. Figure 1 shows that the 180-day cumulative survival rates differed significantly for patients with AKI and those without AKI defined by the three AKI criteria on the first day of ICU admission (p < 0.05).

Discussion
This study included 242 cirrhotic patients with critical illnesses. The overall in-hospital mortality rate was 62.8%, which is well in keeping with that obtained in previous studies 5,15,16 . This investigation showed that the severity of AKI on the ICU admission day was associated with a significantly graded risk of death in critically ill cirrhotic patients, irrespective of which classification was used ( Table 4). The analytical results also showed that the KDIGO classification was an excellent scoring system for predicting the outcome for critically ill cirrhotic patients ( Table 6). Several studies had compared the prediction accuracy of the KDIGO, AKIN, and RIFLE classifications. Most of the studies were performed retrospectively [10][11][12][13] . Luo X et al. had conducted a prospective investigation and showed that the KDIGO and AKIN classification had similar ability to predict mortality in critically ill patients, whereas the prediction accuracy of the RIFLE classification was inferior 14 . Our study focused on critically ill cirrhotic patients and a key aspect of this investigation is the definition of baseline SCr. For general population of patients without a previous SCr value before hospitalization, an alternative estimated baseline SCr value  back-calculated by the Modification of Diet in Renal Disease (MDRD) formula have been widely adopted 12,14 . However, it is well known that creatinine-based equations, such as MDRD and Cockcroft-Gault formulas, are inaccurate in the estimation of glomerular filtration rate (GFR) in cirrhotic patients 17,18 . In this study, we used the last SCr value within the previous 3 months before hospitalization as the baseline SCr. For patients without an available SCr value before hospitalization, we followed the recommendations of the International Club of Ascites and used the first SCr value measured during hospitalization as the baseline SCr 18,19 . For taking account the specific feature of patients with cirrhosis, our study design might more precisely compare the prediction performance of the 3 AKI classifications in these patients. The KDIGO classification reconciles the AKI definition of the RIFLE and AKIN classifications. In this investigation, the AKI incidence determined by using the KDIGO classification was higher than that obtained according to the RIFLE or AKIN classification. Compared with AKIN and RIFLE, the KDIGO classification identified 5% (11 of 242) and 2% (5 of 242) more patients fulfilling the AKI criteria (Table 4). Among the patients with AKI diagnosed according to KDIGO but missed with the AKIN or RIFLE classification, 82% (9 of 11) and 60% (3 of 5)    of them died and accounted for 6% (9 of 152) and 2% (3 of 152) of the overall mortality, respectively. This AKI subgroup was also correlated to significant lower hospital and 180-day cumulative survival rates ( Fig. 1). Tsien et al. observed that regardless of AKI episodes reversed or not, cirrhotic patients with AKI were more vulnerable to further renal dysfunction and to poor survival compared with those without AKI 20 . The greater sensitivity of the KDIGO classification might allow AKI episodes to be recognized earlier and make potential interventions possible.
In patients with chronic liver disease, the absolute level and relative change of SCr concentration are significantly lower than that in the general population 18,[21][22][23][24] . Many studies have reported that even a minor fluctuation in SCr level appears to be strongly associated with adverse outcomes 13,25,26 . The percentage increase in SCr is even more obscure in patients with previous renal dysfunction 27 , which is particularly relevant among cirrhotic patients because of a high proportion of patients with preexisting impaired renal function on hospital admission 19,28 . The lack of capturing of small changes of SCr in RIFLE and considering the preadmission renal function in AKIN may explain their discriminative inferiority to the KDIGO classification (Table 6). Moreover, our data showed a progressive stepwise and significantly elevated in-hospital and 180-day mortality associated with increasing KDIGO stages (Table 7 and Fig. 1). On the basis of the study results, we strongly believe that the KDIGO classification is of great importance for standardizing the definition of AKI as well as facilitating advances in clinical practice and research. The comparisons between patients with RIFLE-R and those with RIFLE-I to F, and between patients with RIFLE-I and those with RIFLE-F have been depicted on the figure. (d) Patients with AKI diagnosed according to KDIGO but missed by AKIN or RIFLE classification had significantly lower hospital survival rate than patients without AKI (Log Rank P < 0.001). (e) Patients with AKI diagnosed according to KDIGO but missed by AKIN or RIFLE classification had significantly lower 180-day cumulative survival rate than patients without AKI (Log Rank P = 0.001). * Abbreviation: AKI, acute kidney injury; RIFLE, risk of renal failure, injury to kidney, failure of kidney function, loss of kidney function, and end-stage renal failure; AKIN, acute kidney injury network; KDIGO, kidney disease improving global outcomes.
Scientific RepoRts | 6:23022 | DOI: 10.1038/srep23022 Despite the encouraging results obtained in our study, several potential study limitations should also be considered. First, this study was conducted on patients from only one academic tertiary-care medical center, which limits the generalization of our findings. Our results may be unsuitable for direct extrapolation to other hospitals with different patient populations. Second, in our study, given that hepatitis B viral infection (27%) was the leading cause of liver cirrhosis, the use of our classification system may not be appropriate for patients in North America and in Europe where liver diseases are mostly attributed to hepatitis C viral infection and alcoholism. Third, sequential measurement of these scoring systems (e.g., daily or weekly) may reflect the dynamic aspects of clinical diseases, thus providing superior information on mortality risk. Fourth, the prognostic instruments were tested on patients already admitted to the specialized hepatogastroenterology ICU, rather than being used as a preadmission screening test, which may have skewed the measured results. Finally, the predictive accuracy of logistic regression models has its own limitations.

Conclusion
This study showed the grave prognosis in critically ill cirrhotic patient with AKI. The analytical data demonstrated that the KDIGO classification is a better tool with superior prediction performance for short-term prognosis than the AKIN or RIFLE classification. We confirmed that the KDIGO classification is a great scoring system for risk stratification, and is capable of providing a more sensitive and standardized method for early AKI detection in critically ill cirrhotic patients.

Materials and Methods
Ethics statement. This clinical study was conducted in full compliance with the ethical principles of the Declaration of Helsinki and was consistent with Good Clinical Practice guidelines and the applicable local regulatory requirements. The local institutional review board of Chang Gung Memorial Hospital approved our study protocol (approval no. 98-3658A3). Patients who met the inclusion criteria were invited to participate in this study on their first day of admission to the intensive care unit (ICU). Trained physicians evaluated the patients' mental status during the screening and proceeded to perform informed consent procedures. Written informed consent was obtained from all mentally competent patients or from the next-of-kin of compromised patients before their participation.

Patient information and data collection.
This study was conducted from September 2012 to August 2014, in a 10-bed specialized ICU (hepatogastroenterology ICU) at a 2000-bed tertiary-care referral hospital in Taiwan. In this study, we included 242 consecutive patients with hepatic cirrhosis requiring intensive monitoring and/or treatment unavailable elsewhere. The following patients were excluded: pediatric patients (age 18 years or below), patients or their next of kin who declined to be enrolled in the study, patients who stayed in the hospital for < 24 h, patients who had a previous end-stage renal disease and were undergoing regular RRT, and patients who had undergone liver transplantation. For patients who were readmitted, we only recorded the clinical condition at the first admission to avoid double weighing the same patient.
Prospective data were collected, including demographic data, reason for admission to the ICU, immediate diagnosis, severity of the illness, serum and urine biochemical analysis, urine microscopy, urine output, duration of ICU and hospital stay, and treatment outcome. The Child-Pugh points, model for end-stage liver disease (MELD), Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation (APACHE) II and III scores determined on the first day of ICU admission, and related clinical data were also recorded. The primary study outcome was in-hospital mortality rate. Follow-up examinations were performed at 6 months after hospital discharge of the patients, by analyzing the chart records.
Definitions. Cirrhosis was diagnosed on the basis of past medical history, the results of liver histology, or a combination of physical signs and symptoms and findings from biochemical analysis and ultrasonography. The severity of the liver disease on admission to the ICU was determined by using the Child-Pugh points and the MELD scoring system. The severity of the illness can also be assessed by using the APACHE II, APACHE III, and SOFA scores. The worst physiological and biochemical values determined on the first day of ICU admission were recorded.
The occurrence of AKI was determined by using the RIFLE, AKIN, and KDIGO classifications. We used the last SCr value within the previous 3 months before hospitalization as the baseline SCr in the RIFLE and KDIGO criteria. In patients without a previous SCr value, we used the first SCr value measured during hospitalization as the baseline SCr. For the AKIN criteria, the lowest SCr within 48 h before ICU admission was used as the baseline SCr. Both of the SCr and urine output criteria were considered in the three AKI classifications, and the criteria resulting in the worst possible classification were used. We applied a simple model for mortality, as follows: non-AKI (0 points); RIFLE-R, KDIGO stage 1, and AKIN stage 1 (1 point); RIFLE-I, KDIGO stage 2, and AKIN stage 2 (2 points); RIFLE-F, KDIGO stage 3, and AKIN stage 3 (3 points) on day 1 of ICU admission 29,30 .
Respiratory failure was defined as a respiratory rate of ≤ 5/min or ≥ 50/min, and/or the requirement for mechanical ventilation for ≥ 3 days, and/or fraction of inspired oxygen (FiO 2 ) of > 0.4, and/or a positive end-expiratory pressure of > 5 cm H 2 O 31-33 . Sepsis was defined as systemic inflammatory response syndrome (SIRS) plus suspected or proven infection. According to the guidelines of the American College of Chest Physician/Society of Critical Care Medicine Consensus Conference, SIRS was defined as patients with more than one of the following clinical findings: body temperature, > 38 °C or < 36 °C; heart rate, > 90 beats/min; hyperventilation evidenced by a respiratory rate of > 20 cycles/min or a PaCO 2 of < 32 mm Hg; and a white blood cell count of > 12,000 or < 4000 cells/μL 34 .
Scientific RepoRts | 6:23022 | DOI: 10.1038/srep23022 Statistical analysis. Continuous variables were summarized with means and standard derivations unless otherwise stated. Primary analysis compared hospital survivors with nonsurvivors. All variables were tested for normal distribution with the Kolmogorov-Smirnov test. Student's t-test was applied to compare the means of continuous variables and normally distributed data; otherwise, the Mann-Whitney U-test was employed. This study used the χ 2 test for trend to assess the categorical data associated with the RIFLE, AKIN, and KDIGO classifications. Correlations of paired-group variables were assessed by using linear regression and Pearson analysis.
Calibration was assessed by using the Hosmer-Lemeshow goodness-of-fit test (C-statistic) to compare the number of observed and predicted deaths in risk groups for the entire range of death probabilities. Discrimination was examined by using the area under the receiver operating characteristic curve (AUROC). To compare the areas under the two resulting AUROC curves, we used a nonparametric approach. Cumulative survival curves as a function of time were plotted by using the Kaplan-Meier approach and were compared by using the log rank test. All statistical tests were two-tailed, and a value of p < 0.05 was considered statistically significant. Data were analyzed with the Statistical Package for the Social Sciences software, version 19.0 for Windows (SPSS Inc., Chicago, IL, USA).