Introduction: Severe acute kidney injury (AKI) requiring renal replacement therapy (RRT) has been associated with an unacceptably high mortality of 50% or more. Successful discontinuation of RRT is thought to be linked to better outcomes. Although functional and structural renal markers have been evaluated in AKI, little is known about their roles in predicting outcomes at the time of RRT discontinuation. Methods: In this prospective single-center cohort study, we analyzed patients who received continuous RRT (CRRT) for AKI between August 2016 and March 2018 in the intensive care unit of the University of Tokyo Hospital (Tokyo, Japan). Clinical parameters and urine samples were obtained at CRRT discontinuation. Successful CRRT discontinuation was defined as neither resuming CRRT for 48 h nor receiving intermittent hemodialysis for 7 days from the CRRT termination. Major adverse kidney events (MAKEs) were defined as death, requirement for dialysis, or a decrease in the estimated glomerular filtration rate (eGFR) of more than 25% from the baseline at day 90. Results: Of 73 patients, who received CRRT for AKI, 59 successfully discontinued CRRT and 14 could not. Kinetic eGFR, urine volume, urinary neutrophil gelatinase-associated lipocalin (NGAL), and urinary L-type fatty acid binding protein were predictive for CRRT discontinuation. Of these factors, urine volume had the highest area under the curve (AUC) 0.91 with 95% confidence interval [0.80–0.96] for successful CRRT discontinuation. For predicting MAKEs at day 90, the urinary NGAL showed the highest AUC 0.76 [0.62–0.86], whereas kinetic eGFR and urine volume failed to show statistical significance (AUC 0.49 [0.35–0.63] and AUC 0.59 [0.44–0.73], respectively). Conclusions: Our prospective study confirmed that urine volume, a functional renal marker, predicted successful discontinuation of RRT and that urinary NGAL, a structural renal marker, predicted long-term renal outcomes. These observations suggest that the functional and structural renal makers play different roles in predicting the outcomes of severe AKI requiring RRT.

Patients with severe AKI require RRT. When we should stop RRT and what parameter predicts worse clinical outcome in long-term have still not been clear. In this prospective study, we confirmed that urine volume was the best predictor to determine when to stop RRT. Further, we found that urinary marker NGAL was predictive for worse clinical outcomes within 3 months after RRT.

Acute kidney injury (AKI) is common among patients in the intensive care unit (ICU), leading to complications in 40–60% of all ICU patients. Patients with AKI who need continuous renal replacement therapy (CRRT) have a particularly poor prognosis [1‒4]. Although multiple randomized controlled studies have tried to reveal the question of when to start CRRT, only a small number of observational studies have been conducted so far to answer the question of when to stop CRRT [5‒8]. Further, the guidelines from the Kidney Disease: Improving Global Outcome (KDIGO) organization similarly do not clearly state an answer to the “when to stop CRRT” question. The concept called “CRRT trauma” has been claimed, meaning the adverse effects of CRRT itself, such as continuous anticoagulation, constant restraint, excessive deprivation of medications, and essential nutrients [9, 10]. Finding reliable predictors of successful CRRT discontinuation could be a solution. We had previously stated that the kinetic estimated glomerular filtration rate (kinetic eGFR) in addition to the volume of urine as a promising early indicator for successful CRRT discontinuation based on a retrospective observational study [11]. Another issue is that there is insufficient data to forecast the non-recovery of RRT requiring AKI. Although there are several AKI biomarkers to detect kidney damage leading to early diagnosis and risk stratification in AKI, they have not much been used for prognosis prediction. Based on the results obtained in our previous retrospective study, we therefore planned a prospective study to reveal the utility of existing clinical markers, including AKI biomarkers, in forecasting the prognosis of patients undergoing CRRT at the time of CRRT discontinuation.

Study Design and Participants

We prospectively enrolled all patients diagnosed with AKI and treated with CRRT at the University of Tokyo Hospital from August 2016 to March 2018. Patients who were under the age of 18, on CRRT for less than 12 h, or voluntarily discontinued intensive treatment were excluded from the analysis. Written informed consent was obtained from participants (or – if it applies – their parent/legal guardian/next of kin) to participate in the study. This study protocol was reviewed and approved by the Ethics Committee of the University of Tokyo, approval number 2810-(13) and 11239-(2). On CRRT stop, blood tests were performed and within 24 h of CRRT stop, urinary neutrophil gelatinase-associated lipocalin (NGAL), L-type fatty acid binding protein (L-FABP), sediments, and chemistries were obtained. Urinary NGAL and L-FABP were measured at the central laboratory using ARCHITECT urine NGAL chemiluminescent immunoassay (Abbott Japan, Tokyo, Japan), and Lumipulse Presto L-FABP chemiluminescent enzyme immunoassay (Fuji Revio, Tokyo, Japan), respectively. Urinary sediments were scored as previously described [12].

We reviewed the patients’ clinical and laboratory variables on the days of CRRT initiation and stop as well as their outcome variables (duration of CRRT, mortality, and renal function [eGFR and RRT dependence]) at 90 days after CRRT initiation. We also reviewed daily weight, urine volume, and serum creatinine (sCr) to calculate the kinetic eGFR. Urine volume was recorded hourly, and daily laboratory tests were performed daily at 6:00 a.m. We defined the blood test at CRRT stop as day 0, and the results of the next day’s 6:00 a.m. tests as day 1. The interval between the two tests, in hours, was designated as T.

We used sCr and eGFR at the last outpatient visit before admission to define baseline renal function. If the outpatient values were unknown, we used the smaller of the sCr value obtained on admission to the hospital or after discharge. GFR was estimated using the Modification of Diet in Renal Disease equation for Japan [13]. The kinetic eGFR was calculated using the approach suggested by Chen et al. [14], with the assumption that the maximum sCr rise is 1.5 mg/dL/day. This assumption includes a constant creatinine production and volume of distribution during the study period. The following is the equation for kinetic eGFR on day 1:

Successful CRRT discontinuation was defined as no treatment with CRRT within 48 h nor with intermittent hemodialysis within 7 days after the last CRRT discontinuation [11]. Major adverse kidney events (MAKEs) at 90 days were defined as eGFR decline of more than 25% compared with baseline, RRT dependence, or death within 90 days after CRRT initiation.

Statistical Analysis

Descriptive statistics were presented separately for the patients in the CRRT discontinued and non-discontinued groups. Continuous variables were presented as medians with interquartile range and were compared using the Wilcoxon rank-sum test. Categorical variables were compared using Fischer’s exact test. The effectiveness of each variable for predicting CRRT discontinuation was ascertained using the receiver operating characteristic (ROC) curve from a univariate logistic regression analysis. Multiple-variable combinations were examined using multivariate logistic regression analyses. Optimal cut-off values were defined using the Youden index (sensitivity + specificity −1), which is a typical ROC curve summary measure indicating the maximum potential effectiveness of a marker [15]. The ROC curves were compared using a previously reported method [16]. All statistical analyses were conducted in JMP Pro version 13 (SAS Institute Inc., Cary, NC). A p value of <0.05 was deemed to indicate statistical significance.

Survival and RRT Trajectory of All Patients with AKI Requiring CRRT

From August 1, 2016, to March 31, 2018, 109 patients received CRRT for AKI in the ICU of the University of Tokyo Hospital. We excluded 26 patients who chose to withdraw from intensive care, 1 patient on CRRT for less than 12 h, 2 patients not consenting to participate, and 7 patients with insufficient data for the analysis. Of 73 patients included in the statistical analysis, 14 were designated as “CRRT non-discontinued” because they failed to meet the criteria for successful CRRT discontinuation and 26 were designated as experiencing of MAKEs (Fig. 1). We could not follow-up 12 patients who transferred to another hospital before 90 days. The RRT trajectory revealed that the RRT dependency rate at 90 days after CRRT start was 6% (6/97), and all were on intermittent RRT (Fig. 2; online suppl. fig. S1; for all online suppl. material, see https://doi.org/10.1159/000532034).

Fig. 1.

Flowchart of the patients enrolled in the study. Of 109 patients who received continuous renal replacement therapy (CRRT) for acute kidney injury (AKI), 36 patients were excluded (26 chose to withdraw from intensive care, seven had insufficient data for the analysis, two did not consent, one received CRRT for less than 12 h). Of the 73 patients included in the analysis, 59 were classified as the “discontinued” group and 14 were classified as the “non-discontinued” group.

Fig. 1.

Flowchart of the patients enrolled in the study. Of 109 patients who received continuous renal replacement therapy (CRRT) for acute kidney injury (AKI), 36 patients were excluded (26 chose to withdraw from intensive care, seven had insufficient data for the analysis, two did not consent, one received CRRT for less than 12 h). Of the 73 patients included in the analysis, 59 were classified as the “discontinued” group and 14 were classified as the “non-discontinued” group.

Close modal
Fig. 2.

Survival and renal replacement therapy (RRT) trajectory after continuous renal replacement therapy (CRRT) start. The RRT trajectory shows the RRT dependency rate at 90 days after CRRT start was 6% (6/97), and all were on intermittent RRT (IRRT).

Fig. 2.

Survival and renal replacement therapy (RRT) trajectory after continuous renal replacement therapy (CRRT) start. The RRT trajectory shows the RRT dependency rate at 90 days after CRRT start was 6% (6/97), and all were on intermittent RRT (IRRT).

Close modal

Patient Characteristics and Outcomes

At CRRT initiation, sCr and blood urea nitrogen were lower in the discontinued group than those in the non-discontinued group (online suppl. Table S1). Online supplementary Table S1 also presents patient outcomes. In the discontinued group, the CRRT duration was significantly shorter, MAKEs were fewer, and RRT dependence at 90 days was less frequent.

Predictive Parameters for CRRT Discontinuation

Parameters that could predict CRRT discontinuation were evaluated by univariate analysis for the non-discontinued and the discontinued groups (Table 1; online suppl. S2). Previously reported predictive parameters, including urine volume and kinetic eGFR, were all significantly different between the groups. Urinary NGAL, L-FABP, protein, N-acetyl-β-d-glucosaminidase, and osmolality were also significantly different. The urinary sediments score did not show significant difference. Table 3 and online suppl. S3 represents the results of the ROC curve analysis, which confirmed that urine volume is most accurate predictor (area under the curve [AUC] 0.91 [0.80–0.96]) and could not be significantly improved by combination with any other parameter (Table 3; online suppl. Fig. S2). A previously proposed discontinued index was also evaluated (online suppl. Table S4) [11] and was not found to be of added utility (AUC 0.76 [0.62–0.86]) compared with the single parameter of urine volume (online suppl. Table S4). The use of diuretics was also confirmed to have no significant effect on the utility of the predictive parameters (online suppl. Table S5). In this prospective study, urine volume was confirmed to be the most accurate predictor of RRT discontinuation.

Table 1.

CRRT discontinuation predictive factors

Non-discontinued (n = 14)Discontinued (n = 59)p value
Urine chemistry 
 Cl (mEq/L) 71 [40–107] 101 [75–118] 0.08 
 IP (mg/dl) 4.0 [1.5–11.0] 1.0 [1.0–11.5] 0.09 
 UA (mg/dL) 4.0 [2.0–9.0] 5.5 [2.3–19.0] 0.15 
 Protein (mg/dL) 83.8 [43.2–192.5] 20.0 [9.8–38.5] <0.0001*** 
 NAG (IU/L) 41.5 [14.3–156.9] 15.9 [9.2–38.1] 0.029* 
 α1MG (mg/L) 43.7 [39.8–66.4] 35.1 [14.0–69.9] 0.12 
 Osmolality (mOsm/kg H2O) 309 [289–334] 338 [312–392] 0.0065** 
Candidate predictive factors for CRRT discontinuation 
 Urine volume d0 (mL/day) 391 [54–999] 1,912 [1,232–2,608] <0.0001*** 
 Cr at CRRT initiation (mg/dL) 3.85 [2.38–5.32] 2.20 [1.41–3.67] 0.0068** 
 eGFR d1 (mL/min/1.73 m217.2 [12.5–33.1] 36.7 [24.3–59.6] 0.0028** 
 Cr clearance (mL/min) 3.5 [1.6–16.6] 33.5 [18.0–65.8] <0.0001*** 
 KeGFR d1 (mL/min/1.73 m27.9 [-5.3–24.3] 27.2 [15.2–49.4] 0.0043** 
Urinary biomarkers 
 uL-FABP (μg/gCr) 234.0 [57.9–786.5] 68.1 [18.25–246.0] 0.018* 
 uNGAL (ng/mL) 452.8 [222.2–2,283.0] 128.5 [34.7–345.2] 0.0015** 
Non-discontinued (n = 14)Discontinued (n = 59)p value
Urine chemistry 
 Cl (mEq/L) 71 [40–107] 101 [75–118] 0.08 
 IP (mg/dl) 4.0 [1.5–11.0] 1.0 [1.0–11.5] 0.09 
 UA (mg/dL) 4.0 [2.0–9.0] 5.5 [2.3–19.0] 0.15 
 Protein (mg/dL) 83.8 [43.2–192.5] 20.0 [9.8–38.5] <0.0001*** 
 NAG (IU/L) 41.5 [14.3–156.9] 15.9 [9.2–38.1] 0.029* 
 α1MG (mg/L) 43.7 [39.8–66.4] 35.1 [14.0–69.9] 0.12 
 Osmolality (mOsm/kg H2O) 309 [289–334] 338 [312–392] 0.0065** 
Candidate predictive factors for CRRT discontinuation 
 Urine volume d0 (mL/day) 391 [54–999] 1,912 [1,232–2,608] <0.0001*** 
 Cr at CRRT initiation (mg/dL) 3.85 [2.38–5.32] 2.20 [1.41–3.67] 0.0068** 
 eGFR d1 (mL/min/1.73 m217.2 [12.5–33.1] 36.7 [24.3–59.6] 0.0028** 
 Cr clearance (mL/min) 3.5 [1.6–16.6] 33.5 [18.0–65.8] <0.0001*** 
 KeGFR d1 (mL/min/1.73 m27.9 [-5.3–24.3] 27.2 [15.2–49.4] 0.0043** 
Urinary biomarkers 
 uL-FABP (μg/gCr) 234.0 [57.9–786.5] 68.1 [18.25–246.0] 0.018* 
 uNGAL (ng/mL) 452.8 [222.2–2,283.0] 128.5 [34.7–345.2] 0.0015** 

CRRT, continuous renal replacement therapy; KeGFR, Kinetic eGFR; AUC, area under the curve; CI, confidence interval; d0, the day of CRRT discontinuation; d1, the following day of CRRT discontinuation; IP, inorganic phosphate; UN, urea nitrogen; UA, uric acid; NAG, N-acetyl-β-d-glucosaminidase.

*p value <0.05.

**p value <0.01.

***p value <0.001.

Predictive Parameters for MAKEs and Mortality within 90 Days

To identify parameters predictive for MAKEs within 90 days, the likely parameters were assessed by univariate analysis (Table 2; online suppl. S6). Most predictive parameters for CRRT discontinuation failed to show significance, but urinary NGAL and L-FABP were distinct for the non-MAKEs and the MAKEs groups. Table 3 presents the outcomes of the ROC curve analysis for MAKEs prediction. Compared to urine volume, urinary NGAL was a significant predictor with AUC 0.76 (0.62–0.86) (Table 3; online suppl. Table S7; online suppl. Fig. S3). Diuretic use had no significant effect on the AUC of urinary NGAL (online suppl. Table S8).

Table 2.

90-day MAKEs predictive factors

Non-MAKEs (n = 47)MAKEs (n = 26)p value
Candidate predictive factors for CRRT discontinuation 
 Urine volume d0 (mL/day) 1,785 [1,215–2,237] 1,501 [289–2,269] 0.21 
 Cr at CRRT initiation (mg/dL) 2.32 [1.44–3.70] 2.75 [2.02–3.84] 0.50 
 eGFR d1 (mL/min/1.73 m231.4 [20.8–49.1] 36.5 [23.4–49.8] 0.69 
 Cr clearance (mL/min) 33.9 [16.5–67.4] 20.9 [3.4–34.3] 0.053 
 KeGFR d1 (mL/min/1.73 m222.0 [11.9–40.8] 27.1 [12.4–76.0] 0.87 
Urinary biomarkers 
 uL-FABP (μg/gCr) 51.2 [18.6–168.0] 232.0 [71.8–577.0] 0.005** 
 uNGAL (ng/mL) 121.0 [30.5–317.2] 381.3 [123.6–1,632.7] 0.0004*** 
Non-MAKEs (n = 47)MAKEs (n = 26)p value
Candidate predictive factors for CRRT discontinuation 
 Urine volume d0 (mL/day) 1,785 [1,215–2,237] 1,501 [289–2,269] 0.21 
 Cr at CRRT initiation (mg/dL) 2.32 [1.44–3.70] 2.75 [2.02–3.84] 0.50 
 eGFR d1 (mL/min/1.73 m231.4 [20.8–49.1] 36.5 [23.4–49.8] 0.69 
 Cr clearance (mL/min) 33.9 [16.5–67.4] 20.9 [3.4–34.3] 0.053 
 KeGFR d1 (mL/min/1.73 m222.0 [11.9–40.8] 27.1 [12.4–76.0] 0.87 
Urinary biomarkers 
 uL-FABP (μg/gCr) 51.2 [18.6–168.0] 232.0 [71.8–577.0] 0.005** 
 uNGAL (ng/mL) 121.0 [30.5–317.2] 381.3 [123.6–1,632.7] 0.0004*** 

CRRT, continuous renal replacement therapy; KeGFR, Kinetic eGFR; AUC, area under the curve; CI, confidence interval; d0, the day of CRRT discontinuation; d1, the following day of CRRT discontinuation.

*p value <0.05.

**p value <0.01.

***p value <0.001.

Table 3.

ROC analysis of predictive factors for CRRT discontinuation and MAKE 90

CRRT discontinuationMAKE 90
AUC [95% CI]p valueAUC [95% CI]p value
UV d0 (mL/day) 0.91 [0.80–0.96] Reference 0.59 [0.44–0.73] Reference 
Cr at CRRT initiation (mg/dL) 0.73 [0.59–0.84] 0.026* 0.45 [0.32–0.59] 0.2 
eGFR d1 (ml/min/1.73 m20.77 [0.58–0.88] 0.15 0.47 [0.34–0.61] 0.17 
Cr clearance (mL/min) 0.86 [0.67–0.95] 0.52 0.64 [0.49–0.77] 0.51 
KeGFR d1 (ml/min/1.73 m20.75 [0.56–0.88] 0.14 0.49 [0.35–0.63] 0.26 
uL-FABP (μg/gCr) 0.71 [0.54–0.84] 0.023* 0.70 [0.55–0.82] 0.26 
uNGAL (ng/mL) 0.78 [0.62–0.89] 0.034* 0.76 [0.62–0.86] 0.027* 
UV d0+KeGFR d1 0.92 [0.84–0.97] 0.42   
UV d0+KeGFR d1+uL-FABP 0.95 [0.88–0.98] 0.19   
UV d0+KeGFR d1+uNGAL 0.93 [0.85–0.97] 0.13   
CRRT discontinuationMAKE 90
AUC [95% CI]p valueAUC [95% CI]p value
UV d0 (mL/day) 0.91 [0.80–0.96] Reference 0.59 [0.44–0.73] Reference 
Cr at CRRT initiation (mg/dL) 0.73 [0.59–0.84] 0.026* 0.45 [0.32–0.59] 0.2 
eGFR d1 (ml/min/1.73 m20.77 [0.58–0.88] 0.15 0.47 [0.34–0.61] 0.17 
Cr clearance (mL/min) 0.86 [0.67–0.95] 0.52 0.64 [0.49–0.77] 0.51 
KeGFR d1 (ml/min/1.73 m20.75 [0.56–0.88] 0.14 0.49 [0.35–0.63] 0.26 
uL-FABP (μg/gCr) 0.71 [0.54–0.84] 0.023* 0.70 [0.55–0.82] 0.26 
uNGAL (ng/mL) 0.78 [0.62–0.89] 0.034* 0.76 [0.62–0.86] 0.027* 
UV d0+KeGFR d1 0.92 [0.84–0.97] 0.42   
UV d0+KeGFR d1+uL-FABP 0.95 [0.88–0.98] 0.19   
UV d0+KeGFR d1+uNGAL 0.93 [0.85–0.97] 0.13   

CRRT, continuous renal replacement therapy; KeGFR, Kinetic eGFR; UV, urine volume; AUC, area under the curve; CI, confidence interval; SE, sensitivity; Sp, specificity; d0, the day of CRRT discontinuation; d1, the following day of CRRT discontinuation.

*p value <0.05.

**p value <0.01.

***p value <0.001.

In the present prospective observational study, we examined markers at CRRT discontinuation that could predict successful CRRT discontinuation and MAKEs within 90 days. Urine volume is reported to predict successful CRRT discontinuation as summarized in online suppl. Table S9 includes four meta-analyses [5, 6, 8, 17] and multiple other studies [18‒35]. Even though we have confirmed that urine volume was the strongest predictive parameter for CRRT discontinuation, it did not demonstrate a significant ability to predict long-term outcomes, such as MAKEs within 90 days. In contrast, urinary NGAL had a significant predictive ability for MAKEs within 90 days. AKI injury markers such as urinary NGAL and L-FABP have different properties and utilities compared with those of functional markers such as urine volume and sCr.

In the present prospective study, we measured two AKI biomarkers, urinary NGAL and L-FABP, at CRRT discontinuation. Despite extensive research on these biomarkers as early diagnostic markers of AKI, the evidence for their utility as renal recovery markers is limited. Zeng et al. [36] measured L-FABP and NGAL multiple times in postsurgical patients with AKI and reported that an AUC of 0.70 was the maximum for a prediction of AKI non-recovery. During Acute Renal Failure Trial Network study in patients receiving CRRT for AKI, Srisawat et al. [37] demonstrated that urinary NGAL measured on days 1, 7, and 14 were useful in predicting a renal recovery in 60 days with an AUC of 0.70. Our findings are consistent with the previous reports and more focused on variables measured at CRRT discontinuation. Yang et al. [38] reported that serum osteopontin and interleukin 6 at CRRT discontinuation predict 60-day renal recovery and survival. Our findings indicate that urinary NGAL at CRRT discontinuation predicts MAKEs within 90 days, which will be clinically beneficial because urinary NGAL can be used as a tool for predicting renal recovery and categorizing patients with AKI who require dialysis.

We comprehensively evaluated the potential renal recovery predictive ability of markers such as sCr, eGFR, kinetic eGFR, creatinine clearance, other urinary chemistry measurements, and urinary sediments. Despite the clinical utility of urinary sediments in AKI diagnosis [12, 39], no predictive value of the urinary sediments score for either CRRT discontinuation or MAKEs within 90 days was revealed. We were also unable to validate the utility of kinetic eGFR as an additive to urine volume in either a multivariate model or a discontinuation index score as reported in our previous study [11]. One possible reason is that the kinetic eGFR was overestimated in patients receiving CRRT as a result of a decrease in creatinine production because of sepsis [40] or long-duration CRRT [41]. Diuretic use did not affect the main findings in the present study, but the furosemide stress test is another functional test that is a candidate predictor of renal recovery and therefore requires further investigation [42, 43].

This study has several limitations. First, the generalizability of the data is unknown because this prospective cohort study was conducted at a single health center. To demonstrate reproducibility, our findings must be replicated in another cohort. Second, the study did not define any specific criteria for CRRT discontinuation or resumption; we therefore cannot rule out the possibility that the predictive parameters influenced the decision to discontinue or resume CRRT and must acknowledge the possible presence of circular arguments. To exclude this bias, prospective multicenter studies with predefined CRRT discontinuation criteria should be conducted.

In conclusion, our study demonstrated that urine volume, a functional renal marker, predicts successful discontinuation of RRT. On the other hand, urinary NGAL, a structural renal marker, predicts long-term renal outcomes. These observations indicate that the renal structural and functional markers at CRRT discontinuation have different roles in predicting the outcomes of severe AKI requiring CRRT.

We are thankful to all physicians, nurses, and medical engineers in particular who have taken care of patients involved in this study in the University of Tokyo Hospital.

Written informed consent was obtained from participants (or – if it applies – their parent/legal guardian/next of kin) to participate in the study. This study protocol was reviewed and approved by the Ethics Committee of the University of Tokyo, approval number 2810-(13) and 11239-(2).

The authors have no conflicts of interest to declare.

The authors have no funding sources to declare.

T.Y. and K.D. conceived the study design. T.Y., R.M., Y.K., Y.M., and K.Y., conducted patient care and obtained consent. T.Y. and K.D. wrote the manuscript. Y.H., E.N., M.N., and K.D. reviewed the manuscript critically.

The data that support the findings of this study are not publicly available due to the privacy of research participants but are available from the authors.

1.
Nisula
S
,
Kaukonen
KM
,
Vaara
ST
,
Korhonen
AM
,
Poukkanen
M
,
Karlsson
S
.
Incidence, risk factors and 90-day mortality of patients with acute kidney injury in Finnish intensive care units: the FINNAKI study
.
Intensive Care Med
.
2013 Mar
39
3
420
8
.
2.
Luo
X
,
Jiang
L
,
Du
B
,
Wen
Y
,
Wang
M
,
Xi
X
.
A comparison of different diagnostic criteria of acute kidney injury in critically ill patients
.
Crit Care
.
2014 Jul
18
4
R144
.
3.
Hoste
EA
,
Bagshaw
SM
,
Bellomo
R
,
Cely
CM
,
Colman
R
,
Cruz
DN
.
Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study
.
Intensive Care Med
.
2015 Aug
41
8
1411
23
.
4.
Fujii
T
,
Uchino
S
,
Doi
K
,
Sato
T
,
Kawamura
T
JAKID study group
.
Diagnosis, management, and prognosis of patients with acute kidney injury in Japanese intensive care units: the JAKID study
.
J Crit Care
.
2018 Oct
47
185
91
.
5.
Hansrivijit
P
,
Yarlagadda
K
,
Puthenpura
MM
,
Ghahramani
N
,
Thongprayoon
C
,
Vaitla
P
.
A meta-analysis of clinical predictors for renal recovery and overall mortality in acute kidney injury requiring continuous renal replacement therapy
.
J Crit Care
.
2020 Dec
60
13
22
.
6.
Katulka
RJ
,
Al Saadon
A
,
Sebastianski
M
,
Featherstone
R
,
Vandermeer
B
,
Silver
SA
.
Determining the optimal time for liberation from renal replacement therapy in critically ill patients: a systematic review and meta-analysis (DOnE RRT)
.
Crit Care
.
2020 Feb
24
1
50
.
7.
Schiffl
H
,
Lang
SM
.
Current approach to successful liberation from renal replacement therapy in critically ill patients with severe acute kidney injury: the quest for biomarkers continues
.
Mol Diagn Ther
.
2021
;
25
:
1
8
.
8.
Wang
L
,
Li
J
,
Sun
S
,
Du
H
,
Chen
P
,
Xu
Y
.
Predictors of successful discontinuation from renal replacement therapy during AKI: a meta-analysis
.
Semin Dial
.
2021
;
34
(
2
):
137
46
.
9.
Maynar Moliner
J
,
Honore
PM
,
Sanchez-Izquierdo Riera
JA
,
Herrera Gutierrez
M
,
Spapen
HD
.
Handling continuous renal replacement therapy-related adverse effects in intensive care unit patients: the dialytrauma concept
.
Blood Purif
.
2012 Oct
34
2
177
85
.
10.
Honore
PM
,
Jacobs
R
,
Joannes-Boyau
O
,
De Waele
E
,
Van Gorp
V
,
Boer
W
.
Con: dialy- and continuous renal replacement (CRRT) trauma during renal replacement therapy: still under-recognized but on the way to better diagnostic understanding and prevention
.
Nephrol Dial Transpl
.
2013 Nov
28
11
2723
7
; discussion 2727-8.
11.
Yoshida
T
,
Matsuura
R
,
Komaru
Y
,
Miyamoto
Y
,
Yoshimoto
K
,
Hamasaki
Y
.
Kinetic estimated glomerular filtration rate as a predictor of successful continuous renal replacement therapy discontinuation
.
Nephrology
.
2019
;
24
(
3
):
287
93
.
12.
Perazella
MA
,
Coca
SG
,
Hall
IE
,
Iyanam
U
,
Koraishy
M
,
Parikh
CR
.
Urine microscopy is associated with severity and worsening of acute kidney injury in hospitalized patients
.
Clin J Am Soc Nephrol
.
2010 Mar
5
3
402
8
.
13.
Matsuo
S
,
Imai
E
,
Horio
M
,
Yasuda
Y
,
Tomita
K
,
Nitta
K
.
Revised equations for estimated GFR from serum creatinine in Japan
.
Am J Kidney Dis
.
2009 Jun
53
6
982
92
.
14.
Chen
S
.
Retooling the creatinine clearance equation to estimate kinetic GFR when the plasma creatinine is changing acutely
.
J Am Soc Nephrol
.
2013 May
24
6
877
88
.
15.
Ruopp
MD
,
Perkins
NJ
,
Whitcomb
BW
,
Schisterman
EF
.
Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection
.
Biom J
.
2008 Jun
50
3
419
30
.
16.
DeLong
ER
,
DeLong
DM
,
Clarke-Pearson
DL
.
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
.
Biometrics
.
1988 Sep
44
3
837
45
.
17.
Li
Y
,
Deng
X
,
Feng
J
,
Xu
B
,
Chen
Y
,
Li
Z
.
Predictors for short-term successful weaning from continuous renal replacement therapy: a systematic review and meta-analysis
.
Ren Fail
.
2023 Dec
45
1
2176170
.
18.
Wheeler
TS
,
Tolwani
AJ
.
165 the 24-hour urine creatinine clearance predicts successful discontinuation of continuous renal replacement therapy in acute renal failure
.
J Investig Med
.
2007 Feb
55
1
S274
.
19.
Wu
VC
,
Ko
WJ
,
Chang
HW
,
Chen
YW
,
Lin
YF
,
Shiao
CC
.
Risk factors of early redialysis after weaning from postoperative acute renal replacement therapy
.
Intensive Care Med
.
2008 Jan
34
1
101
8
.
20.
Uchino
S
,
Bellomo
R
,
Morimatsu
H
,
Morgera
S
,
Schetz
M
,
Tan
I
.
Discontinuation of continuous renal replacement therapy: a post hoc analysis of a prospective multicenter observational study
.
Crit Care Med
.
2009 Sep
37
9
2576
82
.
21.
Solymos
O
,
Frohlich
S
,
Conlon
N
.
Use of 2-hourly creatinine clearance to inform cessation of renal replacement therapy
.
Crit Care
.
2011 Mar
15
S1
P130
.
22.
Frohlich
S
,
Donnelly
A
,
Solymos
O
,
Conlon
N
.
Use of 2-hour creatinine clearance to guide cessation of continuous renal replacement therapy
.
J Crit Care
.
2012 Dec
27
6
744 e1
5
.
23.
Heise
D
,
Gries
D
,
Moerer
O
,
Bleckmann
A
,
Quintel
M
.
Predicting restoration of kidney function during CRRT-free intervals
.
J Cardiothorac Surg
.
2012 Jan
7
1
6
.
24.
Gleeson
PJ
,
Crippa
IA
,
Sexton
DJ
,
Fontana
V
,
Taccone
F
,
Creteur
J
.
Determinants of renal recovery and mortality in patients undergoing continuous renal replacement therapy in the ICU
.
Intensive Care Med Exp
.
2015 Oct
3
S1
A54
.
25.
Aniort
J
,
Ait Hssain
A
,
Pereira
B
,
Coupez
E
,
Pioche
PA
,
Leroy
C
.
Daily urinary urea excretion to guide intermittent hemodialysis weaning in critically ill patients
.
Crit Care
.
2016 Feb
20
1
43
.
26.
Han
SS
,
Bae
E
,
Song
SH
,
Kim
DK
,
Kim
YS
,
Han
JS
.
NT-proBNP is predictive of the weaning from continuous renal replacement therapy
.
Tohoku J Exp Med
.
2016 May
239
1
1
8
.
27.
Katayama
S
,
Uchino
S
,
Uji
M
,
Ohnuma
T
,
Namba
Y
,
Kawarazaki
H
.
Factors predicting successful discontinuation of continuous renal replacement therapy
.
Anaesth Intensive Care
.
2016 Jul
44
4
453
7
.
28.
Viallet
N
,
Brunot
V
,
Kuster
N
,
Daubin
D
,
Besnard
N
,
Platon
L
.
Daily urinary creatinine predicts the weaning of renal replacement therapy in ICU acute kidney injury patients
.
Ann Intensive Care
.
2016 Dec
6
1
71
.
29.
Yang
T
,
Sun
S
,
Lin
L
,
Han
M
,
Liu
Q
,
Zeng
X
.
Predictive factors upon discontinuation of renal replacement therapy for long-term chronic dialysis and death in acute kidney injury patients
.
Artif Organs
.
2017 Dec
41
12
1127
34
.
30.
Itenov
TS
,
Berthelsen
RE
,
Jensen
JU
,
Gerds
TA
,
Pedersen
LM
,
Strange
D
.
Predicting recovery from acute kidney injury in critically ill patients: development and validation of a prediction model
.
Crit Care Resusc
.
2018 Mar
20
1
54
60
.
31.
Jeon
J
,
Kim
DH
,
Baeg
SI
,
Lee
EJ
,
Chung
CR
,
Jeon
K
.
Association between diuretics and successful discontinuation of continuous renal replacement therapy in critically ill patients with acute kidney injury
.
Crit Care
.
2018 Oct 10
22
1
255
.
32.
Kim
CS
,
Bae
EH
,
Ma
SK
,
Kim
SW
.
A prospective observational study on the predictive value of serum cystatin c for successful weaning from continuous renal replacement therapy
.
Kidney Blood Press Res
.
2018
;
43
(
3
):
872
81
.
33.
Raurich
JM
,
Llompart-Pou
JA
,
Novo
MA
,
Talavera
C
,
Ferreruela
M
,
Ayestarán
I
.
Successful weaning from continuous renal replacement therapy. Associated risk factors
.
J Crit Care
.
2018 Jun
45
144
8
.
34.
Chen
X
,
Chen
Z
,
Wei
T
,
Li
P
,
Zhang
L
,
Fu
P
.
The effect of serum neutrophil gelatinase-associated lipocalin on the discontinuation of continuous renal replacement therapy in critically ill patients with acute kidney injury
.
Blood Purif
.
2019 Jul
48
1
10
7
.
35.
Baeg
SI
,
Jeon
J
,
Yoo
H
,
Na
SJ
,
Kim
K
,
Chung
CR
.
A scoring model with simple clinical parameters to predict successful discontinuation of continuous renal replacement therapy
.
Blood Purif
.
2021 Sep
50
6
779
89
.
36.
Zeng
XF
,
Li
JM
,
Tan
Y
,
Wang
ZF
,
He
Y
,
Chang
J
.
Performance of urinary NGAL and L-FABP in predicting acute kidney injury and subsequent renal recovery: a cohort study based on major surgeries
.
Clin Chem Lab Med
.
2014 May
52
5
671
8
.
37.
Srisawat
N
,
Wen
X
,
Lee
M
,
Kong
L
,
Elder
M
,
Carter
M
.
Urinary biomarkers and renal recovery in critically ill patients with renal support
.
Clin J Am Soc Nephrol
.
2011 Aug
6
8
1815
23
.
38.
Yang
T
,
Sun
S
,
Zhao
Y
,
Liu
Q
,
Han
M
,
Lin
L
.
Biomarkers upon discontinuation of renal replacement therapy predict 60-day survival and renal recovery in critically ill patients with acute kidney injury
.
Hemodial Int
.
2018 Jan
22
1
56
65
.
39.
Bagshaw
SM
,
Haase
M
,
Haase-Fielitz
A
,
Bennett
M
,
Devarajan
P
,
Bellomo
R
.
A prospective evaluation of urine microscopy in septic and non-septic acute kidney injury
.
Nephrol Dial Transpl
.
2012 Feb
27
2
582
8
.
40.
Doi
K
,
Yuen
PS
,
Eisner
C
,
Hu
X
,
Leelahavanichkul
A
,
Schnermann
J
.
Reduced production of creatinine limits its use as marker of kidney injury in sepsis
.
J Am Soc Nephrol
.
2009 Jun
20
6
1217
21
.
41.
Wilson
FP
,
Sheehan
JM
,
Mariani
LH
,
Berns
JS
.
Creatinine generation is reduced in patients requiring continuous venovenous hemodialysis and independently predicts mortality
.
Nephrol Dial Transpl
.
2012 Nov
27
11
4088
94
.
42.
van der Voort
PH
,
Boerma
EC
,
Koopmans
M
,
Zandberg
M
,
de Ruiter
J
,
Gerritsen
RT
.
Furosemide does not improve renal recovery after hemofiltration for acute renal failure in critically ill patients: a double blind randomized controlled trial
.
Crit Care Med
.
2009 Feb
37
2
533
8
.
43.
McMahon
BA
,
Chawla
LS
.
The furosemide stress test: current use and future potential
.
Ren Fail
.
2021 Dec
43
1
830
9
.