Biomarkers to predict the need for renal replacement therapy in severe acute kidney injury: an ancillary analysis of a multicenter randomized controlled trial

doi:10.21203/rs.3.rs-4674568/v1

Download PDF

Research Article

Biomarkers to predict the need for renal replacement therapy in severe acute kidney injury: an ancillary analysis of a multicenter randomized controlled trial

https://doi.org/10.21203/rs.3.rs-4674568/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Introduction

Predicting the need for renal replacement therapy (RRT) in acute kidney injury (AKI) remains challenging. The utility of biomarkers was explored during previous studies which were biased as RRT indications relied on clinician opinion rather than evidence. Those studies preceded trials that clarified RRT initiation criteria. We aimed to assess biomarkers in predicting criteria for RRT initiation in severe AKI patients.

Material and methods

This is an ancillary study of the AKIKI2 trial. Patients with severe AKI (stage 3) receiving invasive mechanical ventilation and/or vasopressors were included. Blood and urine samples were collected within 12 hours after the occurrence of severe AKI. The primary endpoint was the onset of rigorous criteria for RRT initiation within 72 hours after severe AKI. We analyzed routine serum biomarkers (pH, serum potassium, serum creatinine) and novel urinary and serum biomarkers (CCL14, KIM1, nicotinamide and its metabolites, cDPP3, plasma proenkephalin A 119–159).

Results

Among the 256 patients, 101 (39%) met at least one criterion for RRT initiation or died within 72 hours. No biomarker demonstrated satisfactory predictive performance for the primary endpoint. Urinary CCL14 showed potential interest in toxic-induced AKI (AUC 0.74 [0.57–0.90]). No novel biomarker was significantly associated with the occurrence of MAKE₆₀. In multivariate analysis, ‘SAPSIII’ and ‘Serum potassium level at D0’ were significantly associated with the occurrence of MAKE₆₀.

Conclusion

Neither routine nor novel biomarkers demonstrated conclusive predictive accuracy for the need for RRT in severe AKI patients. Given evidence-based criteria for initiating RRT, the tested biomarkers may not effectively guide RRT initiation.

Acute Kidney Injury

Renal replacement therapy

Biomarkers

This ancillary study of the AKIKI2 trial found that within the context of rigorous RRT initiation criteria, biomarkers may not be suitable for guiding RRT initiation nor informing renal prognosis in patients with severe AKI potentially requiring RRT.

Acute Kidney Injury (AKI) in critically ill patients is associated with high morbidity and mortality, particularly when renal replacement therapy (RRT) is required[1].

Reliable tools for predicting the need for RRT in this patient population are currently lacking. Biomarkers were claimed to be valid for achieving this objective [2]. In a recent prospective observational study (RUBY), a panel of novel candidate biomarkers was tested to identify persistent AKI[3]. However, that study did not address the question of whether any of these biomarkers are useful in identifying patients who will require RRT. The vast majority of studies addressing this issue did not clearly define the indications for RRT[4]. This may be a major limitation as they actually assessed how biomarkers predict practitioner's decision regarding RRT initiation, a decision that can vary significantly within a country, a hospital or even among medical teams[5]. Finally, these studies were conducted before the publication of four large randomized controlled trials (RCTs) (AKIKI, IDEAL-ICU, STARRT-AKI, and AKIKI2) that clarified RRT indications in critically ill patients [6–9].

Given these developments, it is of utmost interest to assess the ability of biomarkers to predict the onset of RRT initiation criteria in patients with severe AKI based on these recent studies. To achieve this goal, we relied on a biobank prospectively collected during the observational stage of the AKIKI2 study.

Study design and endpoints

The MARKISIO study is an ancillary study of AKIKI2[6], a multicenter randomized controlled trial conducted in 39 ICUs in France from May 2018 to December 2019 that compared two delayed RRT initiation strategies in critically ill patients: a standard delayed strategy and a more delayed strategy. Adult patients hospitalized in the ICU with severe AKI (defined as Kidney Disease: Improving Global Outcomes [KDIGO] stage 3)[10] who were receiving (or had received for this episode) invasive mechanical ventilation and/or vasopressors were first included in an observational stage (prior to the randomization stage). A standard delayed RRT initiation strategy, similar to that used in the AKIKI trial[7], was applied during the observational stage. With this strategy, RRT was withheld until occurrence of one of the following criteria: oliguria/anuria for more than 72 hours or blood urea nitrogen level above 112 mg/dl (serum urea level of 40 mmol/L). Once such criteria were present, patients were then randomized to immediate RRT initiation (standard group) or to a more delayed strategy in which RRT was withheld until BUN level reached a value of 140mg/dl, regardless of the duration of oliguria/anuria.

In investigating centers that agreed to participate in the biobanking, blood and urine samples were prospectively collected at the time of inclusion in the observational stage (i.e within a 12-hour window after documentation of stage 3 AKI) (see supplementary appendix).

The study protocol, including the biobank collection, was approved by the competent French legal authority (Comité de Protection des Personnes de Sud-Est V) for all participating centers. All analyses were performed in accordance with the guidelines of the International Conference on Harmonization and Good Clinical Practice.

The primary endpoint was the occurrence of a criteria for RRT initiation within the 72h after severe AKI onset. Those criteria were: urgent indications (severe hyperkalemia, severe metabolic acidosis, severe pulmonary edema), or oliguria/anuria (urine output < 0,3 mL/kg/h or < 500 mL/day) for more than 72 h, or blood urea nitrogen level of 112 mg/dL or more (serum urea concentration of 40 mmol/L or more) (details are provided in a supplementary appendix, Table S1). To avoid overlooking competitive risk issues, patients who died within 72 h of enrollment were also considered endpoint positive.

The secondary endpoint was the composite of RRT dependency or absence of renal recovery or death 60 days after the inclusion defining major adverse kidney events at 60 days (MAKE₆₀). The absence of recovery was defined as ≥ 25% loss in estimated glomerular filtration rate (eGFR). The estimated GFR was calculated from the serum creatinine levels using the MDRD equation.

Sample and data collection

Blood and urine samples were collected at the time of inclusion in the observational stage and then centrifuged at the investigating center. The resultant plasma, serum, and urine supernatants were quickly frozen and stored at -80°C. Shipments from investigating centers to central biobank (located in Bichat hospital, Paris, France) were done every three months by a secure transportation company in insulated boxes cooled with dry ice. The samples were thawed immediately before analysis.

All patients were followed for up to 60 days after enrollment. The data collection included demographics, comorbidities, exposure to nephrotoxic agents, SAPS III score on ICU admission, clinical and laboratory data throughout the ICU stay, occurrence of RRT initiation criteria, renal function recovery, and survival 60 days after inclusion.

Biomarker testing

We focused on a set of urinary and serum biomarkers chosen for their promising nature and their relevance to distinct pathophysiological pathways implicated in AKI and renal recovery. We refer to them as "novel biomarkers.” We also collected more traditional biomarkers routinely monitored (pH, serum potassium and serum creatinine) and refer to them as “routine biomarkers.” These routine biomarkers were assessed locally as part of standard patient care.

Novel Biomarkers were measured blinded to the clinical data:

C-C motif chemokine ligand 14 (CCL14) and Kidney Injury Molecule 1 (KIM1) were measured in urine samples at INSERM UMRS 1155 (Tenon Hospital, Paris, France) using commercial kits from ABCAM. For interpretation purposes, KIM1 levels were normalized to urine creatinine levels.
Nicotinamide and its metabolites N-methyl-2-pyridone-5-carboxamide (N2PY) and Methylnicotinamide (MNM) in plasma samples were simultaneously quantified by Liquid Chromatography coupled to tandem Mass Spectrometry (LC-MS/MS) method in the pharmacology unit of Jean Verdier hospital (Bondy, France) (more details are provided in supplementary appendix).
Circulating dipeptidyl peptidase (cDPP3) concentration was measured in ethylenediaminetetraacetic acid (EDTA) plasma using a luminescence immunoassay (4TEEN4 Pharmaceuticals GmbH, Hennigsdorf, Germany)[11]. Proenkephalin A 119–159 was measured in ethylenediaminetetraacetic acid (EDTA) plasma using Sphingotest® penKid® luminescence immunoassay (Sphingotec GmbH, Hennigsdorf, Germany) [12, 13]. cDPP3 and Proenkephalin A 119–159 (penKid) measurements were performed by 4TEEN4 Pharmaceuticals GmbH and Sphingotec GmbH, respectively, blinded to the clinical data.

Statistical analyses

Continuous variables were tested for normality using the Shapiro-Wilk test. A sample t-test was used for normally distributed data, whereas the Mann-Whitney U test was applied for non-normally distributed data. The assumptions for homogeneity of variances were checked before applying these tests. Categorical data were compared using the chi-squared test or Fisher’s exact test, depending on the sample size and distribution of the variables.

The predictive potential of the biomarkers for the primary endpoint was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The bootstrap method was used to calculate the confidence intervals of the AUC. In an exploratory analysis, we evaluated the predictive potential of all biomarkers across three distinct subpopulations characterized by specific causes of AKI: sepsis, rhabdomyolysis, and nephrotoxicity.

A logistic regression model was performed, with backward elimination variable selection, for the occurrence of both primary outcome and MAKE60. The log-linearity assumption was checked for all continuous variables and dichotomized in case of violation.

Missing data were addressed using multiple imputations. All statistical analyses were conducted using R 3.5.1 and Python 3.9. Specifically, the ROCR package in R and scikit-learn library in Python were utilized. Statistical significance was defined as a two-sided p-value of < 0.05. Flowchart was provided with whimsical tool.

Patients and outcomes

Among the 767 patients included in the observational stage of AKIKI2, 261 were enrolled in the MARKISIO study, of whom 256 (98%) were available for the analysis of the primary endpoint (Fig. 1). Among these patients, 101 (39%) had at least one RRT initiation criteria (urgent indication, oligo-anuria for more than 72hours or serum urea > 112mg/dl) and 12 died within 72 hours after inclusion. Patient characteristics according to “onset of RRT initiation criteria within 72 hours” (the primary endpoint) are presented in Table 1. Features did not differ in age, coexisting conditions (chronic kidney disease, congestive heart failure, hypertension, and diabetes mellitus), and severity scores (SAPS III and SOFA). Acute Respiratory Distress Syndrom (ARDS) was more common in patients who required RRT within 72 hours than in the rest of patients (40.7% vs 23.8% respectively, p < 0.01). The primary causes of AKI were shock and/or sepsis. At the end of follow-up (60 days after the inclusion), 117 (45.7%) patients had died. No patient was dependent from RRT and 27 (10.5%) did not fully recover renal function (among survivors) leading to the occurrence of MAKE₆₀ in 144 (56.2%) patients.

Table 1

Baseline patients characteristics ^a
Characteristic	All n = 256	Need for RRT within 72 hours n = 113	No need for RRT within 72 hours n = 143	P value
Age — yr	67 ± 17	67 ± 15	67 ± 17	-
Sex— no. (%) - Male - Female	166 (64.6) 90 (35.0)	76 (67.3) 37 (32.7)	90 (62.9) 53 (37.1)	0.56
Serum creatinine before ICU admission^b	0.9 ± 0.3	1 ± 0.4	0.9 ± 0.3	0.06
Coexisting conditions — no. (%) - Chronic kidney disease^c - Congestive heart failure - Hypertension - Diabetes mellitus - Ischemic heart disease	27 (10.5) 12 (4.6) 140 (54.7) 79 (30.9) 26 (10.2)	12 (10.6) 6 (5.3) 64 (56.6) 33(29.2) 33(29.2)	15 (10.5) 6 (4.2) 76 (53.1) 46 (32.2) 14 (9.8)	0.90 0.90 0.67 0.71 0.90
SAPS III at enrollment^d	70 ± 19	70 ± 21	70 ± 18	-
SOFA at enrollment^e	11 ± 3	11 ± 3	11 ± 3	-
Exposure to at least one nephrotoxic agent in past 2 days^f	138 (54)	62 (52)	76 (55)	0.69
Septic shock^g	138 (53.9)	56 (49.6)	82 (57.3.4)	0.18
ARDS	88 (31.2)	46 (40.7)	34 (23.8)	< 0.01
Physiological support - Invasive mechanical ventilation - Vasopressor support (epinephrine or norepinephrine)	181 (70.7) 194 (75.8)	86 (76.1) 89 (78.8)	95 (66.4) 105 (73.4)	0.12 0.40
Cause of AKI — no./total no. (%) - Shock - Sepsis - Rhabdomyolysis - Nephrotoxic agent	204 (79.4) 166 (64.6) 22 (8.6) 35 (13.6)	90 (79.6) 77 (68.1) 11 (9.7) 18 (15.9)	114 (79.7) 89 (62.2) 11 (7.7) 17 (11.9)	- 0.40 0.72 0.45
a Plus–minus values are means ± SD. To convert values for creatinine to micromoles per liter, multiply by 88.4
b The serum creatinine concentration before ICU admission was either determined with the use of values measured in the 12 months preceding the ICU stay or was estimated
c Kidney function was reassessed with Cockcroft formula using available serum creatinine at baseline
d The Simplified Acute Physiology Score (SAPS) III ranges from 0 to 146, with higher scores indicating more severe disease and a higher risk of death
e The Sepsis-related Organ Failure Assessment (SOFA) score ranges from 0 to 24, with higher scores indicating more severe organ failure
f Some patients were exposed to more than one nephrotoxic agent
g Septic shock was defined as sepsis induced hypotension despite fluid resuscitation of at least 30 ml per kilogram of body weight of intravenous fluid administered within the period spanning the 4 h before and 4 h after initiation of vasopressor therapy

Biomarker performances for the primary outcome

Neither routine biomarkers nor any novel biomarker demonstrated satisfactory performance for predicting the need for RRT initiation within 72 hours after severe AKI (Fig. 2) in the whole patient population. The AUC for prediction of the need for RRT within 72 hours ranged from 0.58 to 0.60 for routine biomarkers and from 0.51 to 0.65 for novel biomarkers. Urinary CCL14 demonstrated a modest predictive performance (AUC = 0.65; 95%CI = 0.58 to 0.71).

An exploratory analysis of the subpopulations according to the cause of AKI is presented in Fig. 3. In patients with sepsis, no biomarker demonstrated a satisfactory performance in predicting the primary endpoint. For patients who received potentially nephrotoxic agents, urinary CCL14 exhibited a modest predictive performance (AUC 0.74 [0.56–0.90]).

In multivariate analysis (logistic regression model using clinical and biological variables), the five variables significantly associated with the occurrence of the primary outcome, presented in order from the strongest to the weakest association, were: ‘pH min’, ‘ARDS’ ‘serum creatinine at D0’, ‘urinary CCL14’ and ‘DPP3’ (Table 2).

Table 2

Logistic regression for prediction of the need for RRT within 72h
Variables	Odds Ratio [95% CI Lower-95% CI Upper]	P value
Urinary CCL14	1.02 [1.01–1.04]	< 0.01
DPP3	1.02 [1-1.04]	0.02
ARDS	2.04 [1.1–3.77]	0.02
SCr D0	1.31 [1.11–1.54]	< 0.01
pH min < 7.2	3.41 [1.57–7.42]	< 0.01
CI: confidence interval, ARDS: Acute respiratory distress syndrom, SCr D0: serum creatinine at day 0

Biomarkers performances for the secondary outcome

In multivariate analysis (logistic regression model using clinical and biological variables), ‘SAPS III’ and ‘Serum potassium at D0’ were the only two variables significantly associated with the occurrence of MAKE₆₀ (Table 3). No novel biomarkers were found to be significantly associated with this outcome.

Table 3

Logistic regression for prediction of MAKE₆₀
Variables	Odds Ratio [95% CI Lower-95% CI Upper]	P-value
SAPSIII	1.02 [1-1.04]	0.02
K-max D0	1.5 [1.05–2.14]	0.02
CI: confidence interval; K-max : maximum serum potassium concentration at day 0
Full multivariate model included SAPS III, K-max D0, Age, METHYLNICOTINAMIDE, cDPP3, ARDS, CKD, serum creatinine at day 0

The MARKISIO study was designed to evaluate the ability of a panel of both routine and novel biomarkers to predict the need for RRT within 72 hours following the onset of severe AKI. Among the 256 patients enrolled, 39% met at least one criterion for RRT initiation according to rigorous criteria or died within 72 hours post-inclusion and 56.2% presented a major adverse kidney event 60 days after inclusion. No biomarker achieved an acceptable predictive performance for these outcomes.

Previous studies, which were gathered in a meta-analysis published in 2018, have reported the potential utility of various biomarkers in predicting the initiation of RRT in patients with AKI [4]. Our study refutes these conclusions. This can be largely explained by to the more rigorous definition of the criteria for initiation. Indeed, we took advantage of the results of RCTs that were not available at the time when the vast majority of studies on biomarkers were issued. These recent RCTs allowed the use of well-defined and reproducible RRT initiation criteria that are now considered by many as the standard of care [14, 15]. In contrast, previous studies assessed the utility of biomarkers to predict the physician's decision to initiate RRT; decisions varied widely from one country to another, from one hospital to another, and sometimes within the same team [5]. Historically, there has been a tendency to initiate RRT based on the perceived severity of a patient condition rather than on objective criteria (such as metabolic complications of AKI). This approach not only muddies the predictive power of biomarkers but also creates a circular logic that the ancient Greeks would recognize as “Ouroboros” also known as “the snake that bites its own tail”.

The KDIGO guidelines [10] illustrate this paradox by defining stage 3 AKI as "initiation of RRT." This creates a self-perpetuating cycle in which the severity of a patient condition (Stage 3 AKI) is partly defined by the decision to initiate RRT, which, in turn, has historically been influenced by the perceived severity of patient condition. This feeds back into itself, obscuring both the predictive utility of biomarkers and objective assessment of a patient's need for RRT. This kind of aporia likely explains the consistent efficacy demonstrated by the majority of biomarkers in predicting the specific clinical endpoint of RRT initiation. They were probably primarily assessing the overall severity which is relatively straightforward to evaluate.

The biomarkers chosen for this study were selected based on their potential relevance to the diverse pathophysiological mechanisms implicated in AKI and the potential necessity for RRT. Specifically, urinary KIM1 was included for its presumed role in both kidney injury and recovery. The process of kidney cellular regeneration after toxic or ischemic injury involves undifferentiated proximal tubular epithelial cells that disperse onto denuded areas of the basement membrane and reconstitute a continuous epithelial layer[16]. The KIM1 protein is expressed in these dedifferentiated cells and may play a role in their adhesion to the basement membrane and dispersion onto damaged areas[16, 17]. Nicotinamide and its metabolites, (N2PY and Methylnicotinamide) were examined for their involvement in metabolic pathways directly impacting renal function recovery. In the context of AKI, marked diminutions in nicotinamide adenine dinucleotide (NAD+) concentration, for which nicotinamide is a precursor have an adverse impact on energy synthesis, thereby undermining the primary renal function of selective solute filtration. In contrast, elevated NAD + levels may confer resilience to renal tubules when exposed to a range of acute stressors, making this mechanism one of the most promising directions for therapeutic intervention. Indeed, investigation of this pathway is highly relevant in this particular setting [18–20]. The marker cDPP3 was assessed for its role in the regulation role in Renin Angiotensin System (RAS) known for its implication in glomerular hemodynamics renal inflammation and fibrotic process [21–23]. PenKid was assessed for its previously described potential ability to detect early AKI and renal recovery[24, 25]. Lastly, we evaluated urinary CCL14, given that recent research has suggested its potential as a predictor of persistent AKI and, consequently, lack of renal recovery[3, 26].

In contrast to our study, RUBY [3] found urinary CCL14 to be a promising marker for persistent AKI and subsequent renal non-recovery. In that study, the primary outcome was persistent stage 3 AKI within 72 hours. This outcome was met in more than 50% of cases because RRT was initiated in the absence of pre-specified criteria, creating a circular reasoning as explained above. Although urinary Neutrophil Gelatinase-Associated Lipocalin (NGAL) has been widely claimed a potential biomarker for AKI, we consciously chose not to include it in our panel for this study. This decision was supported by the disappointing performance of NGAL in previous studies, including the ELAIN study on the timing of RRT initiation, where this biomarker failed to discriminate between varying stages of AKI severity[27]. Similarly, the STARRT-AKI study, the largest study in this field to date which initially considered NGAL, opted not to include it in the main study because of its very poor performance in its pilot phase.[8, 28].

One of the principal strengths of our study lies in its methodological rigor, informed by the latest evidence from randomized controlled trials on RRT timing[7, 8] [6, 9]. This allowed us to apply recognized criteria for initiating RRT, thereby reducing the variability often observed in clinical practice. Additionally, the diversity of biomarkers assessed enhances the comprehensiveness of our findings. However, several limitations should be acknowledged. The most significant constraint is the relatively small sample size, which limits the statistical power and generalizability of our results. It is worth noting, however, that this limitation is not unique to our study; the RUBY trial and many other studies on biomarkers also operated under the same constraint of limited sample sizes. Furthermore, the cross-sectional nature of our biomarker measurements could be seen as a limitation, as it provides only a snapshot of a dynamic physiological process. This issue is also present in most other studies, including the RUBY study, due to the practical challenges of obtaining longitudinal biomarker data in a clinical setting. We also could not evaluate the NephroCheck biomarkers (Insulin-like Growth Factor Binding Protein 7 [IGFBP-7] and Tissue Inhibitor of Metalloproteinase 2 [TIMP-2]) or perform the NephroCheck test itself due to the unavailability of kits from the manufacturer, which may have affected the comprehensiveness of our biomarker assessment. In summary, we found that no biomarker demonstrated significant predictive accuracy for the need for RRT within 72 hours after severe AKI onset in ICU patients. In the context of rigorous RRT initiation criteria, these biomarkers may not be suitable for guiding RRT initiation nor informing renal prognosis in patients with severe AKI potentially requiring RRT.

The AKIKI 2 trial was promoted by the Assistance Publique - Hôpitaux de Paris and funded by a grant of the French Ministry of Health (Programme Hospitalier de Recherche Clinique 2016; AOM16278).

Circulating DPP3 concentration was measured free of charge by 4TEEN4 Pharmaceuticals GmbH, Hennigsdorf, Germany

Plasma proenkephalin A 119-159 concentration was measured free of charge by Sphingotec GmbH, Hennigsdorf, Germany

The Cardiovascular Markers in Stress Conditions Research Group is supported by a research grant from 4TEEN4 Pharmaceuticals GmbH, which allowed salary support for A. Picod.

A. Mebazaa received fees as a member of advisory board from Sphingotec

The other authors declare no competing interest regarding the submitted work.

Contributors

KC, AP, MB, DD, and SG were responsible for the design, analysis, and writing of the manuscript.

KC, ST, VJ, SP, JM, AM, FA and SG were responsible for the organization of the samples collection and the measurements of biomarkers

GL, LML, DTB, BL, SB, JB, GC, NC, SB, CV, JMF, DT, GL, SN, JM, KK, JR, JDR and JPQ were responsible for recruitment and clinical care of the patients.

All authors reviewed and approved the final version of the manuscript

Acknowledgements

We thank patients and their surrogates and all medical and nursing teams.

Hoste EAJ, Kellum JA, Selby NM, et al (2018) Global epidemiology and outcomes of acute kidney injury. Nat Rev Nephrol 14:607–625. https://doi.org/10.1038/s41581-018-0052-0
Meersch M, Zarbock A, Küllmar M (2018) Renal biomarkers for the initiation of renal replacement therapy—is this the future? J Thorac Dis 10:S3229–S3232. https://doi.org/10.21037/jtd.2018.08.44
Hoste E, Bihorac A, Al-Khafaji A, et al (2020) Identification and validation of biomarkers of persistent acute kidney injury: the RUBY study. Intensive Care Med 46:943–953. https://doi.org/10.1007/s00134-019-05919-0
Klein SJ, Brandtner AK, Lehner GF, et al (2018) Biomarkers for prediction of renal replacement therapy in acute kidney injury: a systematic review and meta-analysis. Intensive Care Med 44:323–336. https://doi.org/10.1007/s00134-018-5126-8
Legrand M, Darmon M, Joannidis M, Payen D (2013) Management of renal replacement therapy in ICU patients: an international survey. Intensive Care Med 39:101–108. https://doi.org/10.1007/s00134-012-2706-x
Gaudry S, Hajage D, Martin-Lefevre L, et al (2021) Comparison of two delayed strategies for renal replacement therapy initiation for severe acute kidney injury (AKIKI 2): a multicentre, open-label, randomised, controlled trial. The Lancet 397:1293–1300. https://doi.org/10.1016/S0140-6736(21)00350-0
Gaudry S, Hajage D, Schortgen F, et al (2016) Initiation Strategies for Renal-Replacement Therapy in the Intensive Care Unit. N Engl J Med 375:122–133. https://doi.org/10.1056/NEJMoa1603017
STARRT-AKI Investigators, Canadian Critical Care Trials Group, Australian and New Zealand Intensive Care Society Clinical Trials Group, et al (2020) Timing of Initiation of Renal-Replacement Therapy in Acute Kidney Injury. N Engl J Med 383:240–251. https://doi.org/10.1056/NEJMoa2000741
Barbar SD, Clere-Jehl R, Bourredjem A, et al (2018) Timing of Renal-Replacement Therapy in Patients with Acute Kidney Injury and Sepsis. New England Journal of Medicine 379:1431–1442. https://doi.org/10.1056/NEJMoa1803213
Khwaja A (2012) KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract 120:c179-184. https://doi.org/10.1159/000339789
Rehfeld L, Funk E, Jha S, et al (2019) Novel Methods for the Quantification of Dipeptidyl Peptidase 3 (DPP3) Concentration and Activity in Human Blood Samples. The Journal of Applied Laboratory Medicine 3:943–953. https://doi.org/10.1373/jalm.2018.027995
Ernst A, Köhrle J, Bergmann A (2006) Proenkephalin A 119–159, a stable proenkephalin A precursor fragment identified in human circulation. Peptides 27:1835–1840. https://doi.org/10.1016/j.peptides.2006.03.008
Ng LL, Sandhu JK, Narayan H, et al (2014) Proenkephalin and prognosis after acute myocardial infarction. J Am Coll Cardiol 63:280–289. https://doi.org/10.1016/j.jacc.2013.09.037
Gaudry S, Palevsky PM, Dreyfuss D (2022) Extracorporeal Kidney-Replacement Therapy for Acute Kidney Injury. New England Journal of Medicine 386:964–975. https://doi.org/10.1056/NEJMra2104090
Evans L, Rhodes A, Alhazzani W, et al (2021) Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med 47:1181–1247. https://doi.org/10.1007/s00134-021-06506-y
Bailly V, Zhang Z, Meier W, et al (2002) Shedding of Kidney Injury Molecule-1, a Putative Adhesion Protein Involved in Renal Regeneration *. Journal of Biological Chemistry 277:39739–39748. https://doi.org/10.1074/jbc.M200562200
Geng J, Qiu Y, Qin Z, Su B (2021) The value of kidney injury molecule 1 in predicting acute kidney injury in adult patients: a systematic review and Bayesian meta-analysis. Journal of Translational Medicine 19:105. https://doi.org/10.1186/s12967-021-02776-8
Ralto KM, Rhee EP, Parikh SM (2020) NAD + homeostasis in renal health and disease. Nat Rev Nephrol 16:99–111. https://doi.org/10.1038/s41581-019-0216-6
Poyan Mehr A, Tran MT, Ralto KM, et al (2018) De novo NAD + biosynthetic impairment in acute kidney injury in humans. Nat Med 24:1351–1359. https://doi.org/10.1038/s41591-018-0138-z
Fontecha-Barriuso M, Lopez-Diaz AM, Carriazo S, et al (2021) Nicotinamide and acute kidney injury. Clin Kidney J 14:2453–2462. https://doi.org/10.1093/ckj/sfab173
Picod A, Placier S, Genest M, et al (2024) Circulating Dipeptidyl Peptidase 3 Modulates Systemic and Renal Hemodynamics Through Cleavage of Angiotensin Peptides. Hypertension 81:927–935. https://doi.org/10.1161/HYPERTENSIONAHA.123.21913
Picod A, Deniau B, Vaittinada Ayar P, et al (2021) Alteration of the Renin–Angiotensin–Aldosterone System in Shock: Role of the Dipeptidyl Peptidase 3. Am J Respir Crit Care Med 203:526–527. https://doi.org/10.1164/rccm.202010-3873LE
Jha S, Taschler U, Domenig O, et al (2020) Dipeptidyl peptidase 3 modulates the renin–angiotensin system in mice. Journal of Biological Chemistry 295:13711–13723. https://doi.org/10.1074/jbc.RA120.014183
Dépret F, Hollinger A, Cariou A, et al (2020) Incidence and Outcome of Subclinical Acute Kidney Injury Using penKid in Critically Ill Patients. Am J Respir Crit Care Med 202:822–829. https://doi.org/10.1164/rccm.201910-1950OC
von Groote T, Albert F, Meersch M, et al (2022) Proenkephalin A 119–159 predicts early and successful liberation from renal replacement therapy in critically ill patients with acute kidney injury: a post hoc analysis of the ELAIN trial. Crit Care 26:333. https://doi.org/10.1186/s13054-022-04217-4
Bagshaw SM, Al-Khafaji A, Artigas A, et al (2021) External validation of urinary C-C motif chemokine ligand 14 (CCL14) for prediction of persistent acute kidney injury. Crit Care 25:185. https://doi.org/10.1186/s13054-021-03618-1
Zarbock A, Kellum JA, Schmidt C, et al (2016) Effect of Early vs Delayed Initiation of Renal Replacement Therapy on Mortality in Critically Ill Patients With Acute Kidney Injury: The ELAIN Randomized Clinical Trial. JAMA 315:2190–2199. https://doi.org/10.1001/jama.2016.5828
Smith OM, Wald R, Adhikari NKJ, et al (2013) Standard versus accelerated initiation of renal replacement therapy in acute kidney injury (STARRT-AKI): study protocol for a randomized controlled trial. Trials 14:320. https://doi.org/10.1186/1745-6215-14-320

Download PDF

Version 1

posted

You are reading this latest preprint version

Biomarkers to predict the need for renal replacement therapy in severe acute kidney injury: an ancillary analysis of a multicenter randomized controlled trial

Status:

Version 1

Abstract

Introduction

Material and methods

Results

Conclusion

Figures

Take home messages

Introduction

Methods

Study design and endpoints

Sample and data collection

Biomarker testing

Statistical analyses

Results

Patients and outcomes

Biomarker performances for the primary outcome

Biomarkers performances for the secondary outcome

Discussion

Declarations

References

Supplementary Files

Status:

Version 1