Kinetic eGFR and Novel AKI Biomarkers to Predict Renal Recovery

Association between the levels of urinary cell cycle biomarkers and non-recovery of renal function among critically ill geriatric patients with acute kidney injury

The lack of early renal function recovery among geriatric patients with acute kidney injury (AKI) in the intensive care unit (ICU) is a commonly observed and acknowledged poor prognostic factor, especially for older adults. However, no reliable prognostic biomarker is available for identifying individuals at risk of renal non-recovery or mortality in older adults. In this prospective observational cohort study, we enrolled critically ill older adults (aged ≥ 60 years) with AKI from the ICU and followed their disease progression. The primary endpoint was renal non-recovery within seven days of follow-up, while the secondary endpoint was the determinants of 30-day mortality after AKI. We assessed the predictive accuracy using receiver operating characteristic curves and performed between-group comparisons using the log-rank test. Among 209 older adults, 117 (56.0%) experienced renal recovery. Multiple regression analysis revealed that urine levels of tissue inhibitor of metalloproteinase-2 (TIMP-2) multiplied by insulin-like growth factor-binding protein 7 (IGFBP7) ([TIMP-2]*[IGFBP7]), AKI stages 2-3, and the Acute Physiology and Chronic Health Evaluation (APACHE II) score were independently associated with renal non-recovery. The regression model incorporating [TIMP-2]*[IGFBP7] demonstrated a fair predictive value (AUC 0.774, p < 0.001), with the optimal threshold set at 0.81 (ng/mL)2/1000. When [TIMP-2]*[IGFBP7] was combined with AKI severity and the APACHE score, the AUC increased to 0.851. In conclusion, urine [TIMP-2]*[IGFBP7] is a reliable biomarker associated with renal non-recovery in critically ill older adults, and its predictive efficacy can be further enhanced when combined with AKI severity and the APACHE score.

Persistent acute kidney injury biomarkers: A systematic review and meta-analysis

BACKGROUND

Various biomarkers reportedly predict persistent acute kidney injury (AKI) despite their varying predictive performance across clinical trials. This study aims to compare the accuracy of various biomarkers in predicting persistent AKI in different populations and regions.

METHODS

In this meta-analysis, we searched for urinary C-C motif chemokine ligand 14 (CCL14), Tissue inhibitor of metalloproteinase-2&insulin-like growth factor-binding protein-7 (TIMP-2&IGFBP7), Neutrophil Gelatinase-Associated Lipocalin (NGAL), plasma Cystatin C (pCysC), Soluble urokinase plasminogen activator receptor (suPAR), Proenkephalin (PenK) and urinary dickkopf-3:urinary creatinine (uDKK3:uCr) from various databases including Medline, PubMed, Embase, and Cochrane. This was geared towards predicting persistent AKI in adults (>18 years). Hierarchically summarized subject work characteristic curves (HSROC) and diagnostic odds ratio (DOR) values were used to summarize the diagnostic accuracy of the biomarkers. Further, meta-regression and subgroup analyses were carried out to identify sources of heterogeneity as well as evaluate the best predictive biomarkers in different populations and regions.

RESULTS

We screened 31 studies from 2,356 studies and assessed the diagnostic value of 7 biomarkers for persistent AKI. Overall, CCL14 had the best diagnostic efficacy with an AUC of 0.79 (95% CI 0.75-0.82), whereas TIMP-2 & IGFBP7, NGAL, and pCysC had diagnostic efficacy of 0.75 (95% CI 0.71-0.79), 0.71 (95% CI 0.67-0.75), and 0.7007, respectively. Due to a limited number of studies, PenK, uDKK3:uCr, and suPAR were not subjected to meta-analysis; however, relevant literature reported diagnostic efficacy above 0.70. Subgroup analyses based on population, region, biomarker detection time, AKI onset time, and AKI duration revealed that in the intensive care unit (ICU) population, the AUC of CCL14 was 0.8070, the AUC of TIMP-2 & IGFBP7 was 0.726, the AUC of pCysC was 0.72, and the AUC of NGAL was 0.7344; in the sepsis population, the AUC of CCL14 was 0.85, the AUC of TIMP-2&IGFBP7 was 0.7438, and the AUC of NGAL was 0.544; in the post-operative population, the AUC of CCL14 was 0.83-0.93, the AUC of TIMP-2&IGFBP7 was 0.71, and the AUC of pCysC was 0.683. Regional differences were observed in biomarker prediction of persistent kidney injury, with AUCs of 0.8558 for CCL14, 0.7563 for TIMP-2 & IGFBP7, and 0.7116 for NGAL in the Eurasian American population. In the sub-African population, TIMP-2 & IGFBP7 had AUCs of 0.7945, 0.7418 for CCL14, 0.7097 for NGAL, and 0.7007 for pCysC. for TIMP-2 & IGFBP7 was 0.7945, AUC for CCL14 was 0.7418, AUC for NGAL was 0.7097, and AUC for pCysC was 0.7007 in the sub-African population. Duration of biomarker detection, AKI onset, and AKI did not influence the optimal predictive performance of CCL14. Subgroup analysis and meta-regression of CCL14-related studies revealed that CCL14 is the most appropriate biomarker for predicting persistent stage 2-3 AKI, with heterogeneity stemming from sample size and AKI staging.

CONCLUSION

This meta-analysis discovered CCL14 as the best biomarker to predict persistent AKI, specifically persistent stage 2-3 AKI.

Determining steady-state trough range in vancomycin drug dosing using machine learning

BACKGROUND

Vancomycin is a renally eliminated, nephrotoxic, glycopeptide antibiotic with a narrow therapeutic window, widely used in intensive care units (ICU). We aimed to predict the risk of inappropriate vancomycin trough levels and appropriate dosing for each ICU patient.

METHODS

Observed vancomycin trough levels were categorized into sub-therapeutic, therapeutic, and supra-therapeutic levels to train and compare different classification models. We included adult ICU patients (≥ 18 years) with at least one vancomycin concentration measurement during hospitalization at Mayo Clinic, Rochester, MN, from January 2007 to December 2017.

RESULT

The final cohort consisted of 5337 vancomycin courses. The XGBoost models outperformed other machine learning models with the AUC-ROC of 0.85 and 0.83, specificity of 53% and 47%, and sensitivity of 94% and 94% for sub- and supra-therapeutic categories, respectively. Kinetic estimated glomerular filtration rate and other creatinine-based measurements, vancomycin regimen (dose and interval), comorbidities, body mass index, age, sex, and blood pressure were among the most important variables in the models.

CONCLUSION

We developed models to assess the risk of sub- and supra-therapeutic vancomycin trough levels to improve the accuracy of drug dosing in critically ill patients.

Urinary cell cycle biomarkers for the prediction of renal non-recovery in patients with septic acute kidney injury: a prospective study

BACKGROUND

Poor prognosis has been associated with the absence of renal recovery after acute kidney injury (AKI). This study aimed to investigate whether urinary biomarkers at 0 and 24 h could be used independently or in conjunction with a clinical model to predict renal non-recovery in septic AKI.

METHODS

A prospective observational study was conducted to measure the urinary levels of insulin-like growth factor-binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinase-2 (TIMP-2) at the time of AKI diagnosis (0 h) and 24 h later. Renal non-recovery within 7 days was defined as the outcome. The predictive value of urinary biomarkers for renal non-recovery in septic AKI was assessed using the area under the curve (AUC).

RESULTS

A total of 198 individuals with septic AKI were included in the final analysis. Among them, 38.9% (n = 77) did not experience renal recovery within 7 days. The combination of urinary IGFBP7 and TIMP-2 at the initial time point demonstrated prognostic value for non-recovery of renal function, with an AUC of 0.782. When [TIMP-2]*[IGFBP7] was measured at 0 h, the clinical prognostic model, incorporating AKI stage 2-3 and the non-renal sequential organ failure assessment score, showed an improved AUC of 0.822 (with a sensitivity of 88.3% and specificity of 59.5%).

CONCLUSIONS

The combination of urinary [TIMP-2]*[IGFBP7] at 0 h exhibited moderate predictive ability for renal non-recovery in cases of septic AKI. However, there is potential to enhance the prognostic capabilities of the [TIMP-2]*[IGFBP7]-clinical prediction model.

When to discontinue renal replacement therapy. what do we know?

PURPOSE OF REVIEW

Acute kidney injury is common in intensive care patients. Supportive care involves the use of renal replacement therapies as organ support. Initiation of renal replacement therapy has been the subject of much interest over the last few years with several randomised controlled studies examining the optimal time to commence treatment. In contrast to this, little evidence has been generated regarding cessation of therapy. Given that this treatment is complex, not without risk and expensive it seems timely that efforts should be expended at examining this vexing issue.

RECENT FINDINGS

Although several studies have been reported examining the successful discontinuation of renal replacement therapies all studies reported to-date are observational in nature. Conventional biochemical criteria have been used as well as physiological parameters including urine output. More recently, more novel biomarkers of renal function have been studied. Although to-date no optimal variable nor threshold for discontinuation can be established.

SUMMARY

Several variables have been described which may have a role in determining which patients may be successfully weaned from renal replacement therapy. However, few have been exposed to vigorous examination and evidence is sparse in support of any potential approach although urine output currently is the most often described. More recently novel biomarkers have also been examined but again are limited by study design and heterogeneity. Further research is clearly needed focussing on proposed variables preferably in multivariate models to improve predictive ability and successful cessation of therapy.

Amikacin-induced acute kidney injury in mechanically ventilated critically ill patients with sepsis

In this retrospective cohort study, we aimed to evaluate the incidence, risk factors and outcomes of amikacin-induced acute kidney injury (AKI) in critically ill patients with sepsis. A total of 311 patients were included in the study. Of them, 83 (26.7%) had amikacin-induced AKI. In model 1, the multivariable analysis demonstrated concurrent use of colistin (OR 25.51, 95%CI 6.99-93.05, p< 0.001), presence of septic shock during amikacin treatment (OR 4.22, 95%CI 1.76-10.11, p=0.001), and Charlson Comorbidity Index (OR 1.14, 95%CI 1.02-1.28, p=0.025) as factors independently associated with an increased risk of amikacin-induced AKI. In model 2, the multivariable analysis demonstrated concurrent use of at least one nephrotoxic agent (OR 1.95, 95%CI 1.10-3.45; p=0.022), presence of septic shock during amikacin treatment (OR 3.48, 95%CI 1.61-7.53; p=0.002), and Charlson Comorbidity Index (OR 1.12, 95%CI 1.01-1.26; p=0.037) as factors independently associated with an increased risk of amikacin-induced AKI. In conclusion, before amikacin administration, the risk of AKI should be considered, especially in patients with multiple complicated comorbid diseases, septic shock, and those receiving colistin therapy.

Kinetic Estimated Glomerular Filtration Rate in Predicting Paediatric Acute Kidney Disease

Kinetic estimation of glomerular filtration rate (KeGFR) has proved its utility in predicting acute kidney injury (AKI) in both adults and children. Our objective is to assess the clinical utility of KeGFR in predicting AKI severity and progression to acute kidney disease (AKD) in patients already diagnosed with AKI and to examine major adverse kidney events at 30 days (MAKE30). We retrospectively calculated the KeGFR within the first 24 h of identified AKI (KeGFR1) and in the 24 h prior to AKD (KeGFR2) in all admitted children under 18 years old. The cohort consisted of 803 patients with AKI. We proposed a new classification of KeGFR stages, from 1 to 5, and assessed the predictive value of KeGFR stages for AKD development and MAKE30. AKI severity was associated with lower KeGFRs. KeGFR1 and KeGFR2 predicted AKD with AUC values between 0.777 and 0.841 respectively, p < 0.001. KeGFR2 had the best performance in predicting MAKE30 (AUC of 0.819) with a sensitivity of 66.67% and specificity 87.7%. KeGFR1 stage 3, 4 and 5 increased the risk of AKD by 3.07, 6.56 and 28.07 times, respectively, while KeGFR2 stage 2, 3, 4 and 5 increased the risk of AKD 2.79, 3.58, 32.75 and 80.14 times. Stage 5 KeGFR1 and KeGFR2 stages 3, 4 and 5 increased the risk of MAKE30 by 7.77, 4.23. 5.89 and 69.42 times in the adjusted models. KeGFR proved to be a useful tool in AKI settings. KeGFR dynamics can predict AKI severity, duration and outcomes.

The predictive value of [TIMP-2]*[IGFBP7] in adverse outcomes for acute kidney injury: a systematic review and meta-analysis

MATERIALS AND METHODS

Relevant articles published up to 17 June 2023 were retrieved from five databases (Cochrane Library/Embase/PubMed/SinoMed/Web of Science). The pre-established inclusion and exclusion criteria determined the selection of publications. Pooled sensitivity (SEN), specificity (SPE), diagnostic odds ratio, likelihood ratio, and summary receiver operating characteristic curve were employed to assess the predictive value. The presence or potential sources of heterogeneity were investigated via subgroup and SEN analyses.

RESULTS

Ten published and eligible studies (1559 cases) were included in the evaluation for the capability of [TIMP-2]*[IGFBP7] to predict the poor prognosis of AKI through the random effect model. Pooled SEN, SPE, diagnostic odds ratio, and positive and negative likelihood ratios were 0.82 (95% CI: 0.77-0.86, I2 = 53.4%), 0.64 (95% CI: 0.61-0.67, I2 = 88.3%), 14.06 (95% CI: 7.31-27.05, I2 = 55.0%), 2.859 (95% CI: 2.15-3.77, I2 = 80.7%), and 0.28 (95% CI: 0.20-0.40, I2 = 35.0%), respectively. The estimated area under the curve was 0.8864 (standard error: 0.0306), and the Q* was 0.7970 (standard error: 0.0299). The endpoints and cutoff values were the main causes of heterogeneity.

CONCLUSIONS

[TIMP-2]*[IGFBP7] is possible in predicting poor prognosis of AKI, but it is better to be applied along with other indicators or clinical risk factors.

NephroCheck at 10: addressing unmet needs in AKI diagnosis and risk stratification

Despite its name, the current diagnosis of acute kidney injury (AKI) still depends on markers of decreased kidney function and not on markers of injury. This results in a delayed diagnosis: AKI is diagnosed based on serum creatinine criteria only when the severity of injury is enough to decrease glomerular filtration rate. Moreover, by the time AKI is diagnosed, the insult may have already ceased, and even appropriate therapy targeted at the specific insult and its associated pathogenic pathways may no longer be effective. Biomarkers of injury are needed that allow the diagnosis of AKI based on injury criteria. At least three commercially available immunoassays assessing urinary or plasma neutrophil gelatinase-associated lipocalin and urinary tissue inhibitor of metalloproteinases-2 × insulin-like growth factor-binding protein-7 ([TIMP2]*[IGFBP7]) (NephroCheck®) have generated promising data regarding prediction and early diagnosis of AKI, although their relative performance may depend on clinical context. Recently, a urinary peptidomics classifier (PeptAKI) was reported to predict AKI better than current biomarkers. Focusing on [TIMP2]*[IGFBP7], the cellular origin of urinary TIMP2 and IGFBP7 remains unclear, especially under the most common predisposing condition for AKI, i.e. chronic kidney disease. We now discuss novel data on the kidney cell expression of TIMP2 and IGFBP7 and its clinical implications.

Clinical and Biochemical Profile Associated with Renal Recovery after Acute Kidney Injury in A Mexican Population: Retrospective Cohort Study

Background and Objectives: Our primary objective was to study the clinical and biochemical characteristics associated with acute kidney injury (AKI) remission in a group of Mexican patients. Materials and methods: We retrospectively enrolled 75 patients who were diagnosed with AKI and separated the sample into two groups: nonremitting patients (n = 27, 36%) vs. remitting patients (n = 48, 64%). Results: We found significant relationships between nonremitting AKI and previous diagnosis of chronic kidney disease (p = 0.009), higher serum creatinine (Cr) at admission (p < 0.0001), lower estimated glomerular filtration rate (eGFR) (p < 0.0001), maximum serum creatinine during hospitalization (p < 0.0001), higher fractional excretion of sodium (FENa) (p < 0.0003) and 24-h urine protein (p = 0.005), higher serum potassium on admission (p = 0.025), abnormal levels of procalcitonin (p = 0.006), and increased risk of death (p = 0.015). Conclusion: Chronic kidney disease (CKD), lower eGFR, higher levels of serum creatinine during hospitalization, higher FENa and 24-h urine protein, abnormal levels of procalcitonin, and higher serum potassium on admission were associated with nonremitting AKI. These findings may facilitate the rapid identification of patients at risk for nonremitting AKI based on clinical and biochemical characteristics. Furthermore, these findings may inform the design of timely strategies for the vigilance, prevention, and treatment of AKI.

Novel Biomarkers for Early Detection of Acute Kidney Injury and Prediction of Long-Term Kidney Function Decline after Partial Nephrectomy

Background: Identifying acute kidney injury (AKI) within few hours of onset is certainly helpful. However, early prediction of a long-term eGFR decline may be an even more important goal. Our aim was to identify and compare serum [creatinine, kineticGFR, cystatin C, neutrophil gelatinase–associated lipocalin (NGAL)] and urinary (NephroCheck, NGAL, proteinuria, albuminuria, acantocytes at urinary sediment) predictors of AKI that might efficiently predict long-term GFR decline after robotic Nephron-Spearing Surgery (rNSS). Methods: Monocentric prospective observational study. Patients scheduled for rNSS for suspected localized Renal Cell Carcinoma from May 2017 to October 2017 were enrolled. Samples were collected preoperatively and postoperatively (timepoints: 4 h, 10 h, 24 h, 48 h), while kidney function was re-assessed up to 24 months. Results: 38 patients were included; 16 (42%) developed clinical AKI. The eGFR decline at 24 months was more pronounced after postoperative AKI (−20.75 vs. −7.20, p < 0.0001). KineticGFR at 4 h (p = 0.008) and NephroCheck at 10 h (p = 0.001) were, at multivariable linear regression analysis, efficient predictors of post-operative AKI and long-term eGFR decline if compared to creatinine (R2 0.33 vs. 0.04). Conclusions: NephroCheck and kineticGFR have emerged as promising noninvasive, accurate, and early biomarkers of postoperative AKI and long-term GFR decline after rNSS. Combining NephroCheck and kineticGFR in clinical practice would allow to identify high risk of postoperative AKI and long-term GFR decline as early as 10 h after surgery.

Analysis of urinary C-C motif chemokine ligand 14 (CCL14) and first-generation urinary biomarkers for predicting renal recovery from acute kidney injury: a prospective exploratory study

BACKGROUND

Acute kidney injury (AKI) is a frequent syndrome in the intensive care unit (ICU). AKI patients with kidney function recovery have better short-term and long-term prognoses compared with those with non-recovery. Numerous studies focus on biomarkers to distinguish them. To better understand the predictive performance of urinary biomarkers of renal recovery in patients with AKI, we evaluated C-C motif chemokine ligand 14 (CCL14) and two first-generation biomarkers (cell cycle arrest biomarkers and neutrophil gelatinase-associated lipocalin) in two ICU settings.

METHODS

We performed a prospective study to analyze urinary biomarkers for predicting renal recovery from AKI. Patients who developed AKI after ICU admission were enrolled and urinary biomarkers including tissue inhibitor of metalloproteinase-2 (TIMP-2), insulin-like growth factor-binding protein 7 (IGFBP7), CCL14, and neutrophil gelatinase-associated lipocalin (NGAL) were detected on the day of AKI diagnosis. The primary endpoint was non-recovery from AKI within 7 days. The individual discriminative ability of CCL14, [TIMP-2] × [IGFBP7] and NGAL to predict renal non-recovery were evaluated by the area under receiver operating characteristics curve (AUC).

RESULTS

Of 164 AKI patients, 64 (39.0%) failed to recover from AKI onset. CCL14 showed a fair prediction ability for renal non-recovery with an AUC of 0.71 (95% CI 0.63-0.77, p < 0.001). [TIMP-2] × [IGFBP7] showed the best prediction for renal non-recovery with an AUC of 0.78 (95% CI 0.71-0.84, p < 0.001). However, NGAL had no use in predicting non-recovery with an AUC of 0.53 (95% CI 0.45-0.60, p = 0.562). A two-parameter model (non-renal SOFA score and AKI stage) predicted renal non-recovery with an AUC of 0.77 (95% CI 0.77-0.83, p = 0.004). When [TIMP-2] × [IGFBP7] was combined with the clinical factors, the AUC was significantly improved to 0.82 (95% CI 0.74-0.87, p = 0.049).

CONCLUSIONS

Urinary CCL14 and [TIMP-2] × [IGFBP7] were fair predictors of renal non-recovery from AKI. Combing urinary [TIMP-2] × [IGFBP7] with a clinical model consisting of non-renal SOFA score and AKI stage enhanced the predictive power for renal non-recovery. Urinary CCL14 showed no significant advantage in predicting renal non-recovery compared to [TIMP-2] × [IGFBP7].

Diagnosing acute kidney injury ahead of time in critically ill septic patients using kinetic estimated glomerular filtration rate

INTRODUCTION

Accurate and actionable diagnosis of Acute Kidney Injury (AKI) ahead of time is important to prevent or mitigate renal insufficiency. The purpose of this study was to evaluate the performance of Kinetic estimated Glomerular Filtration Rate (KeGFR) in timely predicting AKI in critically ill septic patients.

METHODS

We conducted a retrospective analysis on septic ICU patients who developed AKI in AmsterdamUMCdb, the first freely available European ICU database. The reference standard for AKI was the Kidney Disease: Improving Global Outcomes (KDIGO) classification based on serum creatinine and urine output (UO). Prediction of AKI was based on stages defined by KeGFR and UO. Classifications were compared by length of ICU stay (LOS), need for renal replacement therapy and 28-day mortality. Predictive performance and time between prediction and diagnosis were calculated.

RESULTS

Of 2492 patients in the cohort, 1560 (62.0%) were diagnosed with AKI by KDIGO and 1706 (68.5%) by KeGFR criteria. Disease stages had agreement of kappa = 0.77, with KeGFR sensitivity 93.2%, specificity 73.0% and accuracy 85.7%. Median time to recognition of AKI Stage 1 was 13.2 h faster for KeGFR, and 7.5 h and 5.0 h for Stages 2 and 3. Outcomes revealed a slight difference in LOS and 28-day mortality for Stage 1.

CONCLUSIONS

Predictive performance of KeGFR combined with UO criteria for diagnosing AKI is excellent. Compared to KDIGO, deterioration of renal function was identified earlier, most prominently for lower stages of AKI. This may shift the actionable window for preventing and mitigating renal insufficiency.

Renal scintigraphy to predict persistent renal failure after acute kidney injury: an observational study

INTRODUCTION

Renal scintigraphy (RS) is occasionally performed to assess the risk of persistent renal failure (PRF) in patients with acute kidney disease (AKD). However, its diagnostic performance has never been assessed.

METHODS

We identified all patients with AKD for whom RS was performed in our institution between 2010 and 2017. PRF was defined as persistently low (< 33% of baseline) estimated glomerular filtration rates (eGFR), 1 year after RS. Nuclear medicine specialists reviewed RS data and rated, for each patient, the likelihood of PRF ("PRF score"). We evaluated the performance to predict PRF (area under the ROC curve (AUC)) of RS-derived parameters such as renal accumulation index, accumulation slope, and new parameters derived from serial kidney activity counts. We tested the ability of those parameters to improve a clinical model including hypertension, diabetes, AKI severity and baseline eGFR. Finally, we conducted sensitivity analyses using alternate PRF definitions.

RESULTS

Among 97 patients included, 57 (59%) fulfilled the criteria for PRF. The PRF score was able to predict PRF with an AUC of 0.63. Similarly, the accumulation index and accumulation slope respective AUCs were 0.64 and 0.63. None of these parameters were able to improve the performance of the clinical model. Among new parameters, the 3rd/2nd minute activity ratio and 3rd/2nd minute activity slope had fair diagnostic performance (AUC 0.72 and 0.74, respectively) and improved the performance of the clinical model. Results were confirmed in sensitivity analyses.

CONCLUSION

Conventional renal scintigraphy can identify patients at high risk of PRF with a high specificity but a low sensitivity. New parameters, with comparable diagnostic abilities can be obtained within three minutes of injection.

Development and validation of clinical prediction models for acute kidney injury recovery at hospital discharge in critically ill adults

PURPOSE

Acute kidney injury (AKI) recovery prediction remains challenging. The purpose of the present study is to develop and validate prediction models for AKI recovery at hospital discharge in critically ill patients with ICU-acquired AKI stage 3 (AKI-3).

METHODS

Models were developed and validated in a development cohort (n = 229) and a matched validation cohort (n = 244) from the multicenter EPaNIC database to create prediction models with the least absolute shrinkage and selection operator (Lasso) machine-learning algorithm. We evaluated the discrimination and calibration of the models and compared their performance with plasma neutrophil gelatinase-associated lipocalin (NGAL) measured on first AKI-3 day (NGAL_AKI3) and reference model that only based on age.

RESULTS

Complete recovery and complete or partial recovery occurred in 33.20% and 51.23% of the validation cohort patients respectively. The prediction model for complete recovery based on age, need for renal replacement therapy (RRT), diagnostic group (cardiac/surgical/trauma/others), and sepsis on admission had an area under the receiver operating characteristics curve (AUROC) of 0.53. The prediction model for complete or partial recovery based on age, need for RRT, platelet count, urea, and white blood cell count had an AUROC of 0.61. NGAL_AKI3 showed AUROCs of 0.55 and 0.53 respectively. In cardiac patients, the models had higher AUROCs of 0.60 and 0.71 than NGAL_AKI3's AUROCs of 0.52 and 0.54. The developed models demonstrated a better performance over the reference models (only based on age) for cardiac surgery patients, but not for patients with sepsis and for a general ICU population.

CONCLUSION

Models to predict AKI recovery upon hospital discharge in critically ill patients with AKI-3 showed poor performance in the general ICU population, similar to the biomarker NGAL. In cardiac surgery patients, discrimination was acceptable, and better than NGAL. These findings demonstrate the difficulty of predicting non-reversible AKI early.

Predictive Value of N-Terminal Pro B-Type Natriuretic Peptide for Contrast-Induced Nephropathy Non-Recovery and Poor Outcomes Among Patients Undergoing Percutaneous Coronary Intervention

BACKGROUND

Contrast-induced nephropathy (CIN) is a frequent complication in patients undergoing percutaneous coronary intervention (PCI). The degree of recovery of renal function from CIN may affect long-term prognosis. N-terminal pro B-type natriuretic peptide (NT-proBNP) is a simple but useful biomarker for predicting CIN. However, the predictive value of preprocedural NT-proBNP for CIN non-recovery and long-term outcomes in patients undergoing PCI remains unclear.Methods and Results: This study prospectively enrolled 550 patients with CIN after PCI between January 2012 and December 2018. CIN non-recovery was defined as persistent serum creatinine >25% or 0.5 mg/dL over baseline from 1 week to 12 months after PCI in patients who developed CIN. CIN non-recovery was observed in 40 (7.3%) patients. Receiver operating characteristic analysis indicated that the best NT-proBNP cut-off value for detecting CIN non-recovery was 876.1 pg/mL (area under the curve 0.768; 95% confidence interval [CI] 0.731-0.803). After adjusting for potential confounders, multivariable analysis indicated that NT-proBNP >876.1 pg/mL was an independent predictor of CIN non-recovery (odds ratio 1.94; 95% CI 1.03-3.75; P=0.0042). Kaplan-Meier curves showed higher rates of long-term mortality among patients with CIN non-recovery than those with CIN recovery (Chi-squared=14.183, log-rank P=0.0002).

CONCLUSIONS

Preprocedural NT-proBNP was associated with CIN non-recovery among patients undergoing PCI. The optimal cut-off value for NT-proBNP to predict CIN non-recovery was 876.1 pg/mL.

Predicting renal function recovery and short-term reversibility among acute kidney injury patients in the ICU: comparison of machine learning methods and conventional regression

BACKGROUND

Acute kidney injury (AKI) is one of the most frequent complications of critical illness. We aimed to explore the predictors of renal function recovery and the short-term reversibility after AKI by comparing logistic regression with four machine learning models.

METHODS

We reviewed patients who were diagnosed with AKI in the MIMIC-IV database between 2008 and 2019. Recovery from AKI within 72 h of the initiating event was typically recognized as the short-term reversal of AKI. Conventional logistic regression and four different machine algorithms (XGBoost algorithm model, Bayesian networks [BNs], random forest [RF] model, and support vector machine [SVM] model) were used to develop and validate prediction models. The performance measures were compared through the area under the receiver operating characteristic curve (AU-ROC), calibration curves, and 10-fold cross-validation.

RESULTS

A total of 12,321 critically ill adult AKI patients were included in our analysis cohort. The renal function recovery rate after AKI was 67.9%. The maximum and minimum serum creatinine (SCr) within 24 h of AKI diagnosis, the minimum SCr within 24 and 12 h, and antibiotics usage duration were independently associated with renal function recovery after AKI. Among the 8364 recovered patients, the maximum SCr within 24 h of AKI diagnosis, the minimum Glasgow Coma Scale (GCS) score, the maximum blood urea nitrogen (BUN) within 24 h, vasopressin and vancomycin usage, and the maximum lactate within 24 h were the top six predictors for short-term reversibility of AKI. The RF model presented the best performance for predicting both renal functional recovery (AU-ROC [0.8295 ± 0.01]) and early recovery (AU-ROC [0.7683 ± 0.03]) compared with the conventional logistic regression model.

CONCLUSIONS

The maximum SCr within 24 h of AKI diagnosis was a common independent predictor of renal function recovery and the short-term reversibility of AKI. The RF machine learning algorithms showed a superior ability to predict the prognosis of AKI patients in the ICU compared with the traditional regression models. These models may prove to be clinically helpful and can assist clinicians in providing timely interventions, potentially leading to improved prognoses.

Acute Changes in Serum Creatinine and Kinetic Glomerular Filtration Rate Estimation in Early Phase of Acute Pancreatitis

In patients with acutely changing kidney function, equations used to estimate glomerular filtration rate (eGFR) must be adjusted for dynamic changes in the concentrations of filtration markers (kinetic eGFR, KeGFR). The aim of our study was to evaluate serum creatinine-based KeGFR in patients in the early phase of acute pancreatitis (AP) as a marker of changing renal function and as a predictor of AP severity. We retrospectively calculated KeGFR on day 2 and 3 of the hospital stay in a group of 147 adult patients admitted within 24 h from the onset of AP symptoms and treated in two secondary-care hospitals. In 34 (23%) patients, changes in serum creatinine during days 1-3 of the hospital stay exceeded 26.5 µmol/L; KeGFR values almost completely differentiated those with increasing and decreasing serum creatinine (area under receiver operating characteristic curve, AUROC: 0.990 on day 3). In twelve (8%) patients, renal failure was diagnosed during the first three days of the hospital stay according to the modified Marshall scoring system, which was associated with significantly lower KeGFR values. KeGFR offered good diagnostic accuracy for renal failure (area under receiver operating characteristic-AUROC: 0.942 and 0.950 on days 2 and 3). Fourteen (10%) patients developed severe AP. KeGFR enabled prediction of severe AP with moderate diagnostic accuracy (AUROC: 0.788 and 0.769 on days 2 and 3), independently of age, sex, comorbidities and study center. Lower KeGFR values were significantly associated with mortality. Significant dynamic changes in renal function are common in the early phase of AP. KeGFR may be useful in the assessment of kidney function in AP and the prediction of AP severity.

Cell cycle arrest biomarkers for predicting renal recovery from acute kidney injury: a prospective validation study

Background

Acute kidney injury (AKI) is a common disease in the intensive care unit (ICU). AKI patients with nonrecovery of renal function have a markedly increased risk of death compared with patients with recovery. The current study aimed to explore and validate the utility of urinary cell cycle arrest biomarkers for predicting nonrecovery in patients who developed AKI after ICU admission.

Methods

We prospectively and consecutively enrolled 379 critically ill patients who developed AKI after admission to the ICU, which were divided into a derivation cohort (194 AKI patients) and a validation cohort (185 AKI patients). The biomarkers of urinary tissue inhibitor of metalloproteinase-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7) were detected at inclusion immediately after AKI diagnosis (day 0) and 24 h later (day 1). The optimal cut-off values of these biomarkers for predicting nonrecovery were estimated in the derivation cohort, and their predictive accuracy was assessed in the validation cohort. The primary endpoint was nonrecovery from AKI (within 7 days).

Results

Of 379 patients, 159 (41.9%) patients failed to recover from AKI onset, with 79 in the derivation cohort and 80 in the validation cohort. Urinary [TIMP-2]*[IGFBP7] on day 0 showed a better prediction ability for nonrecovery than TIMP-2 and IGFBP7 alone, with an area under the reciever operating characteristic curve (AUC) of 0.751 [95% confidence interval (CI) 0.701–0.852, p < 0.001] and an optimal cut-off value of 1.05 ((ng/mL)²/1000). When [TIMP-2]*[IGFBP7] was combined with the clinical factors of AKI diagnosed by the urine output (UO) criteria, AKI stage 2–3 and nonrenal SOFA score for predicting nonrecovery, the AUC was significantly improved to 0.852 (95% CI 0.750–0.891, p < 0.001), which achieved a sensitivity and specificity of 88.8% (72.9, 98.7) and 92.6% (80.8, 100.0), respectively. However, urine [TIMP-2]*[IGFBP7], TIMP-2 alone, and IGFBP7 alone on day 1 performed poorly for predicting AKI recovery.

Conclusion

Urinary [TIMP-2]*[IGFBP7] on day 0 showed a fair performance for predicting nonrecovery from AKI. The predictive accuracy can be improved when urinary [TIMP-2]*[IGFBP7] is combined with the clinical factors of AKI diagnosed by the UO criteria, AKI stage 2–3 and nonrenal SOFA score.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-022-00989-8.

[Acute kidney injury in intensive care unit: A review]

Acute kidney injury is a common complication in intensive care unit. Its incidence is variable according to the studies. It is considered to occur in more than 50 % of patients. Acute kidney injury is responsible for an increase in morbidity (length of hospitalization, renal replacement therapy) but also for excess mortality. The commonly accepted definition of acute kidney injury comes from the collaborative workgroup named Kidney Disease: Improving Global Outcomes (KDIGO). It made it possible to standardize practices and raise awareness among practitioners about monitoring plasma creatinine and also diuresis. Acute kidney injury in intensive care unit is a systemic disease including circulatory, endothelial, epithelial and cellular function involvement and an acute kidney injury is not accompanied by ad integrum repair. After prolonged injury, inadequate repair begins with a fibrotic process. Several mechanisms are involved (cell cycle arrest, epithelial-mesenchymal transition, mitochondrial dysfunction) and result in improper repair. A continuum exists between acute kidney disease and chronic kidney disease, characterized by different renal recovery phenotypes. Thus, preventive measures to prevent the occurrence of kidney damage play a major role in management. The nephrologist must be involved at every stage, from the prevention of the first acute kidney injury (upon arrival in intensive care unit) to long-term follow-up and the care of a chronic kidney disease.

Biomarkers of persistent renal vulnerability after acute kidney injury recovery

Acute kidney injury (AKI) is a risk factor for new AKI episodes, chronic kidney disease, cardiovascular events and death, as renal repair may be deficient and maladaptive, and activate proinflammatory and profibrotic signals. AKI and AKI recovery definitions are based on changes in plasma creatinine, a parameter mostly associated to glomerular filtration, but largely uncoupled from renal tissue damage. The evolution of structural and functional repair has been incompletely described. We thus aimed at identifying subclinical sequelae persisting after recovery from cisplatin-induced AKI in rats. Compared to controls, after plasma creatinine recovery, post-AKI kidneys showed histological alterations and attendant susceptibility to new AKI episodes. Tubular function (assessed by the furosemide stress test, FST) also remained affected. Lingering parenchymal and functional subclinical alterations were paralleled by tapering, but abnormally high levels of urinary albumin, transferrin, insulin-like growth factor-binding protein 7 (IGFBP7), tissue inhibitor of metalloproteinases-2 (TIMP-2) and, especially, the [TIMP-2]*[IGFBP7] product. As subclinical surrogates of incomplete renal recovery, the FST and the urinary [TIMP-2]*[IGFBP7] product provide two potential diagnostic tools to monitor the sequelae and kidney vulnerability after the apparent recovery from AKI.

Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis

Acute kidney injury (AKI) is commonly present in critically ill patients with sepsis. Early prediction of short-term reversibility of AKI is beneficial to risk stratification and clinical treatment decision. The study sought to use machine learning methods to discriminate between transient and persistent sepsis-associated AKI. Septic patients who developed AKI within the first 48 h after ICU admission were identified from the Medical Information Mart for Intensive Care III database. AKI was classified as transient or persistent according to the Acute Disease Quality Initiative workgroup consensus. Five prediction models using logistic regression, random forest, support vector machine, artificial neural network and extreme gradient boosting were constructed, and their performance was evaluated by out-of-sample testing. A simplified risk prediction model was also derived based on logistic regression and features selected by machine learning algorithms. A total of 5984 septic patients with AKI were included, 3805 (63.6%) of whom developed persistent AKI. The artificial neural network and logistic regression models achieved the highest area under the receiver operating characteristic curve (AUC) among the five machine learning models (0.76, 95% confidence interval [CI] 0.74-0.78). The simplified 14-variable model showed adequate discrimination, with the AUC being 0.76 (95% CI 0.73-0.78). At the optimal cutoff of 0.63, the sensitivity and specificity of the simplified model were 63% and 76% respectively. In conclusion, a machine learning-based simplified prediction model including routine clinical variables could be used to differentiate between transient and persistent AKI in critically ill septic patients. An easy-to-use risk calculator can promote its widespread application in daily clinical practice.

Early Estimation of Renal Function After Transplantation to Enable Appropriate Dosing of Critical Drugs: Retrospective Analysis of 103 Patients in a Single Center

BACKGROUND

Immediately after renal transplantation (RTX), estimation of renal function (eGFR) is important for drug dosing and the detection of potential complications. Conventional formulas cannot be used since the serum creatinine concentration is not at steady-state. In this study, we evaluated different dynamic renal function formulas (DRFFs) to estimate eGFR immediately after RTX.

METHODS

We retrospectively included 154 RTX patients, of whom 45 had delayed graft function (DGF) and required dialysis, and 6 had unstable graft function without the need for dialysis; 103 patients had early, and thereafter stable, graft function (EGF). DRFFs were evaluated to calculate eGFR 1 day after transplantation (T1) using a new dynamic creatinine clearance calculation (D3C), two previously published formulas (Jelliffe, and the kinetic eGFR [KeGFR]), and a naive predictor (Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] at T1). The estimated DRFF-based renal functions at T1 were compared with the CKD-EPI after stabilization of renal function 3 days after transplantation (eGFR-T3), which was considered the underlying renal function immediately after RTX.

RESULTS

The D3C showed low bias (mean prediction error [MPE] - 4.5 ml/min/1.73 m²) and performed well on other outcome measures (R² = 0.82, root mean squared error [RMSE] = 11.8 ml/min/1.73 m², percentage of predictions within 30% of the reference value [p_30%] = 76%). In addition, the D3C outperformed the KeGFR (MPE 20.5 ml/min/1.73 m², R² = 0.79, RMSE = 26.9 ml/min/1.73 m², p_30% = 29%), Jelliffe (MPE - 13.3 ml/min/1.73 m², R² = 0.76, RMSE = 19.1 ml/min/1.73 m², p_30% = 53%), and the naive predictor (bias - 24.8 ml/min/1.73 m², R² = 0.60, RMSE = 30.2 ml/min/1.73 m², p_30% = 21%).

CONCLUSIONS

The newly developed D3C enables reliable assessment of renal function immediately after RTX, provides crucial information for drug dosing, and might also advance the detection of functional decline, potentially improving treatment and renal outcome.

Discovery of Novel Proteomic Biomarkers for the Prediction of Kidney Recovery from Dialysis-Dependent AKI Patients

BACKGROUND

AKI requiring dialysis (AKI-D) is associated with prolonged hospitalization, mortality, and progressive CKD among survivors. Previous studies have examined only select urine or serum biomarkers for predicting kidney recovery from AKI.

METHODS

Serum samples collected on day 8 of randomized RRT from 72 patients enrolled in the Veteran's Affairs/National Institutes of Health Acute Renal Failure Trial Network study were analyzed by the SOMAscan proteomic platform to profile 1305 proteins in each sample. Of these patients, 38 recovered kidney function and dialysis was discontinued, whereas another 34 patients remained on dialysis by day 28.

RESULTS

Differential serum levels of 119 proteins, with 53 higher and 66 lower, were detected in samples from patients who discontinued dialysis, compared with patients who remained on dialysis by day 28. Patients were classified into tertiles on the basis of SOMAscan protein measurements for the 25 proteins most differentially expressed. The association of serum levels of each protein with kidney recovery was further evaluated using logistic regression analysis. Higher serum levels of CXCL11, CXCL2/CXCL3, CD86, Wnt-7a, BTK, c-Myc, TIMP-3, CCL5, ghrelin, PDGF-C, survivin, CA2, IL-9, EGF, and neuregulin-1, and lower levels of soluble CXCL16, IL1RL1, stanniocalcin-1, IL-6, and FGF23 when classified in tertiles were significantly associated with better kidney recovery. This significant association persisted for each of these proteins after adjusting for potential confounding risk factors including age, sex, cardiovascular SOFA score, congestive heart failure, diabetes, modality of intensive dialysis treatment, cause of AKI, baseline serum creatinine, day 8 urine volume, and estimated 60-day mortality risk.

CONCLUSIONS

These results suggest concerted changes between survival-related proteins and immune-regulatory chemokines in regulating angiogenesis, endothelial and epithelial remodeling, and kidney cell regeneration, illustrating potential mechanisms of kidney recovery. Thus, this study identifies potential novel predictive biomarkers of kidney recovery in patients with AKI-D.

Nomenclature and diagnostic criteria for acute kidney injury - 2020 consensus of the Taiwan AKI-task force

Acute kidney injury (AKI) is a common syndrome that has a significant impact on prognosis in various clinical settings. To evaluate whether new evidence supports changing the current definition/classification/staging systems for AKI suggested by the Kidney Disease: Improving Global Outcomes (KDIGO) 2012 Clinical Practice Guideline, the Taiwan AKI-TASK Force, composed of 64 experts in various disciplines, systematically reviewed the literature and proposed recommendations about the current nomenclature and diagnostic criteria for AKI. The Taiwan Acute Kidney Injury (TW-AKI) Consensus 2020 was established following the principles of evidence-based medicine to investigate topics covered in AKI guidelines. The Taiwan AKI-TASK Force determined that patients with AKI have a higher risk of developing chronic kidney disease, end-stage renal disease, and death. After a comprehensive review, the TASK Force recommended using novel biomarkers, imaging examinations, renal biopsy, and body fluid assessment in the diagnosis of AKI. Clinical issues with regards to the definitions of baseline serum creatinine (sCr) level and renal recovery, as well as the use of biomarkers to predict renal recovery are also discussed in this consensus. Although the present classification systems using sCr and urine output for the diagnosis of AKI are not perfect, there is not enough evidence to change the current criteria in clinical practice. Future research should investigate and clarify the roles of the aforementioned tools in clinical practice for AKI.

In creatinine kinetics, the glomerular filtration rate always moves the serum creatinine in the opposite direction

INTRODUCTION

When the serum [creatinine] is changing, creatinine kinetics can still gauge the kidney function, and knowing the kinetic glomerular filtration rate (GFR) helps doctors take care of patients with renal failure. We wondered how the serum [creatinine] would respond if the kinetic GFR were tweaked. In every scenario, if the kinetic GFR decreased, the [creatinine] would increase, and vice versa. This opposing relationship was hypothesized to be universal.

METHODS

Serum [creatinine] and kinetic GFR, along with other parameters, are described by a differential equation. We differentiated [creatinine] with respect to kinetic GFR to test if the two variables would change oppositely of each other, throughout the gamut of all allowable clinical values. To remove the discontinuities in the derivative, limits were solved.

RESULTS

The derivative and its limits were comprehensively analyzed and proved to have a sign that is always negative, meaning that [creatinine] and kinetic GFR must indeed move in opposite directions. The derivative is bigger in absolute value at the higher end of the [creatinine] scale, where a small drop in the kinetic GFR can cause the [creatinine] to shoot upward, making acute kidney injury similar to chronic kidney disease in that regard.

CONCLUSIONS

All else being equal, a change in the kinetic GFR obligates the [creatinine] to change in the opposite direction. This does not negate the fact that an increasing [creatinine] can be compatible with a rising kinetic GFR, due to differences in how the time variable is treated.

Acute kidney injury in the critically ill: an updated review on pathophysiology and management

Acute kidney injury (AKI) is now recognized as a heterogeneous syndrome that not only affects acute morbidity and mortality, but also a patient’s long-term prognosis. In this narrative review, an update on various aspects of AKI in critically ill patients will be provided. Focus will be on prediction and early detection of AKI (e.g., the role of biomarkers to identify high-risk patients and the use of machine learning to predict AKI), aspects of pathophysiology and progress in the recognition of different phenotypes of AKI, as well as an update on nephrotoxicity and organ cross-talk. In addition, prevention of AKI (focusing on fluid management, kidney perfusion pressure, and the choice of vasopressor) and supportive treatment of AKI is discussed. Finally, post-AKI risk of long-term sequelae including incident or progression of chronic kidney disease, cardiovascular events and mortality, will be addressed.

Plasma Endogenous Sulfur Dioxide: A Novel Biomarker to Predict Acute Kidney Injury in Critically Ill Patients

Purpose

Sulfur dioxide (SO₂) is a novel gaseous signaling molecule that plays an important role in inflammation, which contributes the pathogenesis of acute kidney injury (AKI). The aim of this study was to explore the predictive value of plasma SO₂ for AKI in high-risk patients.

Patients and Methods

A prospective cohort of 167 patients who underwent major noncardiac surgery was enrolled in the study. Plasma SO₂, urine neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitor of metalloproteinase-2 (TIMP-2), and insulin-like growth factor-binding protein 7 (IGFBP7) levels were detected immediately after the operation. The primary endpoint was new-onset AKI within 72 h after admission. The ability of biomarkers including SO₂ and a clinical risk model to predict AKI was compared by receiver operator characteristic (ROC) curve analysis and decision curve analysis (DCA), additional contributions were evaluated by integrated discrimination improvement (IDI) and net reclassification improvement (NRI) analyses.

Results

A total of 61 (36.5%) patients developed AKI within 72 h of surgery. Compared to NGAL and [TIMP-2]·[IGFBP7], SO₂ showed better predictive ability for new-onset AKI with an area under the ROC curve of 0.771 (95% confidence interval: 0.700–0.832, p<0.001). The improvement in predictive value by including SO₂ in the clinical risk model was supported by NRI (0.28; P=0.04) and IDI (0.15; P<0.001) analyses. The net benefit of the combination of SO₂ and clinical variables was the max in DCA.

Conclusion

Plasma SO₂ shows a useful value for predicting new-onset AKI, and improved AKI prediction based on clinical variables, which can guide the implementation of preventive measures for high-risk patients.

Measured and Estimated Glomerular Filtration Rate in the ICU: A Prospective Study

OBJECTIVES

To compare estimated glomerular filtration rate using classical static and kinetic equations with measured glomerular filtration rate assessed by plasma iohexol clearance in a mixed population of critical care patients.

PATIENTS

Unselected patients older than 18 and admitted to a general ICU.

DESIGN

Interventional prospective single center study.

INTERVENTION

Measurement of glomerular filtration rate by the plasma clearance of an IV single dose of iohexol and estimation of glomerular filtration rate with creatinine or cystatin C-based standard and kinetic equations as well as urinary creatinine clearance.

MEASUREMENTS AND MAIN RESULTS

Sixty-three patients were included with a median age of 66 years old. The median measured glomerular filtration rate was 51 mL/min/1.73 m (interquartile range, 19-85 mL/min/1.73 m). All used equations displayed significant biases, high errors, and poor accuracy when compared with measured glomerular filtration rate, overestimating renal function. The highest accuracy and lowest error were observed with cystatin C-based chronic kidney disease epidemiology collaboration equations. Both modification of diet in renal disease and Cockcroft-Gault equations displayed the lowest performance. Kinetic models did not improve performances, except in patients with unstable creatinine levels. Creatinine- but not cystatin C-based estimations largely derived over ICU stay, which appeared more related to sarcopenia than fluid balance. Finally, estimated glomerular filtration rate misclassified patients according to classical glomerular filtration rate categories in approximately half of the studied cases.

CONCLUSIONS

All known estimated glomerular filtration rate equations displayed high biases and unacceptable errors when compared with measured glomerular filtration rate in a mixed ICU population, with the lowest performance related to creatinine-based equations compared with cystatin C. In the ICU, we advocate for caution when using creatinine based estimated glomerular filtration rate equations. Drifting of serum creatinine levels over time should also be taken into consideration when assessing renal function in the ICU.

Usefulness of the Biomarker TIMP-2•IGFBP7 for Acute Kidney Injury Assessment in Critically Ill Patients: A Narrative Review

OBJECTIVES

To review the clinical usefulness of the biomarker TIMP-2•IGFBP7 in adult, general medical-surgical intensive care unit (ICU) settings.

DATA SOURCES

PubMed (1946 to mid-February 2021) and EMBASE (1947 to mid-February 2021) with bibliographies of retrieved articles reviewed for additional articles.

STUDY SELECTION AND DATA EXTRACTION

Studies evaluating use of the urinary TIMP-2•IGFBP7 assay in adult patients in ICU settings.

DATA SYNTHESIS

Studies published after investigations leading to TIMP-2•IGFBP7 assay approval confirm the appropriateness of considerations discussed in product labeling, such as use of the test within 12 hours of assessment, use of a dichotomous 0.3 (ng/mL)²/1000 cutoff, and use only in combination with other assessments of acute kidney injury (AKI). However, as a biomarker routinely used for early identification of patients at risk for AKI in mixed ICU populations, the additional resources required for TIMP-2•IGFBP monitoring are difficult to justify because of limited data demonstrating usefulness in preventing or ameliorating AKI and its attendant complications.

RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE

Biomarkers are potentially useful not only for assessment and diagnosis of AKI, but also for practitioners involved in the management of nephrotoxic medications and medications needing adjustment for decreased kidney function.

CONCLUSIONS

Although there is evidence to suggest that the urinary TIMP-2•IGFBP7 biomarker is helpful in predicting AKI progression in general medical-surgical ICU patients when used within 12 hours of patient assessment in combination with routine testing, including serum creatinine and urine output, there is little evidence that its use leads to improvements in clinically important patient outcomes.

Acute kidney injury in renal transplant recipients undergoing cardiac surgery

BACKGROUND

Acute kidney injury (AKI) is a key risk factor for chronic kidney disease in the general population, but has not been investigated in detail among renal transplant recipients (RTRs). We investigated the incidence, severity and risk factors for AKI following cardiac surgery among RTRs compared with non-RTRs with otherwise similar clinical characteristics.

METHODS

We conducted a retrospective cohort study of RTRs (n = 83) and non-RTRs (n = 83) who underwent cardiac surgery at two major academic medical centers. Non-RTRs were matched 1:1 to RTRs by age, preoperative (preop) estimated glomerular filtration rate and type of cardiac surgery. We defined AKI according to Kidney Disease: Improving Global Outcomes criteria.

RESULTS

RTRs had a higher rate of AKI following cardiac surgery compared with non-RTRs [46% versus 28%; adjusted odds ratio 2.77 (95% confidence interval 1.36-5.64)]. Among RTRs, deceased donor (DD) versus living donor (LD) status, as well as higher versus lower preop calcineurin inhibitor (CNI) trough levels, were associated with higher rates of AKI (57% versus 33% among DD-RTRs versus LD-RTRs; P = 0.047; 73% versus 36% among RTRs with higher versus lower CNI trough levels, P = 0.02). The combination of both risk factors (DD status and higher CNI trough level) had an additive effect (88% AKI incidence among patients with both risk factors versus 25% incidence among RTRs with neither risk factor, P = 0.004).

CONCLUSIONS

RTRs have a higher risk of AKI following cardiac surgery compared with non-RTRs with otherwise similar characteristics. Among RTRs, DD-RTRs and those with higher preop CNI trough levels are at the highest risk.

Utility of Kinetic GFR for Predicting Severe Persistent AKI in Critically Ill Children and Young Adults

Kinetic eGFR can be part of a multidimensional approach for AKI prediction combined with biomarkers, fluid corrected creatinine, and renal angina.Kinetic eGFR on day 1 is not independently associated with severe day-3 AKI in children and young adults who are critically ill.

Determinants of Outcomes of Acute Kidney Injury: Clinical Predictors and Beyond

Acute kidney injury (AKI) is a common clinical syndrome characterized by rapid impairment of kidney function. The incidence of AKI and its severe form AKI requiring dialysis (AKI-D) has been increasing over the years. AKI etiology may be multifactorial and is substantially associated with increased morbidity and mortality. The outcome of AKI-D can vary from partial or complete recovery to transitioning to chronic kidney disease, end stage kidney disease, or even death. Predicting outcomes of patients with AKI is crucial as it may allow clinicians to guide policy regarding adequate management of this problem and offer the best long-term options to their patients in advance. In this manuscript, we will review the current evidence regarding the determinants of AKI outcomes, focusing on AKI-D.

Risk factors and prognosis assessment for acute kidney injury: The 2020 consensus of the Taiwan AKI Task Force

Risk and prognostic factors for acute kidney injury (AKI) have been published in various studies across various populations. We aimed to explore recent advancements in and provide updated recommendations on AKI risk stratification and information about local AKI risk factors. The Taiwan Acute Kidney Injury Task Force reviewed relevant recently published literature and reached a consensus after group meetings. Systemic review and group discussion were performed. We conducted a meta-analysis according to the PRISMA statement for evaluating the diagnostic performance of the furosemide stress test. Several risk and susceptibility factors were identified through literature review. Contrast-associated AKI prediction models after coronary angiography were one of the most discussed prediction models we found. The basic approach and evaluation of patients with AKI was also discussed. Our meta-analysis found that the furosemide stress test can be used as a prognostic tool for AKI progression and to identify patients with AKI who are at low risk of renal replacement therapy. Factors associated with de novo chronic kidney injury or renal non-recovery after AKI were identified and summarized. Our review provided practical information about early identification of patients at high risk of AKI or disease progression for Taiwan local clinics.

Estimating glomerular filtration rate in patients with acute kidney injury: a prospective multicenter study of diagnostic accuracy

BACKGROUND

Estimating glomerular filtration rate (GFR) in acute kidney injury (AKI) is challenging, with limited data comparing estimated and gold standard methods to assess GFR. The objective of our study was to assess the performance of the kinetic estimated GFR (KeGFR) and Jelliffe equations to estimate GFR in AKI, using a radioisotopic method (technetium-diethylenetriaminepentaacetic acid) as a reference measure.

METHODS

We conducted a prospective multicenter observational study in hospitalized patients with AKI. We computed the Jelliffe and KeGFR equations to estimate GFR and compared these estimations to measured GFR (mGFR) by a radioisotopic method. The performances were assessed by correlation, Bland-Altman plots and smoothed and linear regressions. We conducted stratified analyses by age and chronic kidney disease (CKD).

RESULTS

The study included 119 patients with AKI, mostly from the intensive care unit (63%) and with Stage 1 AKI (71%). The eGFR obtained from the Jelliffe and KeGFR equations showed a good correlation with mGFR (r = 0.73 and 0.68, respectively). The median eGFR by the Jelliffe and KeGFR equations was less than the median mGFR, indicating that these equations underestimated the mGFR. On Bland-Altman plots, the Jelliffe and KeGFR equations displayed a considerable lack of agreement with mGFR, with limits of agreement >40 mL/min/1.73 m2. Both equations performed better in CKD and the KeGFR performed better in older patients. Results were similar across AKI stages.

CONCLUSIONS

In our study, the Jelliffe and KeGFR equations had good correlations with mGFR; however, they had wide limits of agreement. Further studies are needed to optimize the prediction of mGFR with estimatation equations.

Long-term consequences of acute kidney injury: a narrative review

The incidence of acute kidney injury (AKI) has increased in the past decades. AKI complicates up to 15% of hospitalizations and can reach up to 50-60% in critically ill patients. Besides the short-term impact of AKI in patient outcomes, several studies report the association between AKI and adverse long-term outcomes, such as recurrent AKI episodes in 25-30% of cases, hospital re-admissions in up to 40% of patients, an increased risk of cardiovascular events, an increased risk of progression of chronic kidney disease (CKD) after AKI and a significantly increased long-term mortality. Despite the long-term impact of AKI, there are neither established guidelines on the follow-up care of AKI patients, nor treatment strategies to reduce the incidence of sequelae after AKI. Only a minority of patients have been referred to nephrology post-discharge care, despite the evidence of improved outcomes associated with nephrology referral by addressing cardiovascular risk and risk of progression to CKD. Indeed, AKI survivors should have specialized nephrology follow-up to assess kidney function after AKI, perform medication reconciliation, educate patients on nephrotoxic avoidance and implement strategies to prevent CKD progression. The authors provide a comprehensive review of the transition from AKI to CKD, analyse the current evidence on the long-term outcomes of AKI and describe predisposing risk factors, highlight the importance of follow-up care in these patients and describe the current therapeutic strategies which are being investigated on their impact in improving patient outcomes.

AKI Treated with Renal Replacement Therapy in Critically Ill Patients with COVID-19

BACKGROUND

AKI is a common sequela of coronavirus disease 2019 (COVID-19). However, few studies have focused on AKI treated with RRT (AKI-RRT).

METHODS

We conducted a multicenter cohort study of 3099 critically ill adults with COVID-19 admitted to intensive care units (ICUs) at 67 hospitals across the United States. We used multivariable logistic regression to identify patient-and hospital-level risk factors for AKI-RRT and to examine risk factors for 28-day mortality among such patients.

RESULTS

A total of 637 of 3099 patients (20.6%) developed AKI-RRT within 14 days of ICU admission, 350 of whom (54.9%) died within 28 days of ICU admission. Patient-level risk factors for AKI-RRT included CKD, men, non-White race, hypertension, diabetes mellitus, higher body mass index, higher d-dimer, and greater severity of hypoxemia on ICU admission. Predictors of 28-day mortality in patients with AKI-RRT were older age, severe oliguria, and admission to a hospital with fewer ICU beds or one with greater regional density of COVID-19. At the end of a median follow-up of 17 days (range, 1-123 days), 403 of the 637 patients (63.3%) with AKI-RRT had died, 216 (33.9%) were discharged, and 18 (2.8%) remained hospitalized. Of the 216 patients discharged, 73 (33.8%) remained RRT dependent at discharge, and 39 (18.1%) remained RRT dependent 60 days after ICU admission.

CONCLUSIONS

AKI-RRT is common among critically ill patients with COVID-19 and is associated with a hospital mortality rate of >60%. Among those who survive to discharge, one in three still depends on RRT at discharge, and one in six remains RRT dependent 60 days after ICU admission.

Kinetic GFR Outperforms CKD-EPI for Slow Graft Function Prediction in the Immediate Postoperative Period Following Kidney Transplantation

Background: Rapid identification of patients at high risk for slow graft function (SGF) is of major importance in the immediate period following renal graft transplantation, both for early therapeutic decisions and long-term prognosis. Due to the high variability of serum creatinine levels after surgery, glomerular filtration rate (GFR) estimation is challenging. In this situation, kinetic estimated GFR (KeGFR) equations are interesting tools but have never been assessed for the identification of SGF patients. Methods: We conducted a single-center retrospective cohort study, including all consecutive kidney allograft recipients in the University Hospitals of Geneva from 2008 to 2016. GFR was estimated using both CKD-EPI and KeGFR formulae. Their accuracies for SGF prediction were compared. Patients were followed up for one year after transplantation. Results: A total of 326 kidney recipients were analyzed. SGF occurred in 76 (23%) patients. KeGFR estimation stabilized from the day following kidney transplantation, more rapidly than CKD-EPI. Discrimination ability for SGF prediction was better for KeGFR than CKD-EPI (AUC 0.82 and 0.66, p < 0.001, respectively). Conclusion: KeGFR computed from the first day after renal transplantation was able to predict SGF with good discrimination, outperforming CKD-EPI estimation. SGF patients had lower renal graft function overall at the one-year follow up.

Kinetic Estimated Glomerular Filtration Rate and Severity of Acute Kidney Injury in Critically Ill Children

OBJECTIVE

To study the Kinetic estimated Glomerular Filtration Rate (KeGFR) using serum creatinine (SCr) for the identification of acute kidney injury (AKI), stages of AKI, and extent of agreement with Kidney Disease Improving Global Outcomes (KDIGO) classification in critically ill children.

METHODS

A prospective observational study was conducted in a pediatric intensive care unit (PICU) in a tertiary care institute of South India from July through August 2018. Sixty children were enrolled. The patients with known End-Stage Renal Disease (ESRD), with previous renal transplantation, admission SCr more than 4 mg per dL, expired within 24 h of admission and patients who underwent Renal Replacement Therapy (RRT) before PICU admission were excluded. KeGFR was calculated for the first seven days, and the worst achieved value was determined. AKI staging by KDIGO was compared with AKI by KeGFR value. The requirement of RRT, multi-organ dysfunction syndrome (MODS), mechanical ventilation, cumulative fluid balance, PICU stay, and hospital mortality was recorded.

RESULTS

AKI detection by KeGFR method showed a sensitivity of 93% (95% CI 80% - 98.2%) and specificity of 76% (95% CI 49.8% - 92.2%) compared to KDIGO criteria. The good agreement between KDIGO and KeGFR values for AKI was noted (Kappa = 0.71, p < 0.001). It was observed that 81.3% (n = 13) of Group-I, 56% (n = 14) of Group-II, 77.8% (n = 7) of Group-III and 90% (n = 9) of Group-IV by KeGFR were graded as Stage-0, Stage-1, Stage-2 and Stage-3 of AKI by KDIGO criteria respectively (p < 0.001). There was no significant difference noted in secondary outcomes. The survival of children with AKI and those without AKI (by both KDIGO staging and KeGFR) showed no significant difference.

CONCLUSIONS

KeGFR is highly sensitive, and there is a good agreement with KDIGO criteria in the identification of AKI in critically ill children. Further research is required to validate these study results.

Clinical adoption of Nephrocheck® in the early detection of acute kidney injury

Acute kidney injury is a common complication of acute illnesses and is associated with increased morbidity and mortality. Over the past years several acute kidney injury biomarkers for diagnostication, decision-making processes, and prognosis of acute kidney injury and its outcomes have been developed and validated. Among these biomarkers, tissue inhibitor of metalloproteinase-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7), the so-called cell cycle arrest biomarkers, showed a superior profile of accuracy and stability even in patients with substantial comorbidities. Therefore, in 2014, the US Food and Drug Administration approved the use of the product of TIMP-2 and IGFBP7 ([TIMP-2] × [IGFBP7]), known as cell cycle arrest biomarkers, to aid critical care physicians and nephrologists in the early prediction of acute kidney injury in the critical care setting. To date, Nephrocheck® is the only commercially available test for [TIMP-2] × [IGFBP7]. In this narrative review, we describe the growing clinical and investigational momentum of biomarkers, focusing on [TIMP-2] × [IGFBP7], as one of the most promising candidate biomarkers. Additionally, we review the current state of clinical implementation of Nephrocheck®.

Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients

Background

The prevalence of acute kidney injury (AKI) in COVID-19 patients is high, with poor prognosis. Early identification of COVID-19 patients who are at risk for AKI and may develop critical illness and death is of great importance.

Objective

The aim of this study was to develop and validate a prognostic model of AKI and in-hospital death in patients with COVID-19, incorporating the new tubular injury biomarker urinary neutrophil gelatinase-associated lipocalin (u-NGAL) and artificial intelligence (AI)-based chest computed tomography (CT) analysis.

Methods

A single-center cohort of patients with COVID-19 from Wuhan Leishenshan Hospital were included in this study. Demographic characteristics, laboratory findings, and AI-assisted chest CT imaging variables identified on hospital admission were screened using least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a model for predicting the AKI risk. The accuracy of the AKI prediction model was measured using the concordance index (C-index), and the internal validity of the model was assessed by bootstrap resampling. A multivariate Cox regression model and Kaplan-Meier curves were analyzed for survival analysis in COVID-19 patients.

Results

One hundred seventy-four patients were included. The median (±SD) age of the patients was 63.59 ± 13.79 years, and 83 (47.7%) were men.u-NGAL, serum creatinine, serum uric acid, and CT ground-glass opacity (GGO) volume were independent predictors of AKI, and all were selected in the nomogram. The prediction model was validated by internal bootstrapping resampling, showing results similar to those obtained from the original samples (i.e., 0.958; 95% CI 0.9097–0.9864). The C-index for predicting AKI was 0.955 (95% CI 0.916–0.995). Multivariate Cox proportional hazards regression confirmed that a high u-NGAL level, an increased GGO volume, and lymphopenia are strong predictors of a poor prognosis and a high risk of in-hospital death.

Conclusions

This model provides a useful individualized risk estimate of AKI in patients with COVID-19. Measurement of u-NGAL and AI-based chest CT quantification are worthy of application and may help clinicians to identify patients with a poor prognosis in COVID-19 at an early stage.

Estimating Creatinine Clearance in the Nonsteady State: The Determination and Role of the True Average Creatinine Concentration

Creatinine clearance is a tenet of nephrology practice. However, with just a single creatinine concentration included in the denominator of the creatinine clearance equation, the resulting value seems to apply only in the steady state. Does the basic clearance formula work in the nonsteady state, and can it recapitulate the kinetic glomerular filtration rate (GFR) equation? In the kinetic state, a nonlinear creatinine trajectory is reducible into a “true average” value that can be found using calculus, proceeding from a differential equation based on the mass balance principle. Using the fundamental theorem of calculus, we prove definitively that the true average is the correct creatinine to divide by, even as the mathematical model accommodates clinical complexities such as volume change and other factors that affect creatinine kinetics. The true average of a creatinine versus time function between 2 measured creatinine values is found by a definite integral. To use the true average to compute kinetic GFR, 2 techniques are demonstrated, a graphical one and a numerical one. We apply this concept to a clinical case of an individual with acute kidney injury requiring dialysis; despite the effects of hemodialysis on serum creatinine concentration, kinetic GFR was able to track the underlying kidney function and provided critical information regarding kidney function recovery. Finally, a prior concept of the maximum increase in creatinine per day is made more clinically objective. Thus, the clearance paradigm applies to the nonsteady state as well when the true average creatinine is used, providing a fundamentally valid strategy to deduce kinetic GFRs from serum creatinine trends occurring in real-life acute kidney injury and kidney recovery.

Urine cell cycle arrest biomarkers distinguish poorly between transient and persistent AKI in early septic shock: a prospective, multicenter study

Background

The urine biomarkers tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7) have been validated for predicting and stratifying AKI. In this study, we analyzed the utility of these biomarkers for distinguishing between transient and persistent AKI in the early phase of septic shock.

Methods

We performed a prospective, multicenter study in 11 French ICUs. Patients presenting septic shock, with the development of AKI within the first 6 h, were included. Urine [TIMP-2]*[IGFBP7] was determined at inclusion (0 h), 6 h, 12 h, and 24 h. AKI was considered transient if it resolved within 3 days. Discriminative power was evaluated by receiver operating characteristic (ROC) curve analysis.

Results

We included 184 patients, within a median [IQR] time of 1.0 [0.0–3.0] h after norepinephrine (NE) initiation; 100 (54%) patients presented transient and 84 (46%) presented persistent AKI. Median [IQR] baseline urine [TIMP-2]*[IGFBP7] was higher in the persistent AKI group (2.21 [0.81–4.90] (ng/ml)²/1000) than in the transient AKI group (0.75 [0.20–2.12] (ng/ml)²/1000; p < 0.001). Baseline urine [TIMP-2]*[IGFBP7] was poorly discriminant, with an AUROC [95% CI] of 0.67 [0.59–0.73]. The clinical prediction model combining baseline serum creatinine concentration, baseline urine output, baseline NE dose, and baseline extrarenal SOFA performed well for the prediction of persistent AKI, with an AUROC [95% CI] of 0.81 [0.74–0.86]. The addition of urine [TIMP-2]*[IGFBP7] to this model did not improve the predictive performance.

Conclusions

Urine [TIMP-2]*[IGFBP7] measurements in the early phase of septic shock discriminate poorly between transient and persistent AKI and do not improve clinical prediction over that achieved with the usual variables.

Trial registration

NCT02812784

Early Recognition of Persistent Acute Kidney Injury

Despite the vast amount of literature dedicated to acute kidney injury (AKI) and its clinical consequences, short-term renal recovery has been relatively neglected. Recent studies have suggested that timing of renal recovery is associated with longer-term risk of death, residual renal function, and end-stage renal failure risk. In addition, longer AKI duration is associated with an increased requirement for renal replacement therapy. Comorbidities, especially renal and cardiovascular, severity of AKI, criteria to reach AKI diagnosis, as well as severity of critical illness have been associated with longer AKI duration, and, more specifically, risk of persistent renal dysfunction. Because predicting short-term renal recovery is clinically relevant, several tests, imaging, and biomarkers have been tested in a way to predict the course of AKI and chances for early renal recovery. In this review, the definition of recovery, consequences of persistent AKI, and tools proposed to predict recovery are described. The performance of these tools and their limits are discussed.

Assessing the NephroCheck® Test System in Predicting the Risk of Death or Dialysis in Burn Patients

Acute kidney injury (AKI) is associated with high mortality in burn patients. Urinary biomarkers can aid in the prediction of AKI and its consequences, such as death and the need for renal replacement therapy (RRT). The purpose of this study was to investigate a novel methodology for detecting urinary biomarkers, the NephroCheck® Test System, and assess its ability to predict death or the need for RRT in burn patients. Burn patients admitted to the United States Army Institute of Surgical Research (USAISR) burn intensive care unit were prospectively enrolled between March 2016 and April 2018. A urine sample was obtained from all study participants using the NephroCheck® system. Patient and injury characteristics were gathered, and descriptive statistics were calculated and multivariable logistic regression analyses were performed using these data. Of the 69 patients in this study, 15 patients (21.7%) attained the composite outcome of death or needing RRT within 30 days of urine collection. NephroCheck® scores were higher for patients with the composite outcome, with P = 0.06 for centrifuged scores and P = 0.04 for noncentrifuged scores. Centrifuged and noncentrifuged scores were in high agreement and correlation (R2 = 0.97, P < 0.0001). Noncentrifuged scores were significant in the unadjusted analysis, but they were not significant in the adjusted analysis. Although these scores had a lower sensitivity and negative predictive value compared with other parameters, they had the second highest specificity and positive predictive value. NephroCheck® scores were higher in burn patients with the composite outcome of death or needing RRT, and they demonstrated comparable sensitivity and specificity to creatinine and TBSA.

Reversibility of Acute Kidney Injury in Medical ICU Patients: Predictability Performance of Urinary Tissue Inhibitor of Metalloproteinase-2 x Insulin-Like Growth Factor-Binding Protein 7 and Renal Resistive Index

OBJECTIVES

Urinary biomarkers and renal Doppler sonography remain considered as promising tools to distinguish transient from persistent acute kidney injury. The performance of the urinary biomarker, tissue inhibitor of metalloproteinase-2 x insulin-like growth factor-binding protein 7 and of renal resistive index to predict persistent acute kidney injury showed contradictory results. Our aim was to evaluate the performance of tissue inhibitor of metalloproteinase-2 x insulin-like growth factor-binding protein 7 and renal resistive index in predicting reversibility of acute kidney injury in critically ill patients.

DESIGN

Prospective observational study.

SETTING

Twenty-bed medical ICU in an university hospital.

PATIENTS

Consecutive patients with acute kidney injury.

INTERVENTION

None.

MEASUREMENTS AND MAIN RESULTS

Renal resistive index was measured within 12 hours after admission, and urinary tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 was measured at H0, H6, H12, and H24. Renal dysfunction reversibility was evaluated at day 3. Receiver operating characteristic curves were plotted to evaluate diagnostic performance of renal resistive index and tissue inhibitor of metalloproteinase-2 x insulin-like growth factor-binding protein 7 to predict a persistent acute kidney injury. Overall, 100 patients were included in whom 50 with persistent acute kidney injury. Renal resistive index was higher in persistent acute kidney injury group. Urinary tissue inhibitor of metalloproteinase-2 x insulin-like growth factor-binding protein 7 was not significantly different at each time between both groups. The performance of tissue inhibitor of metalloproteinase-2 x insulin-like growth factor-binding protein 7 was poor with respectively an area under the receiver operating characteristic curves of 0.57 (95% CI, 0.45-0.68), 0.58 (95% CI, 0.47-0.69), 0.61 (95% CI, 0.50-0.72), and 0.57 (95% CI, 0.46-0.68) at H0, H6, H12, and H24. The area under the receiver operating characteristic curve for renal resistive index was 0.93 (95% CI, 0.89-0.98). A renal resistive index greater than or equal to 0.685 predicting persistent acute kidney injury with 78% (95% CI, 64-88%) sensitivity and 90% (95% CI, 78-97%) specificity.

CONCLUSIONS

Renal resistive index had a good performance for predicting the reversibility of acute kidney injury in critically ill patients. Urinary tissue inhibitor of metalloproteinase-2 x insulin-like growth factor-binding protein 7 was unable to differentiate transient from persistent acute kidney injury.