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

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.

Acute kidney injury in critical COVID-19 patients: usefulness of urinary biomarkers and kidney proximal tubulopathy

Tubular injury is the main cause of acute kidney injury (AKI) in critically ill COVID-19 patients. Proximal tubular dysfunction (PTD) and changes in urinary biomarkers, such as NGAL, TIMP-2, and IGFBP7 product ([TIMP-2]•[IGFBP7]), could precede AKI. We conducted a prospective cohort study from 2020/03/09 to 2020/05/03, which consecutively included all COVID-19 patients who had at least one urinalysis, to assess the incidence of PTD and AKI, and the effectiveness of PTD, NGAL, and [TIMP-2]•[IGFBP7] in AKI and persistent AKI prediction using the area under the receiver operating characteristic curves (AUCs), Kaplan-Meier methodology (log-rank tests), and Cox models. Among the 60 patients admitted to the ICU with proven COVID-19 (median age: 63-year-old (interquartile range: IQR, 55-74), 45 males (75%), median simplified acute physiology score (SAPS) II: 34 (IQR, 22-47) and median BMI: 25.7 kg/m2 (IQR, 23.3-30.8)) analyzed, PTD was diagnosed in 29 patients (48%), AKI in 33 (55%) and persistent AKI in 20 (33%). Urinary NGAL had the highest AUC for AKI prediction: 0.635 (95%CI: 0.491-0.779) and persistent AKI prediction: 0.681 (95%CI: 0.535-0.826), as compared to PTD and [TIMP-2]•[IGFBP7] (AUCs <0.6). AKI was independently associated with higher SAPSII (HR = 1.04, 95%CI: 1.01-1.06, p = 0.005) and BMI (HR = 1.07, 95%CI: 1.00-1.14, p = 0.04) and persistent AKI with higher SAPSII (HR = 1.03, 95%CI: 1.00-1.06, p = 0.048) and nephrotoxic drug use (HR = 3.88, 95%CI: 1.20-12.5, p = 0.02). In conclusion, in critically ill COVID-19 patients, the incidence of PTD and AKI was relatively high. NGAL was the best urinary biomarker for predicting AKI, but only clinical severity was independently associated with its occurrence.

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.

Influence of arterial blood gases on the renal arterial resistive index in intensive care unit

BACKGROUND

Renal artery Doppler sonography with resistive index (RI) determination is a noninvasive, fast, and reliable diagnostic tool increasingly used in the intensive care unit (ICU) to predict and assess the reversibility of acute kidney injury (AKI). However, interpreting the RI can be challenging due to numerous influencing factors. While some studies have explored various confounding factors, arterial blood gases have received limited attention. Therefore, our study aims to evaluate the impact of arterial blood gases on the RI in the ICU setting.

METHODS

This prospective observational study enrolled ICU patients who required blood gas analysis and had not experienced significant hemodynamic changes recently. The RI was measured using standardized Doppler ultrasound within an hour of the arterial blood gases sampling and analysis.

RESULTS

A total of sixty-four patients were included in the analysis. Univariate analysis revealed a correlation between the RI and several variables, including PaCO2 (R = 0.270, p = 0.03), age (R = 0.574, p < 0.0001), diastolic arterial pressure (DAP) (R = - 0.368, p = 0.0028), and SaO2 (R = - 0.284, p = 0.0231). Multivariate analysis confirmed that age > 58 years and PaCO2 were significant factors influencing the RI, with respective odds ratios of 18.67 (p = 0.0003) and 1.132 (p = 0.0267).

CONCLUSION

The interpretation of renal arterial RI should take into account thresholds for PaCO2, age, and diastolic arterial pressure. Further studies are needed to develop a comprehensive scoring system that incorporates all these cofactors for a reliable analysis of RI levels. Trial registration This observational study, registered under number 70-0914, received approval from local Ethical Committee of Toulouse University Hospital.

Auto- and paracrine rewiring of NIX-mediated mitophagy by insulin-like growth factor-binding protein 7 in septic AKI escalates inflammation-coupling tubular damage

AIMS

Inflammation-coupling tubular damage (ICTD) contributes to pathogenesis of septic acute kidney injury (AKI), in which insulin-like growth factor-binding protein 7 (IGFBP-7) serves as a biomarker for risk stratification. The current study aims to discern how IGFBP-7 signalling influences ICTD, the mechanisms that underlie this process and whether blockade of the IGFBP-7-dependent ICTD might have therapeutic value for septic AKI.

MATERIALS AND METHODS

In vivo characterization was carried out in B6/JGpt-Igfbp7^em1Cd1165/Gpt mice subjected to cecal ligation and puncture (CLP). Transmission electron microscopy, immunofluorescence, flow cytometry, immunoblotting, ELISA, RT-qPCR and dual-luciferase reporter assays were used to determine mitochondrial functions, cell apoptosis, cytokine secretion and gene transcription.

KEY FINDINGS

ICTD augments the transcriptional activity and protein secretion of tubular IGFBP-7, which enables an auto- and paracrine signalling via deactivation of IGF-1 receptor (IGF-1R). Genetic knockout (KO) of IGFBP-7 provides renal protection, improves survival and resolves inflammation in murine models of cecal ligation and puncture (CLP), while administering recombinant IGFBP-7 aggravates ICTD and inflammatory invasion. IGFBP-7 perpetuates ICTD in a NIX/BNIP3-indispensable fashion through dampening mitophagy that restricts redox robustness and preserves mitochondrial clearance programs. Adeno-associated viral vector 9 (AAV9)-NIX short hairpin RNA (shRNA) delivery ameliorates the anti-septic AKI phenotypes of IGFBP-7 KO. Activation of BNIP3-mediated mitophagy by mitochonic acid-5 (MA-5) effectively attenuates the IGFBP-7-dependent ICTD and septic AKI in CLP mice.

SIGNIFICANCE

Our findings identify IGFBP-7 is an auto- and paracrine manipulator of NIX-mediated mitophagy for ICTD escalation and propose that targeting the IGFBP-7-dependent ICTD represents a novel therapeutic strategy against septic AKI.

Acute Kidney Injury: Advances in Clinical Management

Acute kidney injury (AKI), closely related to increased mortality, involved 15-20% of hospitalized patients with higher incidence, with about 50% in the intensive care unit (ICU) [...].

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.

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.

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.

Renal resistive index is associated with acute kidney injury in COVID-19 patients treated in the intensive care unit

Background

Renal resistive index (RRI) is a promising tool for the assessment of acute kidney injury (AKI) in critically ill patients in general, but its role and association to AKI among patients with Coronavirus disease 2019 (COVID-19) is not known.

Objective

The aim of this study was to describe the pattern of RRI in relation to AKI in patients with COVID-19 treated in the intensive care unit.

Methods

In this observational cohort study, RRI was measured in COVID-19 patients in six intensive care units at two sites of a Swedish University Hospital. AKI was defined by the creatinine criteria in the Kidney Disease Improving Global Outcomes classification. We investigated the association between RRI and AKI diagnosis, different AKI stages and urine output.

Results

RRI was measured in 51 patients, of which 23 patients (45%) had AKI at the time of measurement. Median RRI in patients with AKI was 0.80 (IQR 0.71–0.85) compared to 0.72 (IQR 0.67–0.78) in patients without AKI (p = 0.004). Compared to patients without AKI, RRI was higher in patients with AKI stage 3 (median 0.83, IQR 0.71–0.85, p = 0.006) but not in patients with AKI stage 1 (median 0.76, IQR 0.71–0.83, p = 0.347) or AKI stage 2 (median 0.79, min/max 0.79/0.80, n = 2, p = 0.134). RRI was higher in patients with an ongoing AKI episode compared to patients who never developed AKI (median 0.72, IQR 0.69–0.78, p = 0.015) or patients who developed AKI but had recovered at the time of measurement (median 0.68, IQR 0.67–0.81, p = 0.021). Oliguric patients had higher RRI (median 0.84, IQR 0.83–0.85) compared to non-oliguric patients (median 0.74, IQR 0.69–0.81) (p = 0.009). After multivariable adjustment, RRI was independently associated with AKI (OR for 0.01 increments of RRI 1.22, 95% CI 1.07–1.41).

Conclusions

Critically ill COVID-19 patients with AKI have higher RRI compared to those without AKI, and elevated RRI may have a role in identifying severe and oliguric AKI at the bedside in these patients.

Focus on renal blood flow in mechanically ventilated patients with SARS-CoV-2: a prospective pilot study

Mechanically ventilated patients with ARDS due to the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) seem particularly susceptible to AKI. Our hypothesis was that the renal blood flow could be more compromised in SARS-CoV-2 patients than in patients with “classical” ARDS. We compared the renal resistivity index (RRI) and the renal venous flow (RVF) in ARDS patients with SARS-CoV-2 and in ARDS patients due to other etiologies. Prospective, observational pilot study performed on 30 mechanically ventilated patients (15 with SARS-COV-2 ARDS and 15 with ARDS). Mechanical ventilation settings included constant-flow controlled ventilation, a tidal volume of 6 ml/kg of ideal body weight and the PEEP level titrated to the lowest driving pressure. Ultrasound Doppler measurements of RRI and RVF pattern were performed in each patient. Patients with SARS-COV-2 ARDS had higher RRI than patients with ARDS (0.71[0.67–0.78] vs 0.64[0.60–0.74], p = 0.04). RVF was not-continuous in 9/15 patients (71%) in the SARS-COV-2 ARDS group and in and 5/15 (33%) in the ARDS group (p = 0.27). A linear correlation was found between PEEP and RRI in patients with SARS-COV-2 ARDS (r² = 0.31; p = 0.03) but not in patients with ARDS. Occurrence of AKI was 53% in patients with SARS-COV-2 ARDS and 33% in patients with ARDS (p = 0.46). We found a more pronounced impairment in renal blood flow in mechanically ventilated patients with SARS-COV-2 ARDS, compared with patients with “classical” ARDS.