Early Prediction Of Acute Kidney Injury Research Articles

Prediction of acute kidney injury using a combined model of inflammatory vascular endothelium biomarkers and ultrasound indices.

Acute kidney injury (AKI) is a common complication of sepsis, with the burden of long hospital admission. Early prediction of AKI is the most effective strategy for intervention and improvement of the outcomes. In our study, we aimed to investigate the predictive performance of the combined model using ultrasound indices (grayscale and Doppler indieces), endothelium injury (E-selectin, VCAM-1, ICAM1, Angiopoietin 2, syndecan-1, and eNOS) as well as inflammatory biomarkers (TNF-a, and IL-1β) to identify AKI. Sixty albino rats were divided into control and lipopolysaccharide (LPS) groups. Renal ultrasound, biochemical and immunohistological variables were recorded 6 hrs, 24 hrs, and 48 hrs after AKI. Endothelium injury and inflammatory markers were found to be significantly increased early after AKI, and correlated significantly with kidney size reduction and renal resistance indices elevation. Using area under the curve (AUC), the combined model was analyzed based on ultrasound and biochemical variables and provided the highest predictive value for renal injury.

Identification of AKI signatures and classification patterns in ccRCC based on machine learning.

Acute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive. Four public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated as a validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers. Seven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1 T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability. Our study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The current work not only sheds light on the early prediction of AKI, but also provides new insights into the correlation between AKI and ccRCC.

The clinical significance of renal resistance index (RRI) and renal oxygen saturation (RrSO2) in critically ill children with AKI: a prospective cohort study

ObjectiveThe purpose of this study was to look into the clinical significance of the renal resistance index (RRI) and renal oxygen saturation (RrSO2) in predicting the development of acute kidney injury (AKI) in critically ill children. A new non-invasive method for the early detection and prediction of AKI needs to develop.MethodsPatients admitted to the pediatric intensive care unit (PICU) affiliated with the capital institute of pediatrics from December 2020 to March 2021 were enrolled consecutively. Data of clinical information, renal Doppler ultrasound, RrSO2, and hemodynamic index within 24 h of admission were prospectively collected. Patients were divided into two groups: the study group was AKI occurred within 72 h, while the control group did not. SPSS (version 25.0) was used to analyze the data, and P < 0.05 was considered a statistical difference.Results1) A total of 66 patients were included in this study, and the incidence of AKI was 19.70% (13/66). The presence of risk factors (shock, tumor, severe infection) increased the incidence of AKI by three times. 2) Univariate analysis showed significant differences in length of hospitalization, white blood cells (WBC), C-reactive protein (CRP), renal resistance index (RRI), and ejection fraction (EF) between the study and control groups (P < 0.05). There were no significant differences in renal perfusion semi-quantitative score (P = 0.053), pulsatility index (P = 0.051), pediatric critical illness score (PCIS), and peripheral vascular resistance index (P > 0.05). 3) Receiver operating characteristic (ROC) curve showed that if RRI > 0.635, the sensitivity, specificity, and AUC for predicting AKI were 0.889, 0.552, and 0.751, respectively; if RrSO2 < 43.95%, the values were 0.615, 0.719 and 0.609, respectively; if RRI and RrSO2 were united, they were 0.889, 0.552, and 0.766, respectively.ConclusionsThe incidence of AKI is high in PICU patients. And infection, RRI, and EF are risk factors for AKI in PICU patients. RRI and RrSO2 have certain clinical significance in the early prediction of AKI and may provide a new non-invasive method for early diagnosis and prediction of AKI.

Biomarkers for prediction of acute kidney injury in pediatric patients: a systematic review and meta-analysis of diagnostic test accuracy studies.

Severity of acute kidney injury (AKI) confers higher odds of mortality. Timely recognition and early initiation of preventive measures may help mitigate the injury further. Novel biomarkers may aid in the early detection of AKI. The utility of these biomarkers across various clinical settings in children has not been evaluated systematically. To synthesize the currently available evidence on different novel biomarkers for the early diagnosis of AKI in pediatric patients. We searched four electronic databases (PubMed, Web of Science, Embase, and Cochrane Library) for studies published between 2004 and May 2022. Cohort and cross-sectional studies evaluating the diagnostic performance of biomarkers in predicting AKI in children were included. Participants in the study included children (aged less than 18years) at risk of AKI. We used the QUADAS-2 tool for the quality assessment of the included studies. The area under the receiver operating characteristics (AUROC) was meta-analyzed using the random-effect inverse-variance method. Pooled sensitivity and specificity were generated using the hierarchical summary receiver operating characteristic (HSROC) model. We included 92 studies evaluating 13,097 participants. Urinary NGAL and serum cystatin C were the two most studied biomarkers, with summary AUROC of 0.82 (0.77-0.86) and 0.80 (0.76-0.85), respectively. Among others, urine TIMP-2*IGFBP7, L-FABP, and IL-18 showed fair to good predicting ability for AKI. We observed good diagnostic performance for predicting severe AKI by urine L-FABP, NGAL, and serum cystatin C. Limitations were significant heterogeneity and lack of well-defined cutoff value for various biomarkers. Urine NGAL, L-FABP, TIMP-2*IGFBP7, and cystatin C showed satisfactory diagnostic accuracy in the early prediction of AKI. To further improve the performance of biomarkers, they need to be integrated with other risk stratification models. PROSPERO (CRD42021222698). A higher resolution version of the Graphical abstract is available as "Supplementary information".

The urine albumin creatinine ratio is one of the predictors of acute kidney injury in hepatitis C-related cirrhotic hepatic encephalopathy

Background/objectiveHepatic encephalopathy (HE) is a main manifestation of acute decompensation in liver cirrhosis. Recently, systemic inflammation was proposed as a key mechanism in the development of acute kidney injury (AKI) in cirrhotic patients. The urine albumin creatinine ratio (UACR) is considered a marker of systemic inflammation in a variety of clinical settings. Here, we aimed to evaluate the role of the urine albumin creatinine ratio in the early prediction of AKI in HE.Patients and methodsSixty-seven consecutive patients presented with cirrhotic HE, and 59 age- and sex-matched cirrhotic patients with no history of HE served as controls. HE was defined and graded by the West Haven criteria. The severity of liver cirrhosis was evaluated by the Child-Turcotte-Pugh (CTP) score, and model for end-stage liver disease (MELD) score. The incidence of AKI that developed during hospital admission and the in-hospital mortality rate was estimated among HE patients. In addition, predictors of AKI were analyzed.ResultsThe mean age of HE patients was 58.09±12.26 years; 36 (53.7%) were males, and 31 (46.3%) were females. Among HE patients, 16 (23.9%) developed AKI during hospital admission. The in-hospital mortality rate among HE patients was 22 (32.8%), the in-hospital mortality among HE-AKI patients was 81.3% (n=13/16), and UACR levels > 91.5 mg/g identified HE-AKI with 81.25% sensitivity (AUC = 0.85, P ≤ 0.001).ConclusionsPatients with cirrhotic HE are at high risk of AKI. HE-AKI patients had a high rate of in-hospital mortality. Estimation of UACR at hospital admission is suggested for the early detection of patients with HE-AKI.

Construction and Validation of a Risk Prediction Model for Acute Kidney Injury in Patients Suffering from Septic Shock.

Background Acute kidney injury (AKI) is an important complication in critically ill patients, especially in sepsis and septic shock patients. Early prediction of AKI in septic shock can provide clinicians with sufficient information for timely intervention so that improve the patients' survival rate and quality of life. The aim of this study was to establish a nomogram that predicts the risk of AKI in patients with septic shock in the intensive care unit (ICU). Methods The data were collected from the Medical Information Mart for Intensive Care III (MIMIC-III) database between 2001 and 2012. The primary outcome was AKI in the 48 h following ICU admission. Univariate and multivariate logistic regression analyses were used to screen the independent risk factors of AKI. The performance of the nomogram was evaluated according to the calibration curve, receiver operating characteristic (ROC) curve, decision curve analysis, and clinical impact curve. Results A total of 2415 patients with septic shock were included in this study. In the training and validation cohort, 1091 (64.48%) of 1690 patients and 475 (65.52%) of 725 patients developed AKI, respectively. The predictive factors for nomogram construction were gender, ethnicity, congestive heart failure, diabetes, obesity, Simplified Acute Physiology Score II (SAPS II), angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARBs), bilirubin, creatinine, blood urea nitrogen (BUN), and mechanical ventilation. The model had a good discrimination with the area under the ROC curve of 0.756 and 0.760 in the training and validation cohorts, respectively. The calibration curve for probability of AKI in septic shock showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analysis indicated that the nomogram conferred high clinical net benefit. Conclusion The proposed nomogram can quickly and effectively predict the risk of AKI at an early stage in patients with septic shock in ICU, which can provide information for timely and efficient intervention in patients with septic shock in the ICU setting.

Diagnostic accuracy of renal angina index alone or in combination with biomarkers for predicting acute kidney injury in children.

Early recognition of patients at risk for severe acute kidney injury (AKI) by renal angina index (RAI) may help in the early institution of preventive measures. Objective was to evaluate performance of RAI alone or in combination with biomarkers in predicting severe AKI (KDIGO stage 2 and 3 or equivalent) and receipt of kidney replacement therapy (KRT) in critically ill children. We searched PubMed, EMBASE, Web of Sciences, and CENTRAL for studies published till May 2021. Search terms included acute kidney injury, pediatrics, adolescent, renal angina index, and biomarker. Proceedings of relevant conferences and references of included studies were also scrutinized. Two reviewers independently assessed the study eligibility. Cohort and cross-sectional studies evaluating the diagnostic performance of RAI in predicting AKI or receipt of KRT in children were included.Eligible participants were the children less than 18 years with RAI assessment on day 0 ofadmission.We followed PRISMA-DTA guidelines and used the QUADAS-2 tool for quality assessment. A bivariate model for meta-analysis was used to calculate the summary estimates of diagnostic parameters. Major outcomes were the diagnostic accuracy of RAI (≥ 8) alone or with biomarkers in predicting severe AKI and KRT receipt. Diagnostic accuracy was reported using summary sensitivity, specificity, and area under the curve (AUC). Overall, 22 studies (24 reports, 14,001 participants) were included. RAI ≥ 8 on day 0 has summary sensitivity, specificity, and AUC of 0.86 (95% CI, 0.77-0.92), 0.77 (0.68-0.83), and 0.88 (0.85-0.91) respectively for prediction of severe AKI on day 3. In comparison, a combination of RAI and urinary neutrophil gelatinase-associated lipocalin (NGAL) showed summary sensitivity, specificity, and AUC of 0.76 (0.62-0.85), 0.89 (0.74-0.96), and 0.87 (0.84-0.90) respectively for predicting severe AKI. The sensitivity, specificity, and AUC of RAI for predicting receipt of KRT were 0.82 (0.71-0.90), 0.74 (0.66-0.81), and 0.85 (0.81-0.88) respectively. In meta-regression, only the study setting (sepsis vs. heterogenous) was associated with heterogeneity. We observed substantial heterogeneity among eligible studies. Five studies had concerns in patient selection, and seven studies also had applicability concerns in patient selection for this review. Moderate certainty evidence showed that RAI ≥ 8 has good predicting ability in recognizing children at risk of severe AKI and receipt of KRT. The combination of urinary NGAL and RAI further improves the predicting ability (low-certainty evidence). Further studies are required on the context-driven assessment of novel biomarkers in the early prediction of AKI in RAI-positive children. Systematic review registration number: CRD4202122268. A higher resolution version of the Graphical abstract is available as Supplementary information.

Neutrophil gelatinase associated lipocalin in predicting postoperative acute kidney injury in elderly.

Elderly patients are at increased risk of developing acute kidney injury (AKI) following major surgery and rapid diagnosis is essential. This study aimed to examine the potential utility of plasma neutrophil gelatinase associated lipocalin levels in early prediction of AKI in geriatric patients undergoing laparotomic oncological surgery. This prospective single-center cohort study included 60 geriatric patients (≥ 65years of age) that underwent major oncologic surgery with laparotomy. Perioperative measurements of plasma creatinine, blood urinary nitrogen, plasma lactate, urine output, and neutrophil gelatinase associated lipocalin (NGAL) were made. Patients were followed for AKI development, which is the primary outcome measure, and predictive role of NGAL was investigated. At 48h follow-up, AKI developed in 13 patients (21.7%). Significant differences in creatinine (p < 0.001), NGAL (p < 0.001), and urine output levels (p = 0.001) were evident over time between the two groups. High NGAL measurements at 6 and 24h after surgery seem to be highly predictive of AKI development. At 6h, a plasma NGAL level greater than 71.8ng/mL has a sensitivity and specificity of 85% and 81% in predicting subsequent AKI development. Plasma NGAL levels seem to represent an early and reliable marker for AKI in elderly patients undergoing laparotomic surgery with the potential to allow early diagnosis and prevent further renal deterioration. Further confirmatory studies are warranted. The study was registered to ClinicalTrials.gov (number, NCT05030727). Elderly patients are at increased risk of postoperative acute kidney injury (AKI). This study shows that plasma neutrophil gelatinase associated lipocalin is a helpful marker to predict AKI in elderly patients undergoing laparotomic major surgery, which will allow early diagnosis and prevent further renal deterioration in this vulnerable patient group.

Prehospital Blood Pressure and Lactate are Early Predictors of Acute Kidney Injury After Trauma

BackgroundThe purpose of this study is to report the prevalence of acute kidney injury (AKI) after trauma in our center, describe the risk factors associated with AKI, and determine whether these risk factors help avoid AKI. Materials and MethodsWe retrospectively analyzed the data which were prospectively collected from a single center trauma registry from January 2017 to December 2018. Patients who were <16 years of age, patients with burns, and patients with chronic kidney disease were excluded from the present study. AKI was defined according to the risk, injury, failure, loss of the kidney function, and end-stage kidney disease (RIFLE) classification from serum creatinine alone. A logistic regression analysis was performed to identify prehospital and early hospital risk factors for AKI. ResultsThere were 806 trauma patients recorded in the database. One hundred thirty cases were excluded based on the abovementioned exclusion criteria. Six hundred seventy-six patients were included in the analysis. The prevalence of AKI in the overall population was 14.5% including 10.5% of patients with stage R, 3.0% of patients with stage I and 1.0% with stage F. The incidence of AKI increased to 36.3%, 12.1% and 3.3% in the subgroup of patients with hemorrhagic shock. The multivariate analysis revealed that the minimum prehospital systolic blood pressure and arterial lactate level were independent predictors of AKI. The model showed good discrimination with an area under the receiver operating characteristic curve (AUC-ROC) of 0.867 and 0.852 in the prediction of AKI stage I or F. The cutoff values were ≤126 mmHg and ≥2.5 mmol/L, respectively. ConclusionThese parameters showed good performance in the early prediction of AKI after trauma. They are associated with the early onset of AKI after trauma and may be an early predictor of the effects of treatment to prevent AKI.

Snake bite associated with acute kidney injury.

Acute kidney injury (AKI) is a well-known life-threatening systemic effect of snake envenomation which commonly happens secondary to snake bites from families of Viperidae and Elapidae. Enzymatic toxins in snake venom result in injuries to all kidney cell types including glomerular, tubulo-interstitial and kidney vasculature. Pathogenesis of kidney injury due to snake envenomation includes ischaemia secondary to decreased kidney blood flow caused by systemic bleeding and vascular leakage, proteolytic degradation of the glomerular basement membrane by snake venom metalloproteinases (SVMPs), deposition of microthrombi in the kidney microvasculature (thrombotic microangiopathy), direct cytotoxic action of venom, systemic myotoxicity (rhabdomyolysis) and accumulation of large amounts of myoglobin in kidney tubules. Clinical features of AKI include fatigue, loss of appetite, headache, nausea, vomiting, oliguria and anuria. Monitoring of blood pressure, fluid balance, serum creatinine, blood urea nitrogen and serum electrolytes is useful in managing AKI induced by snake envenomation. Early initiation of anti-snake venom and early diagnosis of AKI are always desirable. Biomarkers which will help in early prediction of AKI are being explored, and current studies suggest that urinary clusterin, urinary neutrophil gelatinase-associated lipocalin, and serum cystatin C may play an important clinical role in the future. Apart from fluid and electrolyte management, kidney support including early and prompt initiation of kidney replacement therapy when indicated forms the bedrock in managing snake bite-associated AKI. Long-term follow-up is important because of chances of progression towards CKD.

Developing and validating a risk score model for prediction of acute kidney injury in non-ICU hospitalized patients.

Acute kidney injury (AKI) is a common complication in hospitalized patients. Several risk score models have been designed for intensive care unit (ICU) patients. We aim to establish a new risk prediction score for AKI patients in general wards. All hospitalized ward patients who developed AKI were included in our study. To develop a new prediction score model we used the data collected from 107 patients who developed AKI. We used our prospective validation cohort (122 patients) to develop and validate this prediction score model of AKI. Of 10,243 patients, 107 (1%) patients developed AKI 24 hours after admission to the general wards. Mortality rate was 26.2%. A score model of 15 points, based on clinical and laboratory data, was developed for prediction of AKI. We demonstrated a cutoff value ≥4 out of 15 as a predictor of AKI in non-ICU patients. The area under the receiver operating characteristic (AUC ROC) value of the score model was 0.950, 95% CI (confidence interval) and the p-value <0.001 with sensitivity of 94.39 and specificity of 81.43. On applying this score model on a prospective group of patients (validation group n=122), the AUC ROC value was 0.826. We developed and validated a new risk score model with a cutoff value ≥4 out of 15 for prediction of AKI in non-ICU patients. It will help in the early prediction of AKI in non-ICU patients.

Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance

Acute kidney injury (AKI) is a major complication after cardiothoracic surgery. Early prediction of AKI could prompt preventive measures, but is challenging in the clinical routine. One important reason is that the amount of postoperative data is too massive and too high-dimensional to be effectively processed by the human operator. We therefore sought to develop a deep-learning-based algorithm that is able to predict postoperative AKI prior to the onset of symptoms and complications. Based on 96 routinely collected parameters we built a recurrent neural network (RNN) for real-time prediction of AKI after cardiothoracic surgery. From the data of 15,564 admissions we constructed a balanced training set (2224 admissions) for the development of the RNN. The model was then evaluated on an independent test set (350 admissions) and yielded an area under curve (AUC) (95% confidence interval) of 0.893 (0.862–0.924). We compared the performance of our model against that of experienced clinicians. The RNN significantly outperformed clinicians (AUC = 0.901 vs. 0.745, p < 0.001) and was overall well calibrated. This was not the case for the physicians, who systematically underestimated the risk (p < 0.001). In conclusion, the RNN was superior to physicians in the prediction of AKI after cardiothoracic surgery. It could potentially be integrated into hospitals’ electronic health records for real-time patient monitoring and may help to detect early AKI and hence modify the treatment in perioperative care.

Nomogram to predict the risk of septic acute kidney injury in the first 24 h of admission: an analysis of intensive care unit data

Background Acute kidney injury (AKI) is a significant cause of morbidity and mortality, especially in sepsis patients. Early prediction of AKI can help physicians determine the appropriate intervention, and thus, improve the outcome. This study aimed to develop a nomogram to predict the risk of AKI in sepsis patients (S-AKI) in the initial 24 h following admission. Methods Sepsis patients with AKI who met the Sepsis 3.0 criteria and Kidney Disease: Improving Global Outcomes criteria in the Massachusetts Institute of Technology critical care database, Medical Information Mart for Intensive Care (MIMIC-III), were identified for analysis. Data were analyzed using multiple logistic regression, and the performance of the proposed nomogram was evaluated based on Harrell’s concordance index (C-index) and the area under the receiver operating characteristic curve. Results We included 2917 patients in the analysis; 1167 of 2042 patients (57.14%) and 469 of 875 patients (53.6%) had AKI in the training and validation cohorts, respectively. The predictive factors identified by multivariate logistic regression were blood urea nitrogen level, infusion volume, lactate level, weight, blood chloride level, body temperature, and age. With the incorporation of these factors, our model had well-fitted calibration curves and achieved good C-indexes of 0.80 [95% confidence interval (CI): 0.78–0.82] and 0.79 (95% CI: 0.76–0.82) in predicting S-AKI in the training and validation cohorts, respectively. Conclusion The proposed nomogram effectively predicted AKI risk in sepsis patients admitted to the intensive care unit in the first 24 h.

Role of biomarkers as predictors of acute kidney injury and mortality in decompensated cirrhosis

Evidence suggests that novel biomarkers predict acute kidney injury (AKI) development and outcome earlier than serum creatinine. The aim of this study was to determine the incidence and prognosis of AKI in decompensated cirrhotic patients, and also assess the usefulness of plasma cystatin C, urine neutrophil gelatinase associated lipocalin (NGAL), tissue inhibitor of metalloproteinase-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7) in early prediction of AKI and mortality. Single-center, prospective observational study enrolling decompensated cirrhotic patients without AKI at the time of admission. Of 111 patients with decompensated cirrhosis, 45 (40.5%) developed AKI while hospitalized. Even with 53.3% being transient (stage 1), mortality was significantly higher in AKI than non-AKI patients (46.5% vs. 25%, p = 0.02). Plasma cystatin C and urine NGAL, but not urine [TIMP-2]·[IGFBP7] at the time of admission were found to be independent early predictors of AKI. Substitution of cystatin C for creatinine significantly improved the model for end-stage liver disease (MELD) score accuracy for mortality prediction. The incidence of AKI is high and is associated with high mortality in decompensated cirrhotic patients. Plasma cystatin C and urine NGAL are useful for early detection of AKI. MELD-cystatin C, rather than original MELD, improves predictive accuracy of mortality.

Serum Klotho as a marker for early diagnosis of acute kidney injury after cardiac surgery.

Early diagnosis of acute kidney injury (AKI) after cardiac surgery is based on serum creatinine which is neither a specific nor a sensitive biomarker. In our study, we investigated the role of serum Klotho in early prediction of AKI after cardiac surgery using cardiopulmonary bypass (CPB). The included patients were classified into three groups according to AKI stages using KDIGO criteria. The measurements of creatinine and Klotho levels in serum were performed before surgery, at the end of CPB, 2 hours after the end of CPB, 24 hours and 48 hours postoperatively. Seventy-eight patients were included in the study. A significant increase of creatinine levels (p<0.001) was measured on the first day after the surgery in both AKI groups compared to the non-AKI group. However, a significant difference between AKI-2 and AKI-1 groups (p=0.006) was not measured until the second day after the operation. Using decision trees for classification of patients with a higher or lower risk of AKI we found out that Klotho discriminated between the patients at low risk of developing more severe kidney injury in the first hours after surgery and the patients at high risk better than creatinine. Adding also the early measurements of creatinine in the decision tree model further improved the prediction of AKI. Serum Klotho may be useful to discriminate between the patients at lower and the patients at higher risk of developing severe kidney injury after cardiac surgery using CPB already in the first hours after surgery.

Predicting acute kidney injury using urinary liver-type fatty-acid binding protein and serum N-terminal pro-B-type natriuretic peptide levels in patients treated at medical cardiac intensive care units

BackgroundThe early prediction of acute kidney injury (AKI) can facilitate timely intervention and prevent complications. We aimed to understand the predictive value of urinary liver-type fatty-acid binding protein (L-FABP) levels on admission to medical (non-surgical) cardiac intensive care units (CICUs) for AKI, both independently and in combination with serum N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels.MethodsWe prospectively investigated the predictive value of L-FABP and NT-proBNP for AKI in a large, heterogeneous cohort of patients treated in medical CICUs. Baseline urinary L-FABP and serum NT-proBNP were measured on admission. AKI was diagnosed according to the Kidney Disease: Improving Global Outcomes criteria. We studied 1273 patients (mean age, 68 years), among whom 46% had acute coronary syndromes, 38% had acute decompensated heart failure, 5% had arrhythmia, 3% had pulmonary hypertension, 2% had acute aortic syndrome, 2% had infective endocarditis, and 1% had Takotsubo cardiomyopathy.ResultsUrinary L-FABP levels correlated with serum NT-proBNP levels (r = 0.17, p < 0.0001). AKI occurred in 224 patients (17.6%), including 48 patients with stage 2 or 3 disease. Patients who developed AKI had higher one-week and 6-month mortality than those who did not develop AKI (p = 0.0002 and p = 0.003, respectively). In the multivariate logistic analysis, both L-FABP (p < 0.0001) and NT-proBNP (p = 0.006) were independently associated with the development of AKI. Adding L-FABP and NT-proBNP to a baseline model that included established risk factors further improved reclassification (p < 0.001) and discrimination (p < 0.01) beyond that of the baseline model or any single biomarker individually.ConclusionsUrinary L-FABP and serum NT-proBNP levels on admission are independent predictors of AKI, and when used in combination, improve early prediction of AKI in patients hospitalized at medical CICUs.

Predictive value of red blood cell distribution width for acute kidney injury in children with sepsis

To study the clinical value of red blood cell distribution width (RDW) in the early prediction of acute kidney injury (AKI) in children with sepsis. A total of 126 children with sepsis were divided into an AKI group (n=66) and a non-AKI group (n=60) according to the presence or absence of AKI. These patients were also classified into high-RDW and low-RDW groups according to the mean RDW. The groups were compared in terms of age, male-to-female ratio, body mass index (BMI), Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Sequential Organ Failure Assessment (SOFA) score, serum blood urea nitrogen (BUN), creatinine (Cr), uric acid (UA), serum C-reactive protein (CRP), and routine blood test results. Independent factors associated with RDW were analyzed by multiple linear regression. Age, male-to-female ratio, BMI, CRP, SOFA score, and APACHE II score did not differ significantly between the AKI and non-AKI groups (P>0.05), but the AKI group had significantly higher BUN, Cr, UA, and RDW levels than the non-AKI group (P<0.05). Age, male-to-female ratio, and BMI did not differ significantly between the high-RDW and low-RDW groups (P>0.05), but the high-RDW group had significantly higher BUN, Cr, UA, CRP, SOFA score, APACHE II score, Hb, and mean corpuscular volume (MCV) than the low-RDW group (P<0.05). The multiple linear regression analysis showed that age, sex, APACHE II score, Cr, Hb, and MCV were independent factors associated with RDW. RDW has a certain clinical value in the early prediction of AKI in children with sepsis.

Assessment of a renal angina index for prediction of severe acute kidney injury in critically ill children: a multicentre, multinational, prospective observational study

Acute kidney injury (AKI) occurs in one in four children admitted to the intensive care unit (ICU) and AKI severity is independently associated with increased patient morbidity and mortality. Early prediction of AKI has the potential to improve outcomes. In smaller, single center populations, we have previously derived and validated the performance of the renal angina index (RAI), a context driven risk stratification system, to predict severe AKI. A prospective, observational study (AWARE1, January-December 2014) was conducted in intensive care units from 32 centers in 9 countries. The primary outcome was the presence of severe AKI ("AKIS"; Stage 2-3 AKI KDIGO guidelines) on the third day after ICU admission (). We compared the performance of the RAI to changes in serum creatinine relative to baseline (SCr/Base) for prediction of the primary outcome and secondary outcomes of interest. A RAI ≥ 8 defined fulfillment of renal angina (RA+); RA+ was compared to SCr increased relative to baseline ("SCr>Base"; using maximum SCr in first 12 hours of ICU admission). The 1590 patients studied were 55% male and had median age of 54.5 months. 286 patients (17.9%) were RA+. AKIS occurred in 121 (42.3%) RA+ vs. 247 (18.9%) RA-patients (relative risk (RR) 2.23; 95% confidence interval (CI): 1.87-2.66, p<0.001). 368 (23.1%) patients with AKIS had increased renal replacement therapy utilization (10.9% vs. 1.5%, p<0.001) and increased mortality (7.6% vs. 4.3%, p=0.01) compared to patients without AKIS. RA+ demonstrated better prediction for AKIS than SCr>Base (RR: 1.61; (1.33-1.93), p<0.001) which was maintained on multivariate regression (independent odds ratio (OR): RA+ 3.21; 95% CI (2.20-4.67) vs. SCr>Base 0.68; 95% CI (0.49-4.94)). Earlier, better prediction of severe AKI has the potential to improve AKI associated patient outcomes. Compared to isolated, context-free changes in SCr, renal angina risk assessment improved accuracy for prediction of severe AKI in critically ill children and young adults.

Non-invasive measurement of renal perfusion and oxygen metabolism to predict postoperative acute kidney injury in neonates and infants after cardiopulmonary bypass surgery

BackgroundThe pathophysiology of acute kidney injury (AKI) after cardiopulmonary bypass surgery for congenital heart disease is not completely understood. The aim of this study was to carry out a prospective analysis of the diagnostic value of non-invasive monitoring of renal oxygenation and microcirculation by combining laser Doppler flowmetry and tissue spectrometry. MethodsIn 50 neonates and infants who underwent repair (n = 31) or neonatal palliation (n = 19) of congenital heart disease with cardiopulmonary bypass, renal oxygenation, and microcirculatory flow, the approximate renal metabolic rate of oxygen and Doppler-based renal resistive index were determined after surgery. Correlations between these parameters and the occurrence of AKI according to the Pediatric Risk, Injury, Failure, Loss, End Stage Renal Disease criteria were investigated. ResultsAcute kidney injury occurred in 45% of patients after repair and in 32% after palliation. Renal oxygenation was significantly lower and the approximate renal metabolic rate of oxygen significantly higher in patients with AKI (P < 0.05). The microcirculatory flow was significantly higher in patients with AKI after neonatal palliation (P < 0.05), whereas renal resistive index was significantly higher in patients with AKI after repair (P < 0.05). The sensitivity of renal oxygenation, metabolic rate of oxygen, microcirculation, and resistive index in predicting AKI was 78–80, 73–78, 64–83, and 71–74%, respectively, with a specificity of 63–65, 54–75, 64–78, and 46–74% (area under the curve: 0.73–0.75, 0.68–0.83, 0.52–0.68, and 0.60–0.75), respectively. ConclusionsMonitoring of renal oxygen metabolism allows early prediction of AKI in infants after cardiac surgery. In contrast, renal resistive index does not allow prediction of AKI after neonatal palliation with aortopulmonary shunt establishment.

Validity of Klotho, CYR61 and YKL-40 as ideal predictive biomarkers for acute kidney injury: review study.

Acute kidney injury (AKI) is still a headache for clinicians and scientists as a possible reason for increased death among intensive care unit (ICU) patients after invasive cardiac surgery. Furthermore, the diagnostic process for AKI using conventional biomarkers is not sufficient to ensure early warning of this condition because of the morbid influence of non-renal factors that definitively delay the time for the prognosis. These imposed limitations have led to significant amounts of research targeted towards identifying novel biomarkers for AKI with a sustained degree of sensitivity and specificity. Here, we reviewed previous studies conducted on the Klotho, CYR61 and YKL-40 biomarkers in relation to AKI. Review of the literature conducted in the Institute of Clinical Chemistry & Biochemistry, Ljubljana University Medical Center, Slovenia. The literature was searched in PubMed and the Cochrane Library. From the database of this specialty, we selected 17 references that matched our context for detailed analysis and further investigation. The studies reviewed showed notable differences in their results relating to the diagnostic impact of Klotho, CYR61 and YKL-40 on early prediction of AKI. The results regarding the Klotho, CYR61 and YKL-40 biomarkers showed markedly equivocal performance in the previous studies and did not fulfill the expectations that these factors would form valid possible biomarkers for AKI.