Assessment of urine CCL2 as a potential diagnostic biomarker for acute kidney injury and septic acute kidney injury in intensive care unit patients.
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SUBMITTER: Peng Y
PROVIDER: S-EPMC10863526 | biostudies-literature | 2024 Dec
REPOSITORIES: biostudies-literature
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Assessment of urine CCL2 as a potential diagnostic biomarker for acute kidney injury and septic acute kidney injury in intensive care unit patients.
Peng Yuan Y Wang Qin Q Jin Fang F Tao Tao T Qin Qihong Q
Renal failure 20240212 1
Acute kidney injury (AKI) is a prevalent and serious condition in the intensive care unit (ICU), associated with significant morbidity and mortality. Septic acute kidney injury (SAKI) contributes substantially to AKI cases in the ICU. However, current diagnostic methods have limitations, necessitating the exploration of novel biomarkers. In this study, we investigated the potential of plasma and urine CCL2 levels as diagnostic markers for AKI and SAKI in 216 ICU patients. Our findings revealed s ...[more]
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Project description:PurposeDetermine whether there are unique patterns to the urine biochemistry profile in septic compared with non-septic acute kidney injury (AKI) and whether urinary biochemistry predicts worsening AKI, need for renal replacement therapy and mortality.Materials and methodsProspective cohort study of critically ill patients with septic and non-septic AKI, defined by the RIFLE (Risk, Injury, Failure, Loss, End-Stage) criteria. Urine biochemistry parameters were compared between septic and non-septic AKI and were correlated with neutrophil gelatinase-associated lipocalin (NGAL), worsening AKI, renal replacement therapy (RRT), and mortality.ResultsEighty-three patients were enrolled, 43 (51.8%) with sepsis. RIFLE class was not different between groups (P = .43). Urine sodium (UNa) <20 mmol/L, fractional excretion of sodium (FeNa) <1%, and fractional excretion of urea (FeU) <35% were observed in 25.3%, 57.8%, and 33.7%, respectively. Septic AKI had lower UNa compared with non-septic AKI (P = .04). There were no differences in FeNa or FeU between groups. Urine NGAL was higher for FeNa ≥ 1% compared to FeNa<1% (177.4 ng/mL [31.9-956.5] vs 48.0 ng/mL [21.1-232.4], P = .04). FeNa showed low correlation with urine NGAL (P = .05) and plasma NGAL (P = .14). There was poor correlation between FeU and urine NGAL (P = .70) or plasma NGAL (P = .41). UNa, FeNa, and FeU showed poor discrimination for worsening AKI, RRT and mortality.ConclusionUrine biochemical profiles do not discriminate septic and non-septic AKI. UNa, FeNa, and FeU do not reliably predict biomarker release, worsening AKI, RRT or mortality. These data imply limited utility for these measures in clinical practice in critically ill patients with AKI.
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Project description:BackgroundNew renal biomarkers measured in urine promise to increase specificity for risk stratification and early diagnosis of acute kidney injury (AKI) but concomitantly may be altered by urine concentration effects and chronic renal insufficiency. This study therefore directly compared the performance of AKI biomarkers in urine and plasma.MethodsThis single-center, prospective cohort study included 110 unselected adults undergoing cardiac surgery with cardiopulmonary bypass between 2009 and 2010. Plasma and/or urine concentrations of creatinine, cystatin C, neutrophil gelatinase-associated lipocalin (NGAL), liver fatty acid-binding protein (L-FABP), kidney injury molecule 1 (KIM1), and albumin as well as 15 additional biomarkers in plasma and urine were measured during the perioperative period. The primary outcome was AKI defined by AKIN serum creatinine criteria within 72 hours after surgery.ResultsBiomarkers in plasma showed markedly better discriminative performance for preoperative risk stratification and early postoperative (within 24h after surgery) detection of AKI than urine biomarkers. Discriminative power of urine biomarkers improved when concentrations were normalized to urinary creatinine, but urine biomarkers had still lower AUC values than plasma biomarkers. Best diagnostic performance 4h after surgery had plasma NGAL (AUC 0.83), cystatin C (0.76), MIG (0.74), and L-FAPB (0.73). Combinations of multiple biomarkers did not improve their diagnostic power. Preoperative clinical scoring systems (EuroSCORE and Cleveland Clinic Foundation Score) predicted the risk for AKI (AUC 0.76 and 0.71) and were not inferior to biomarkers. Preexisting chronic kidney disease limited the diagnostic performance of both plasma and urine biomarkers.ConclusionsIn our cohort plasma biomarkers had higher discriminative power for risk stratification and early diagnosis of AKI than urine biomarkers. For preoperative risk stratification of AKI clinical models showed similar discriminative performance to biomarkers. The discriminative performance of both plasma and urine biomarkers was reduced by preexisting chronic kidney disease.
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Project description:Background and objectivesLittle is known about the long-term burden of AKI in the pediatric intensive care unit. We aim to evaluate if pediatric AKI is associated with higher health service use post-hospital discharge.Design, setting, participants, & measurementsThis is a retrospective cohort study of children (≤18 years old) admitted to two tertiary centers in Montreal, Canada. Only the first admission per patient was included. AKI was defined in two ways: serum creatinine alone or serum creatinine and/or urine output. The outcomes were 30-day, 1-year, and 5-year hospitalizations, emergency room visits, and physician visits per person-time using provincial administrative data. Univariable and multivariable Poisson regression were used to evaluate AKI associations with outcomes.ResultsA total of 2041 children were included (56% male, mean admission age 6.5±5.8 years); 299 of 1575 (19%) developed AKI defined using serum creatinine alone, and when urine output was included in the AKI definition 355 of 1622 (22%) children developed AKI. AKI defined using serum creatinine alone and AKI defined using serum creatinine and urine output were both associated with higher 1- and 5-year hospitalization risk (AKI by serum creatinine alone adjusted relative risk, 1.42; 95% confidence interval, 1.12 to 1.82; and 1.80; 1.54 to 2.11, respectively [similar when urine output was included]) and higher 5-year physician visits (adjusted relative risk, 1.26; 95% confidence interval, 1.14 to 1.39). AKI was not associated with emergency room use after adjustments.ConclusionsAKI is independently associated with higher hospitalizations and physician visits postdischarge.
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Project description:IntroductionAcute kidney injury (AKI) is common among hospitalized patients and has a significant impact on morbidity and mortality. Although early prediction of AKI has the potential to reduce adverse patient outcomes, it remains a difficult condition to predict and diagnose. The purpose of this study was to evaluate the ability of a machine learning algorithm to predict for AKI as defined by Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 up to 48 hours in advance of onset using convolutional neural networks (CNNs) and patient electronic health record (EHR) data.MethodsA CNN prediction system was developed to use EHR data gathered during patients' stays to predict AKI up to 48 hours before onset. A total of 12,347 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) database. An XGBoost AKI prediction model and the sequential organ failure assessment (SOFA) scoring system were used as comparators. The outcome was AKI onset. The model was trained on routinely collected patient EHR data. Measurements included area under the receiver operating characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advance prediction of AKI onset.ResultsOn a hold-out test set, the algorithm attained an AUROC of 0.86 and PPV of 0.24, relative to a cohort AKI prevalence of 7.62%, for long-horizon AKI prediction at a 48-hour window before onset.ConclusionA CNN machine learning-based AKI prediction model outperforms XGBoost and the SOFA scoring system, revealing superior performance in predicting AKI 48 hours before onset, without reliance on serum creatinine (SCr) measurements.
| S-EPMC8116756 | biostudies-literature
Project description:PurposeTo analyse the usefulness of the composite index of the tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7) as urinary biomarkers for the early prediction of AKI in septic and non-septic patients.MethodsThis is a prospective, observational study including patients admitted to ICU from acute care departments and hospital length of stay <48 h. The main exclusion criteria were pre-existing eGFR <30 mL/min/1.73 m2 and hospitalisation 2 months prior to current admission. The [TIMP-2]·[IGFBP7] index was analysed twice, within the first 12 h of ICU admission.ResultsThe sample included 98 patients. AKI incidence during ICU stay was 50%. Sepsis was diagnosed in 40.8%. Baseline renal variables were comparable between subgroups except for a higher baseline eGFR in non-septic patients. Patients were stratified based on the presence of AKI and their highest level of [TIMP-2]·[IGFBP7] within the first 12 h of stay. [TIMP-2]·[IGFBP7] index values were dependent on the incidence of AKI but not of sepsis. [TIMP-2]·[IGFBP7] values were significantly related to AKI severity according to AKIN criteria (p < 0.0001). The AUROC curve to predict AKI of the worst [TIMP-2]·[IGFBP7] index value was 0.798 (sensitivity 73.5%, specificity 71.4%, p < 0.0001). Index values below 0.8 ruled out any need for renal replacement (NPV 100%), whereas an index >0.8 predicted a rate of AKI of 71% and AKIN ≥ 2 of 62.9%.ConclusionsIn our study, urinary [TIMP-2]·[IGFBP7] was an early predictor of AKI in ICU patients regardless of sepsis. Besides, index values <0.8(ng/mL)2/1000 ruled out the need for renal replacement.
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Project description:BackgroundpRIFLE (Pediatric Risk, Injury, Failure, Loss, End Stage Renal Disease), KDIGO (Kidney Disease Improving Global Outcomes) and pROCK (Pediatric Reference Change Value Optimized for AKI) are diagnostic criteria used for acute kidney injury (AKI) incidence evaluation. The aim of this study was to explore the diagnostic consistency, incidence and mortality rate, clinical signs, and influencing factors of renal injury related to sepsis in children diagnosed by three different AKI diagnostic criteria, and then evaluate which one was more valuable.MethodsA retrospective analysis was performed on the clinical data of children with severe sepsis. The patients were diagnosed and staged according to the 2007 pRIFLE standard, the 2012 KDIGO standard, and the 2018 pROCK standard. The clinical characteristics and prognosis of children with different stages of sepsis were compared between the three diagnostic standards.ResultsA total of 62 patients with sepsis were included. Blood stream infection is common (11 cases, 17.74%). According to pRIFLE, KDIGO, and pROCK standards, the incidence of sepsis-associated AKI (SA-AKI) was 74.2%, 67.7%, and 56.5%, respectively. The pRIFLE had the highest diagnostic rate of early detection of SA-AKI. There was no statistical difference in SA-AKI incidence or staging consistency between the pRIFLE and KDIGO groups (κ=0.0671; κ>0.60); the consistency of SA-AKI diagnoses across the three standards was good (all P values <0.05), and pROCK demonstrated a higher specificity. A high Pediatric Risk of Mortality (PRISM) score and high procalcitonin level were independent risk factors. Shock and renal replacement therapy were independent risk factors for SA-AKI death. Death from admission to 28 days after admission was used as an endpoint to draw a survival graph, which revealed that the AKI group had a significantly higher risk of death than did the non-AKI group.ConclusionsThe consistency of diagnosing SA-AKI across the three classification criteria was similar, and mortality rate increased with increased SA-AKI staging. The pRIFLE criteria were more sensitive in the early detection of SA-AKI, while the pROCK had higher specificity. There was no significant difference between the pRIFLE and KDIGO in terms of incidence, diagnosis, or staging of SA-AKI.
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Project description:BackgroundAcute kidney injury (AKI) represents a significant global health challenge, leading to increased patient distress and financial health care burdens. The development of AKI in intensive care unit (ICU) settings is linked to prolonged ICU stays, a heightened risk of long-term renal dysfunction, and elevated short- and long-term mortality rates. The current diagnostic approach for AKI is based on late indicators, such as elevated serum creatinine and decreased urine output, which can only detect AKI after renal injury has transpired. There are no treatments to reverse or restore renal function once AKI has developed, other than supportive care. Early prediction of AKI enables proactive management and may improve patient outcomes.ObjectiveThe primary aim was to develop a machine learning algorithm, NAVOY Acute Kidney Injury, capable of predicting the onset of AKI in ICU patients using data routinely collected in ICU electronic health records. The ultimate goal was to create a clinical decision support tool that empowers ICU clinicians to proactively manage AKI and, consequently, enhance patient outcomes.MethodsWe developed the NAVOY Acute Kidney Injury algorithm using a hybrid ensemble model, which combines the strengths of both a Random Forest (Leo Breiman and Adele Cutler) and an XGBoost model (Tianqi Chen). To ensure the accuracy of predictions, the algorithm used 22 clinical variables for hourly predictions of AKI as defined by the Kidney Disease: Improving Global Outcomes guidelines. Data for algorithm development were sourced from the Massachusetts Institute of Technology Lab for Computational Physiology Medical Information Mart for Intensive Care IV clinical database, focusing on ICU patients aged 18 years or older.ResultsThe developed algorithm, NAVOY Acute Kidney Injury, uses 4 hours of input and can, with high accuracy, predict patients with a high risk of developing AKI 12 hours before onset. The prediction performance compares well with previously published prediction algorithms designed to predict AKI onset in accordance with Kidney Disease: Improving Global Outcomes diagnosis criteria, with an impressive area under the receiver operating characteristics curve (AUROC) of 0.91 and an area under the precision-recall curve (AUPRC) of 0.75. The algorithm's predictive performance was externally validated on an independent hold-out test data set, confirming its ability to predict AKI with exceptional accuracy.ConclusionsNAVOY Acute Kidney Injury is an important development in the field of critical care medicine. It offers the ability to predict the onset of AKI with high accuracy using only 4 hours of data routinely collected in ICU electronic health records. This early detection capability has the potential to strengthen patient monitoring and management, ultimately leading to improved patient outcomes. Furthermore, NAVOY Acute Kidney Injury has been granted Conformite Europeenne (CE)-marking, marking a significant milestone as the first CE-marked AKI prediction algorithm for commercial use in European ICUs.
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Project description:BackgroundParaquat ingestion is frequently fatal. While biomarkers of kidney damage increase during paraquat-induced acute kidney injury (AKI), significant concurrent proteinuria may alter diagnostic thresholds for diagnosis and prognosis to an unknown extent. This study evaluated the effect of albuminuria on biomarker cutoffs for diagnosis and outcome prediction.MethodsThis was a multi-centre prospective clinical study of patients following acute paraquat self-poisoning in 5 Sri Lankan hospitals. Biomarker concentrations were quantified using ELISA and microbead assays and correlated with urinary albumin. Functional-AKI was defined by the Acute Kidney Injury Network serum creatinine definition and alternatively by a ≥50% increase in serum cystatin C. Albuminuria was defined as albumin-creatinine ratio >30 mg/g. The study outcomes were compared with a retrospective analysis of a pre-clinical study of paraquat-induced nephrotoxicity with appropriate controls.ResultsAlbuminuria was detected in 34 of 50 patients, and increased with functional-AKI severity. The concentrations of uNGAL, uCysC, uClusterin, uβ2M, and uKIM-1 were higher in albuminuric compared to non-albuminuric patients (p < 0.001). Albuminuria correlated with biomarker concentration (r > 0.6, p < 0.01) and was associated with death (p = 0.006). Optimal biomarker cutoffs for prediction of death were higher in the albuminuric group. Similar outcomes with more detailed analysis were obtained in experimental paraquat nephrotoxicity.ConclusionAlbuminuria was associated with paraquat-induced nephrotoxicity and increased excretion of low-molecular weight protein biomarkers. AKI biomarker cutoffs for diagnosis, outcome prediction and AKI stratification increased in the presence of albuminuria. This may lead to over-diagnosis of AKI in conditions independently associated with proteinuria.
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Project description:BackgroundsPredicting the development of acute kidney injury (AKI) in the critical care setting is challenging. Although several biomarkers showed somewhat satisfactory performance for detecting established AKI even in a heterogeneous disease-oriented population, identification of new biomarkers that predict the development of AKI accurately is urgently required.MethodsA single-center prospective observational cohort study was undertaken to evaluate for the first time the reliability of the newly identified biomarker semaphorin 3A for AKI diagnosis in heterogeneous intensive care unit populations. In addition to five urinary biomarkers of L-type fatty acid-binding protein (L-FABP), neutrophil gelatinase-associated lipocalin (NGAL), IL-18, albumin and N-acetyl-β-d-glucosaminidase (NAG), urinary semaphorin 3A was measured at intensive care unit (ICU) admission.Results and conclusionThree hundred thirty-nine critically ill adult patients were recruited for this study. Among them, 131 patients (39%) were diagnosed with AKI by the RIFLE criteria and 66 patients were diagnosed as AKI at post-ICU admission (later-onset AKI). Eighty-four AKI patients showed worsening severity during 1 week observation (AKI progression). Although L-FABP, NGAL and IL-18 showed significantly higher area under the curve (AUC)-receiver operating characteristic (ROC) values than semaphorin 3A in detecting established AKI, semaphorin 3A was able to detect later-onset AKI and AKI progression with similar AUC-ROC values compared with the other five biomarkers [AUC-ROC (95% CI) for established AKI 0.64 (0.56-0.71), later-onset AKI 0.71 (0.64-0.78), AKI progression 0.71 (0.64-0.77)]. Urinary semaphorin 3A was not increased in non-progressive established AKI, while the other biomarkers were elevated regardless of further progression. Finally, sepsis did not have any impact on semaphorin 3A while the other urinary biomarkers were increased with sepsis. Semaphorin 3A is a new biomarker of AKI which may have a distinct predictive use for AKI progression when compared with other AKI biomarkers.
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Project description:ObjectiveStudies have shown that serum response factor (SRF) is increased in chronic kidney injury, such as diabetic nephropathy, hyperuricemic nephropathy and renal cell carcinoma. The objective is to explore the early diagnostic value of SRF in acute kidney injury (AKI).MethodsAKI-related microarray data were analyzed, and the expression and location of SRF were investigated in the early phase of AKI.ResultsBioinformatics results demonstrated that SRF was dramatically elevated 2-4 h after ischemia/reperfusion (I/R) in mouse renal tissue. In I/R rats, SRF was mostly expressed and located in renal tubular epithelial cells (TECs). SRF started to increase at 1 h, peaked at 3-9 h and started to decrease at 12 h after I/R. The areas under the ROC curve of renal SRF mRNA, renal SRF protein, urinary SRF, serum SRF and serum creatinine (Scr) were 87.9%, 83.0%, 81.3%, 78.8%, 68.8%, respectively.ConclusionSRF is remarkably upregulated in early (before 24 h) AKI and can replace Scr as a potential new early diagnostic biomarker of AKI.
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