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Predictive performance of urine neutrophil gelatinase-associated lipocalin for dialysis requirement and death following cardiac surgery in neonates and infants.

ABSTRACT: Urine neutrophil gelatinase-associated lipocalin (uNGAL) has been shown to accurately predict and allow early detection of AKI, as assessed by an increase in serum creatinine in children and adults. The present study explores the accuracy of uNGAL for the prediction of severe AKI-related outcomes in neonates and infants undergoing cardiac surgery: dialysis requirement and/or death within 30 days.Prospective, observational cohort study conducted in a tertiary referral pediatric cardiac intensive care unit, including 75 neonates and 125 infants undergoing surgery with cardiopulmonary bypass between August 1, 2010, and May 31, 2011. Urine samples were collected before surgery and at median of five time points within 48 hours of bypass. Urine NGAL was quantified as absolute concentration, creatinine-normalized concentration, and absolute excretion rate, and a clusterization algorithm was applied to the individual uNGAL kinetics. The accuracy for the prediction of the outcome was assessed using receiver-operating characteristic areas, likelihood ratios, diagnostic odds ratios, net reclassification index, integrated reclassification improvement, and number needed to screen.A total of 1176 urine samples were collected. Of all patients, 8% required dialysis and 4% died. Three clusters of uNGAL kinetics were identified, including patients with significantly different outcomes. The uNGAL level peaked between 1 and 3 hours of bypass and remained high in half of all patients who required dialysis or died. The uNGAL levels measured within 24 hours of bypass accurately predicted the outcome and performed best after normalization to creatinine, with varying cutoffs according to the time elapsed since bypass. The number needed to screen to correctly identify the risk of dialysis or death in one patient varied between 1.5 and 2.6 within 12 hours of bypass.uNGAL is a valuable predictive tool of dialysis requirement and death in neonates and infants with AKI after cardiac surgery.

SUBMITTER: Bojan M

PROVIDER: S-EPMC3913237 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Predictive performance of urine neutrophil gelatinase-associated lipocalin for dialysis requirement and death following cardiac surgery in neonates and infants.

Bojan Mirela M Vicca Stéphanie S Lopez-Lopez Vanessa V Mogenet Agnes A Pouard Philippe P Falissard Bruno B Journois Didier D

Clinical journal of the American Society of Nephrology : CJASN 20131121 2

<h4>Background and objectives</h4>Urine neutrophil gelatinase-associated lipocalin (uNGAL) has been shown to accurately predict and allow early detection of AKI, as assessed by an increase in serum creatinine in children and adults. The present study explores the accuracy of uNGAL for the prediction of severe AKI-related outcomes in neonates and infants undergoing cardiac surgery: dialysis requirement and/or death within 30 days.<h4>Design, setting, participants, & measurements</h4>Prospective, ...[more]

PMID: 24262504

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Project description:We evaluated urine NGAL as a marker of acute kidney injury in patients undergoing partial nephrectomy. We sought to identify the preoperative clinical features and surgical factors during partial nephrectomy that are associated with renal injury, as measured by increased urine NGAL vs controls.Using patients treated with radical nephrectomy or thoracic surgery as controls, we prospectively collected and analyzed urine and serum samples from patients treated with partial or radical nephrectomy, or thoracic surgery between April 2010 and April 2012. Urine was collected preoperatively and at multiple time points postoperatively. Differences in urine NGAL levels were analyzed among the 3 surgical groups using a generalized estimating equation model. The partial nephrectomy group was subdivided based on a preoperative estimated glomerular filtration rate of less than 60, or 60 ml/minute/1.73 m(2) or greater.Of 162 patients included in final analysis more than 65% had cardiovascular disease. The median estimated glomerular filtration rate was greater than 60 ml/minute/1.73 m(2) in the radical and partial nephrectomy, and thoracic surgery groups (61, 78 and 84.5 ml/minute/1.73 m(2), respectively). Preoperatively, a 10 unit increase in the estimated glomerular filtration rate was associated with a 4 unit decrease in urine NGAL in the partial nephrectomy group. Postoperatively, urine NGAL in the partial nephrectomy group was not higher than in controls and did not correlate with ischemia time. Patients with partial nephrectomy with a preoperative estimated glomerular filtration rate of less than 60 ml/minute/1.73 m(2) had higher urine NGAL postoperatively than those with a higher preoperative estimated rate.Urine NGAL does not appear to be a useful marker for detecting renal injury in healthy patients treated with partial nephrectomy. However, patients with poorer preoperative renal function have higher baseline urine levels and appear more susceptible to acute kidney injury, as detected by urine levels and Acute Kidney Injury Network criteria, than those with a normal estimated glomerular filtration rate.

Project description:Background and objectivesAKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited.Design, setting, participants, & measurementsUsing data from adult patients hospitalized with COVID-19 from five hospitals from the Mount Sinai Health System who were admitted between March 10 and December 26, 2020, we developed and validated several models (logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme GradientBoosting [XGBoost; with and without imputation]) for predicting treatment with dialysis or death at various time horizons (1, 3, 5, and 7 days) after hospital admission. Patients admitted to the Mount Sinai Hospital were used for internal validation, whereas the other hospitals formed part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vital signs within 12 hours of hospital admission.ResultsA total of 6093 patients (2442 in training and 3651 in external validation) were included in the final cohort. Of the different modeling approaches used, XGBoost without imputation had the highest area under the receiver operating characteristic (AUROC) curve on internal validation (range of 0.93-0.98) and area under the precision-recall curve (AUPRC; range of 0.78-0.82) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range of 0.85-0.87, and AUPRC range of 0.27-0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04; mean difference in AUPRC of 0.15). Features of creatinine, BUN, and red cell distribution width were major drivers of the model's prediction.ConclusionsAn XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in patients positive for COVID-19 had the best performance, as compared with standard and other machine learning models.PodcastThis article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_07_09_CJN17311120.mp3.

Dataset Information

Predictive performance of urine neutrophil gelatinase-associated lipocalin for dialysis requirement and death following cardiac surgery in neonates and infants.

Publications

Predictive performance of urine neutrophil gelatinase-associated lipocalin for dialysis requirement and death following cardiac surgery in neonates and infants.

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