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Diagnostic and prognostic stratification in the emergency department using urinary biomarkers of nephron damage: a multicenter prospective cohort study.

ABSTRACT: This study aimed to determine the diagnostic and prognostic value of urinary biomarkers of intrinsic acute kidney injury (AKI) when patients were triaged in the emergency department.Intrinsic AKI is associated with nephron injury and results in poor clinical outcomes. Several urinary biomarkers have been proposed to detect and measure intrinsic AKI.In a multicenter prospective cohort study, 5 urinary biomarkers (urinary neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, urinary liver-type fatty acid binding protein, urinary interleukin-18, and cystatin C) were measured in 1,635 unselected emergency department patients at the time of hospital admission. We determined whether the biomarkers diagnosed intrinsic AKI and predicted adverse outcomes during hospitalization.All biomarkers were elevated in intrinsic AKI, but urinary neutrophil gelatinase-associated lipocalin was most useful (81% specificity, 68% sensitivity at a 104-ng/ml cutoff) and predictive of the severity and duration of AKI. Intrinsic AKI was strongly associated with adverse in-hospital outcomes. Urinary neutrophil gelatinase-associated lipocalin and urinary kidney injury molecule 1 predicted a composite outcome of dialysis initiation or death during hospitalization, and both improved the net risk classification compared with conventional assessments. These biomarkers also identified a substantial subpopulation with low serum creatinine at hospital admission, but who were at risk of adverse events.Urinary biomarkers of nephron damage enable prospective diagnostic and prognostic stratification in the emergency department.

SUBMITTER: Nickolas TL

PROVIDER: S-EPMC3487165 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Diagnostic and prognostic stratification in the emergency department using urinary biomarkers of nephron damage: a multicenter prospective cohort study.

Nickolas Thomas L TL Schmidt-Ott Kai M KM Canetta Pietro P Forster Catherine C Singer Eugenia E Sise Meghan M Elger Antje A Maarouf Omar O Sola-Del Valle David Antonio DA O'Rourke Matthew M Sherman Evan E Lee Peter P Geara Abdallah A Imus Philip P Guddati Achuta A Polland Allison A Rahman Wasiq W Elitok Saban S Malik Nasir N Giglio James J El-Sayegh Suzanne S Devarajan Prasad P Hebbar Sudarshan S Saggi Subodh J SJ Hahn Barry B Kettritz Ralph R Luft Friedrich C FC Barasch Jonathan J

Journal of the American College of Cardiology 20120101 3

<h4>Objectives</h4>This study aimed to determine the diagnostic and prognostic value of urinary biomarkers of intrinsic acute kidney injury (AKI) when patients were triaged in the emergency department.<h4>Background</h4>Intrinsic AKI is associated with nephron injury and results in poor clinical outcomes. Several urinary biomarkers have been proposed to detect and measure intrinsic AKI.<h4>Methods</h4>In a multicenter prospective cohort study, 5 urinary biomarkers (urinary neutrophil gelatinase- ...[more]

PMID: 22240130

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Project description:BackgroundUrinary tract infection (UTI) is a common emergency department (ED) diagnosis with reported high diagnostic error rates. Because a urine culture, part of the gold standard for diagnosis of UTI, is usually not available for 24-48 hours after an ED visit, diagnosis and treatment decisions are based on symptoms, physical findings, and other laboratory results, potentially leading to overutilization, antibiotic resistance, and delayed treatment. Previous research has demonstrated inadequate diagnostic performance for both individual laboratory tests and prediction tools.ObjectiveOur aim, was to train, validate, and compare machine-learning based predictive models for UTI in a large diverse set of ED patients.MethodsSingle-center, multi-site, retrospective cohort analysis of 80,387 adult ED visits with urine culture results and UTI symptoms. We developed models for UTI prediction with six machine learning algorithms using demographic information, vitals, laboratory results, medications, past medical history, chief complaint, and structured historical and physical exam findings. Models were developed with both the full set of 211 variables and a reduced set of 10 variables. UTI predictions were compared between models and to proxies of provider judgment (documentation of UTI diagnosis and antibiotic administration).ResultsThe machine learning models had an area under the curve ranging from 0.826-0.904, with extreme gradient boosting (XGBoost) the top performing algorithm for both full and reduced models. The XGBoost full and reduced models demonstrated greatly improved specificity when compared to the provider judgment proxy of UTI diagnosis OR antibiotic administration with specificity differences of 33.3 (31.3-34.3) and 29.6 (28.5-30.6), while also demonstrating superior sensitivity when compared to documentation of UTI diagnosis with sensitivity differences of 38.7 (38.1-39.4) and 33.2 (32.5-33.9). In the admission and discharge cohorts using the full XGboost model, approximately 1 in 4 patients (4109/15855) would be re-categorized from a false positive to a true negative and approximately 1 in 11 patients (1372/15855) would be re-categorized from a false negative to a true positive.ConclusionThe best performing machine learning algorithm, XGBoost, accurately diagnosed positive urine culture results, and outperformed previously developed models in the literature and several proxies for provider judgment. Future prospective validation is warranted.

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Diagnostic and prognostic stratification in the emergency department using urinary biomarkers of nephron damage: a multicenter prospective cohort study.

Publications

Diagnostic and prognostic stratification in the emergency department using urinary biomarkers of nephron damage: a multicenter prospective cohort study.

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