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Enregistrement W2122687675 · doi:10.1002/hep.27063

Kidney damage biomarkers: Novel tools for the diagnostic assessment of acute kidney injury in cirrhosis

2014· letter· en· W2122687675 sur OpenAlexaffabout
Florence Wong, Patrick Murray

Notice bibliographique

RevueHepatology · 2014
Typeletter
Langueen
DomaineMedicine
ThématiqueLiver Disease and Transplantation
Établissements canadiensToronto General HospitalUniversity of Toronto
Organismes subventionnairesHealth Research Board
Mots-clésAcute kidney injuryCirrhosisMedicineKidneyNephrologyIntensive care medicineInternal medicinePathologyUrologyGastroenterology

Résumé

récupéré en direct d'OpenAlex

Renal dysfunction is a common complication of liver cirrhosis, occurring in approximately 20% of all patients with cirrhosis admitted into hospital.1 The majority of these cases are acute episodes of renal dysfunction, more commonly known as acute kidney injury (AKI). Two thirds of these AKI episodes are functional in nature, related to hemodynamic changes in cirrhosis, consisting of splanchnic and systemic arterial vasodilatation with resultant reduction in effective arterial blood volume,2 whereas the remainder of AKI episodes are related to renal structural damage, more commonly tubular than glomerular.1 The most severe form of AKI is type 1 hepatorenal syndrome (HRS-1),3 which, if left untreated, has a median survival of 7-10 days.4 Pharmacotherapy using vasoconstrictors for HRS-1 has led to improvement in renal function in approximately one third of patients.5, 6 The reasons for this low response rate to vasoconstrictor therapy are unclear. Many clinicians believe that vasoconstrictor therapy for HRS-1 may be started too late in the course of the natural history of renal dysfunction, and therefore many patients may have crossed the threshold of “no return.” This may be related to the fact that serum creatinine, the commonly used index of renal function, tends to overestimate the glomerular filtration rate in decompensated cirrhosis,7 and this may contribute to a delay in initiating treatment for HRS-1. Prolonged renal ischemia in late-presenting HRS-1 can also lead to structural damage, such as acute tubular necrosis (ATN), as recent data have suggested,8, 9 thus blurring the line between structural and functional causes of AKI in cirrhosis. In addition, ATN can present with the same features as HRS-1,10 thus making the differentiation of the different types of AKI and decisions on treatment plans difficult. To overcome some of these issues, the International Ascites Club and the Acute Dialysis Quality Initiative recently modified the AKI diagnostic criteria of the Acute Kidney Injury Network (AKIN) for the population with cirrhosis11 as an acute rise in serum creatinine by 26.4 μmol/L (0.3 mg/dL) in <48 hours or a 50% increase from baseline irrespective of the final serum creatinine level. Patients diagnosed with AKI with these new diagnostic criteria had a worse survival, whether in an out-12 or inpatient13 setting. This means that treatment intervention at an earlier stage of renal dysfunction could potentially improve patient outcome. However, the AKIN diagnostic criteria also provide guidelines to define staging and progression of AKI, which further improves prognostic implications.14 Despite their improvement over serum creatinine in characterizing AKI in cirrhosis, none of these diagnostic criteria can differentiate between the different types of AKI nor can they predict the progression of AKI. The recent emergence of novel biomarkers capable of identifying renal tubular damage as indicators of kidney injury has led to some progress in the phenotyping and subclassification of AKI cases (Fig. 1). Indeed, several investigators have reported on the success of one particular renal biomarker, neutrophil gelatinase-associated lipocalin (NGAL), in the early diagnosis of AKI in cirrhosis.16-18 The urine usually does not contain NGAL in significant amounts, and its urinary appearance indicates either a failure to reabsorb filtered NGAL caused by proximal tubular structural damage or induction of renal tubular NGAL production in more distal tubular injury, as opposed to the functional impairment marked by increases in serum urea, creatinine, and cystatin C. Therefore, the article by Belcher et al. in this issue of Hepatology is a significant advancement on the use of renal biomarkers in the diagnosis of the etiology of AKI in patients with cirrhosis,19 because they employed a panel of biomarkers to evaluate both glomerular and tubular function, as well as tubular damage, and found this approach to be particularly useful in separating ATN from other causes of AKI. The researchers used urinary albumin and the urinary fractional excretion of sodium as glomerular and tubular functional biomarkers, respectively, although emerging data suggest that the development of albuminuria may represent a failure of constitutive reabsorption of filtered albumin by injured proximal tubules, rather than simply reflecting increased glomerular permeability.20 The four specific tubular damage biomarkers studied were NGAL, kidney injury molecule 1 (KIM-1), interleukin-18 (IL-18), and liver type fatty acid binding protein (L-FABP), and these are still research tools in most countries.21 Similar to NGAL, L-FABP, KIM-1, and IL-18 are constitutively produced by the renal tubular cells in response to some form of renal insult, be it ischemic or toxic. Therefore, their appearance in the urine is also abnormal and signifies renal tubular damage. However, there are some limitations to the specificity of these tests as pure renal tubular damage markers. For example, NGAL is produced by neutrophils, and its production is induced by bacterial infection; therefore, high levels can be observed in patients with any infection, especially urinary tract infection, even in the absence of AKI. IL-18 is also induced by endotoxemia, as well as inflammatory and autoimmune diseases, and therefore its sensitivity and specificity as a renal tubular damage marker is significantly reduced. However, by combining these four biomarkers, the test performance for diagnosing ATN is significantly improved. In fact, the more biomarkers that are present in elevated amounts, the more likely the patient is to have ATN and less likely to have functional causes of AKI, such as prerenal azotemia or HRS-1. Urinary albumin performs similarly well in distinguishing ATN from other forms of progressive AKI, probably (as discussed above) by reflecting proximal tubular injury (and failure to reabsorb filtered albumin) in ATN,20 rather than increased glomerular permeability, as intended. Because numerous other novel candidate AKI biomarkers are similarly in various stages of development and validation, there appears to be a glimmer of hope in sorting out the different types of AKI when a patient with cirrhosis presents with elevated serum creatinine. These biomarkers are easily obtainable noninvasively. Functional biomarkers can be applied at the bedside. Tubular damage biomarkers are still not readily available commercially worldwide, but are in development on a variety of standardized laboratory and point-of-care platforms. At least there is a chance that clinicians in the future will be able to diagnose ATN versus HRS-1 early and therefore apply the correct medical intervention to prevent progression of AKI, perhaps using vasoconstrictor therapy only in those with HRS-1 and avoiding use in patients with ATN, in whom it may be harmful. With further refinement of the application of these biomarkers, we may be able to use them to track transition from one type of AKI to another over time or to follow response to intervention. Florence Wong, M.D., FRACP, FRCPC1 Patrick Murray, M.D., FASN, FRCPI, FJFICMI2 1Department of Medicine Toronto General Hospital University of Toronto Toronto, Ontario, Canada 2UCD School of Medicine and Medical Science Belfield Dublin, Ireland

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,203
Score d'incertitude au seuil0,831

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,031
Tête enseignante GPT0,321
Écart entre enseignants0,290 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations10
Publié2014
Routes d'admission2
Résumé présentoui

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