Acute Kidney Injury Classification

Chang, Chih‐Hsiang; Lin, Chan‐Yu; Tian, Ya-Chung; Jenq, Chang‐Chyi; Chang, Ming‐Yang; Chen, Yung‐Chang; Fang, Ji‐Tseng; Yang, Chih‐Wei

doi:10.1097/shk.0b013e3181b2fe0c

Cited by 72 publications

(23 citation statements)

References 31 publications

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“…This finding corroborates other studies conducted in the ICU setting that have also evaluated parameters related to the development of kidney disorders. (5,7,8) …”

Section: Discussionmentioning

confidence: 99%

“…This finding was consistent with similar studies in the medical-scientific literature. (5-7,17,18) However, some studies have shown small differences in the ability to diagnose AKI between the RIFLE and AKIN criteria. Some of those studies referred to the RIFLE as a more sensitive predictor, (10,19) while others indicated the AKIN as a better classification tool, (20,21) although both yield very similar results regarding the prediction of death.…”

Section: Discussionmentioning

confidence: 99%

“…In this context, these values were similar to results found in the literature in studies addressing the same main topic. (5,7,22-24) …”

Section: Discussionmentioning

confidence: 99%

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Comparison of the RIFLE, AKIN and KDIGO criteria to predict mortality in critically ill patients

Levi¹,

Souza²,

Magalhães³

et al. 2013

Revista Brasileira De Terapia Intensiva

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ObjectiveAcute kidney injury is a common complication in critically ill patients, and the RIFLE, AKIN and KDIGO criteria are used to classify these patients. The present study's aim was to compare these criteria as predictors of mortality in critically ill patients.MethodsProspective cohort study using medical records as the source of data. All patients admitted to the intensive care unit were included. The exclusion criteria were hospitalization for less than 24 hours and death. Patients were followed until discharge or death. Student's t test, chi-squared analysis, a multivariate logistic regression and ROC curves were used for the data analysis.ResultsThe mean patient age was 64 years old, and the majority of patients were women of African descent. According to RIFLE, the mortality rates were 17.74%, 22.58%, 24.19% and 35.48% for patients without acute kidney injury (AKI) in stages of Risk, Injury and Failure, respectively. For AKIN, the mortality rates were 17.74%, 29.03%, 12.90% and 40.32% for patients without AKI and at stage I, stage II and stage III, respectively. For KDIGO 2012, the mortality rates were 17.74%, 29.03%, 11.29% and 41.94% for patients without AKI and at stage I, stage II and stage III, respectively. All three classification systems showed similar ROC curves for mortality.ConclusionThe RIFLE, AKIN and KDIGO criteria were good tools for predicting mortality in critically ill patients with no significant difference between them.

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“…This finding corroborates other studies conducted in the ICU setting that have also evaluated parameters related to the development of kidney disorders. (5,7,8) …”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of the RIFLE, AKIN and KDIGO criteria to predict mortality in critically ill patients

Levi¹,

Souza²,

Magalhães³

et al. 2013

Revista Brasileira De Terapia Intensiva

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“…Ketamine has been reported to exert anti-inflammatory effects on macrophages stimulated with lipopolysaccharide (LPS) in vitro and in vivo , with involvement of toll-like receptors (TLRs) and MAPK pathways (JNK and ERK) [23,32]. Glial cells, the "immunocytes" of the CNS, somewhat like macrophages, have been confirmed to be involved in the immune response in neuropathic pain condition [33].…”

Section: Discussionmentioning

confidence: 99%

Inhibition of spinal astrocytic c-Jun N-terminal kinase (JNK) activation correlates with the analgesic effects of ketamine in neuropathic pain

Mei

Zhang

Wang

et al. 2011

J Neuroinflammation

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BackgroundWe have previously reported that inhibition of astrocytic activation contributes to the analgesic effects of intrathecal ketamine on spinal nerve ligation (SNL)-induced neuropathic pain. However, the underlying mechanisms are still unclear. c-Jun N-terminal kinase (JNK), a member of mitogen-activated protein kinase (MAPK) family, has been reported to be critical for spinal astrocytic activation and neuropathic pain development after SNL. Ketamine can decrease lipopolysaccharide (LPS)-induced phosphorylated JNK (pJNK) expression and could thus exert its anti-inflammatory effect. We hypothesized that inhibition of astrocytic JNK activation might be involved in the suppressive effect of ketamine on SNL-induced spinal astrocytic activation.MethodsImmunofluorescence histochemical staining was used to detect SNL-induced spinal pJNK expression and localization. The effects of ketamine on SNL-induced mechanical allodynia were confirmed by behavioral testing. Immunofluorescence histochemistry and Western blot were used to quantify the SNL-induced spinal pJNK expression after ketamine administration.ResultsThe present study showed that SNL induced ipsilateral pJNK up-regulation in astrocytes but not microglia or neurons within the spinal dorsal horn. Intrathecal ketamine relieved SNL-induced mechanical allodynia without interfering with motor performance. Additionally, intrathecal administration of ketamine attenuated SNL-induced spinal astrocytic JNK activation in a dose-dependent manner, but not JNK protein expression.ConclusionsThe present results suggest that inhibition of JNK activation may be involved in the suppressive effects of ketamine on SNL-induced spinal astrocyte activation. Therefore, inhibition of spinal JNK activation may be involved in the analgesic effects of ketamine on SNL-induced neuropathic pain.

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“…It is also associated with increased mortality and a longer stay in the hospital [1-7]. There have been many definitions, such as acute renal failure and renal impairment, and this has made it difficult to compare results across studies.…”

Section: Introductionmentioning

confidence: 99%

A comparison of different diagnostic criteria of acute kidney injury in critically ill patients

et al. 2014

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IntroductionRecently, the Kidney Disease: Improving Global Outcomes (KDIGO) proposed a new definition and classification of acute kidney injury (AKI) on the basis of the RIFLE (Risk, Injury, Failure, Loss of kidney function, and End-stage renal failure) and AKIN (Acute Kidney Injury Network) criteria, but comparisons of the three criteria in critically ill patients are rare.MethodsWe prospectively analyzed a clinical database of 3,107 adult patients who were consecutively admitted to one of 30 intensive care units of 28 tertiary hospitals in Beijing from 1 March to 31 August 2012. AKI was defined by the RIFLE, AKIN, and KDIGO criteria. Receiver operating curves were used to compare the predictive ability for mortality, and logistic regression analysis was used for the calculation of odds ratios and 95% confidence intervals.ResultsThe rates of incidence of AKI using the RIFLE, AKIN, and KDIGO criteria were 46.9%, 38.4%, and 51%, respectively. KDIGO identified more patients than did RIFLE (51% versus 46.9%, P = 0.001) and AKIN (51% versus 38.4%, P <0.001). Compared with patients without AKI, in-hospital mortality was significantly higher for those diagnosed as AKI by using the RIFLE (27.8% versus 7%, P <0.001), AKIN (32.2% versus 7.1%, P <0.001), and KDIGO (27.4% versus 5.6%, P <0.001) criteria, respectively. There was no difference in AKI-related mortality between RIFLE and KDIGO (27.8% versus 27.4%, P = 0.815), but there was significant difference between AKIN and KDIGO (32.2% versus 27.4%, P = 0.006). The areas under the receiver operator characteristic curve for in-hospital mortality were 0.738 (P <0.001) for RIFLE, 0.746 (P <0.001) for AKIN, and 0.757 (P <0.001) for KDIGO. KDIGO was more predictive than RIFLE for in-hospital mortality (P <0.001), but there was no difference between KDIGO and AKIN (P = 0.12).ConclusionsA higher incidence of AKI was diagnosed according to KDIGO criteria. Patients diagnosed as AKI had a significantly higher in-hospital mortality than non-AKI patients, no matter which criteria were used. Compared with the RIFLE criteria, KDIGO was more predictive for in-hospital mortality, but there was no significant difference between AKIN and KDIGO.

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Acute Kidney Injury Classification

Cited by 72 publications

References 31 publications

Comparison of the RIFLE, AKIN and KDIGO criteria to predict mortality in critically ill patients

Comparison of the RIFLE, AKIN and KDIGO criteria to predict mortality in critically ill patients

Inhibition of spinal astrocytic c-Jun N-terminal kinase (JNK) activation correlates with the analgesic effects of ketamine in neuropathic pain

A comparison of different diagnostic criteria of acute kidney injury in critically ill patients

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