RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis

Hoste, Eric; Clermont, Gilles; Kersten, Alexander; Venkataraman, Ramesh; Angus, Derek C.; Bacquer, Dirk De; Kellum, John A.

doi:10.1186/cc4915

Cited by 1,249 publications

(397 citation statements)

References 35 publications

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“…It is clear that ICU and in-hospital mortality increases alongside severity of AKI. 1,4,[46][47][48][49][50][51][52][53][54][55][56] Despite the trend of lower mortality in recent years, AKI still remains an important negative prognostic factor, particularly in critically ill patients. Even small isolated increases in serum creatinine levels have an associated increase in short-term morbidity and mortality and in longer-term outcomes, including 1-year mortality; 38,48,[57][58][59][60][61][62] this is even more the case when RRT is required.…”

Section: Outcomes Of Acute Kidney Injurymentioning

confidence: 99%

“…57 Even small isolated increases in serum creatinine levels have an associated increase in short-term morbidity and mortality and in longer-term outcomes, including 1-year mortality. 1,2,4,47,58,59,70,71 'Silent and discrete' episodes of AKI in the community, therefore, require further research directed at recognition and early indentification, as intervention in this group may have a significant effect on outcomes.…”

Section: Outcomes Of Acute Kidney Injurymentioning

confidence: 99%

“…[46][47][48][49]83,[91][92][93] The definitions of both AKI and CKD are time dependent. For AKI there must be an increase in serum creatinine levels over a period of 2 (AKIN) to 7 (RIFLE) days.…”

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confidence: 99%

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Development of risk models for the prediction of new or worsening acute kidney injury on or during hospital admission: a cohort and nested study

Bedford

Stevens

Coulton

et al. 2016

Health Serv Deliv Res

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BackgroundAcute kidney injury (AKI) is a common clinical problem with significant morbidity and mortality. All hospitalised patients are at risk. AKI is often preventable and reversible; however, the 2009 National Confidential Enquiry into Patient Outcome and Death highlighted systematic failings of identification and management, and recommended risk assessment of all emergency admissions.ObjectivesTo develop three predictive models to stratify the risk of (1) AKI on arrival in hospital; (2) developing AKI during admission; and (3) worsening AKI if already present; and also to (4) develop a clinical algorithm for patients admitted to hospital and explore effective methods of delivery of this information at the point of care.Study designQuantitative methodology (1) to formulate predictive risk models and (2) to validate the models in both our population and a second population. Qualitative methodology to plan clinical decision support system (CDSS) development and effective integration into clinical care.Settings and participantsQuantitative analysis – the study population comprised hospital admissions to three acute hospitals of East Kent Hospitals University NHS Foundation Trust in 2011, excluding maternity and elective admissions. For validation in a second population the study included hospital admissions to Medway NHS Foundation Trust. Qualitative analysis – the sample consisted of six renal consultants (interviews) and six outreach nurses (focus group), with representation from all sites.Data collectionData (comprising age, sex, comorbidities, hospital admission and outpatient history, relevant pathology tests, drug history, baseline creatinine and chronic kidney disease stage, proteinuria, operative procedures and microbiology) were collected from the hospital data warehouse and the pathology and surgical procedure databases.Data analysisQuantitative – both traditional and Bayesian regression methods were used. Traditional methods were performed using ordinal logistic regression with univariable analyses to inform the development of multivariable analyses. Backwards selection was used to retain only statistically significant variables in the final models. The models were validated using actual and predicted probabilities, an area under the receiver operating characteristic (AUROC) curve analysis and the Hosmer–Lemeshow test. Qualitative – content analysis was employed.Main outcome measures(1) A clinical pratice algorithm to guide clinical alerting and risk modeling for AKI in emergency hospital admissions; (2) identification of the key variables that are associated with the risk of AKI; (3) validated risk models for AKI in acute hospital admissions; and (4) a qualitative analysis providing guidance as to the best approach to the implementation of clinical alerting to highlight patients at risk of AKI in hospitals.FindingsQuantitative – we have defined a clinical practice algorithm for risk assessment within the first 24 hours of hospital admission. Bayesian methodology enabled prediction of low risk but could not reliably identify high-risk patients. Traditional methods identified key variables, which predict AKI both on admission and at 72 hours post admission. Validation demonstrated an AUROC curve of 0.75 and 0.68, respectively. Predicting worsening AKI during admission was unsuccessful. Qualitative – analysis of AKI alerting gave valuable insights in terms of user friendliness, information availability, clinical communication and clinical responsibility, and has informed CDSS development.ConclusionsThis study provides valuable evidence of relationships between key variables and AKI. We have developed a clinical algorithm and risk models for risk assessment within the first 24 hours of hospital admission. However, the study has its limitations, and further analysis and testing, including continuous modelling, non-linear modelling and interaction exploration, may further refine the models. The qualitative study has highlighted the complexity regarding the implementation and delivery of alerting systems in clinical practice.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

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Section: Outcomes Of Acute Kidney Injurymentioning

confidence: 99%

Section: Outcomes Of Acute Kidney Injurymentioning

confidence: 99%

See 1 more Smart Citation

Development of risk models for the prediction of new or worsening acute kidney injury on or during hospital admission: a cohort and nested study

Bedford

Stevens

Coulton

et al. 2016

Health Serv Deliv Res

View full text Add to dashboard Cite

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“…The failure class of RIFLE is less susceptible to being affected by other factors that can determine minor kidney damage, and the impact on clinical outcomes is also higher as the degree of AKI by RIFLE increases (15,16). Therefore, it may be expected that the failure class would be more specific for reflecting an injury caused by exposure to a nephrotoxic agent.…”

mentioning

confidence: 99%

Multicenter Prospective Cohort Study of Renal Failure in Patients Treated with Colistin versus Polymyxin B

Rigatto

Oliveira

Neto

et al. 2016

Antimicrob Agents Chemother

120

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Nephrotoxicity is the main adverse effect of colistin and polymyxin B (PMB). It is not clear whether these two antibiotics are associated with different nephrotoxicity rates. We compared the incidences of renal failure (RF) in patients treated with colistimethate sodium (CMS) or PMB for >48 h. A multicenter prospective cohort study was performed that included patients aged >18 years. The primary outcome was renal failure (RF) according to Risk, Injury, Failure, Loss, and End-stage renal disease (RI-FLE) criteria. Multivariate analysis with a Cox regression model was performed. A total of 491 patients were included: 81 in the CMS group and 410 in the PMB group. The mean daily doses in milligrams per kilogram of body weight were 4.2 ؎ 1.3 and 2.4 ؎ 0.73 of colistin base activity and PMB, respectively. The overall incidence of RF was 16.9% (83 patients): 38.3% and 12.7% in the CMS and PMB groups, respectively (P < 0.001). In multivariate analysis, CMS therapy was an independent risk factor for RF (hazard ratio, 3.35; 95% confidence interval, 2.05 to 5.48; P < 0.001) along with intensive care unit admission, higher weight, older age, and bloodstream and intraabdominal infections. CMS was also independently associated with a higher risk of RF in various subgroup analyses. The incidence of RF was higher in the CMS group regardless of the patient baseline creatinine clearance. The development of RF during therapy was not associated with 30-day mortality in multivariate analysis. CMS was associated with significantly higher rates of RF than those of PMB. Further studies are required to confirm our findings in other patient populations.

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“…Also, in many centers nephrologists provide renal replacement therapy (though we prefer a collaborative model of shared responsibility). We should also recognize that most cases of AKI resolve prior to hospital discharge [12] and indeed nearly half of stage 1 patients never progress beyond stage 1 and most of these recover [13]. Stage 1 may not even be associated with reduced survival after controlling for severity of illness and demographics [2].…”

mentioning

confidence: 97%

A nephrologist should be consulted in all cases of acute kidney injury in the ICU: No

Kellum

Hoste

2017

Intensive Care Med

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RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis

Cited by 1,249 publications

References 35 publications

Development of risk models for the prediction of new or worsening acute kidney injury on or during hospital admission: a cohort and nested study

Development of risk models for the prediction of new or worsening acute kidney injury on or during hospital admission: a cohort and nested study

Multicenter Prospective Cohort Study of Renal Failure in Patients Treated with Colistin versus Polymyxin B

A nephrologist should be consulted in all cases of acute kidney injury in the ICU: No

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