Outcome and predictive factors of acute renal failure in the intensive care unit

Bernieh, Bassam; Hakim, M. Al; Boobes, Y.; Siemkovics, E.; Jack, H. El

doi:10.1016/j.transproceed.2004.07.016

Cited by 24 publications

(19 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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%

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

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.

show abstract

Section: Outcomes Of Acute Kidney Injurymentioning

confidence: 99%

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

show abstract

“…Probably the most well recognized complication of AKI in the ICU is the high mortality rate which exceeds 50% in the majority of studies [2][3][4][27][28][29][30][31] (Table 1). This high mortality persists despite aggressive overall ICU care and recent advances in renal replacement technology.…”

Section: Long-term Mortalitymentioning

confidence: 99%

Long-Term Outcomes of Acute Kidney Injury

Goldberg

Dennen

2008

Advances in Chronic Kidney Disease

122

View full text Add to dashboard Cite

“…It occurs mostly as part of a multiple organ dysfunction syndrome associated with the systemic inflammatory response syndrome (SIRS) and sepsis, but it can also occur as a separate event. Its incidence varies from 3 to 30% (4,(8)(9)(10)(11) with mortality ranging from 36 to 90% (1)(2)(3)(4)11,12), depending on the type of ICU or type of patient assessed and the period during which the study is conducted. One of the factors that contribute to this high incidence and mortality is the greater morbidity of the patients currently admitted to ICU.…”

Section: Introductionmentioning

confidence: 99%

Incidence, risk factors and prognostic factors of acute renal failure in patients admitted to an intensive care unit

Mataloun

Machado

Senna

et al. 2006

Braz J Med Biol Res

View full text Add to dashboard Cite

The objective of the present study was to assess the incidence, risk factors and outcome of patients who develop acute renal failure (ARF) in intensive care units. In this prospective observational study, 221 patients with a 48-h minimum stay, 18-year-old minimum age and absence of overt acute or chronic renal failure were included. Exclusion criteria were organ donors and renal transplantation patients. ARF was defined as a creatinine level above 1.5 mg/dL. Statistics were performed using Pearsons' χ 2 test, Student t-test, and Wilcoxon test. Multivariate analysis was run using all variables with P < 0.1 in the univariate analysis. ARF developed in 19.0% of the patients, with 76.19% resulting in death. Main risk factors (univariate analysis) were: higher intra-operative hydration and bleeding, higher death risk by APACHE II score, logist organ dysfunction system on the first day, mechanical ventilation, shock due to systemic inflammatory response syndrome (SIRS)/sepsis, noradrenaline use, and plasma creatinine and urea levels on admission. Heart rate on admission (OR = 1.023 (1.002-1.044)), male gender (OR = 4.275 (1.340-13642)), shock due to SIRS/sepsis (OR = 8.590 (2.710-27.229)), higher intra-operative hydration (OR = 1.002 (1.000-1004)), and plasma urea on admission (OR = 1.012 (0.980-1044)) remained significant (multivariate analysis). The mortality risk factors (univariate analysis) were shock due to SIRS/sepsis, mechanical ventilation, blood stream infection, potassium and bicarbonate levels. Only potassium levels remained significant (P = 0.037). In conclusion, ARF has a high incidence, morbidity and mortality when it occurs in intensive care unit. There is a very close association with hemodynamic status and multiple organ dysfunction.

show abstract

Outcome and predictive factors of acute renal failure in the intensive care unit

Cited by 24 publications

References 17 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

Long-Term Outcomes of Acute Kidney Injury

Incidence, risk factors and prognostic factors of acute renal failure in patients admitted to an intensive care unit

Contact Info

Product

Resources

About