Contrast-Induced Nephropathy

McCullough, Peter A.; Soman, Sandeep

doi:10.1016/j.ccc.2004.12.003

Cited by 164 publications

(123 citation statements)

References 74 publications

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“…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. 5,6,13,34,63 A study from the Medicare Sample Beneficiary Analytical File from 1992 to 2001 found that patients with AKI who required RRT had an in-hospital mortality rate of 32.9%, compared with 27.5% in patients with AKI not requiring RRT, and 4.6% in patients without AKI.…”

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%

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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%

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

“…Management of the adverse effects of contrast dye is an example of the second approach. The role of contrast dye in the occurrence of CSA-AKI is well known and as it is not avoidable, the minimum possible dose of preferably newer non-ionic contrast with lower osmolality should be used [149,150] . Timing of surgery following contrast angiography may play a role in CSA-AKI.…”

Section: Preventive Measuresmentioning

confidence: 99%

Serum creatinine role in predicting outcome after cardiac surgery beyond acute kidney injury

Najafi

2014

WJC

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Serum creatinine is still the most important determinant in the assessment of perioperative renal function and in the prediction of adverse outcome in cardiac surgery. Many biomarkers have been studied to date; still, there is no surrogate for serum creatinine measurement in clinical practice because it is feasible and inexpensive. High levels of serum creatinine and its equivalents have been the most important preoperative risk factor for postoperative renal injury. Moreover, creatinine is the mainstay in predicting risk models and risk factor reduction has enhanced its importance in outcome prediction. The future perspective is the development of new definitions and novel tools for the early diagnosis of acute kidney injury largely based on serum creatinine and a panel of novel biomarkers.

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“…It is one of the leading causes of hospital acquired renal failure 2 .In the present era of modern interventional cardiology; many patients require such procedures during their lifetime. Hence it is necessary to assess risk of development of nephropathy in such patients.…”

Section: Introductionmentioning

confidence: 99%

Risk of Contrast induced nephropathy in patients undergoing Primary coronary angioplasty

Kumar

Dev

chokkanna

2012

Int J of Biomed & Adv Res

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Background: Contrast induced nephropathy (CIN) is one of the common cause for hospital acquired renal failure in patients undergoing coronary angioplasty for acute myocardial infarction. The aim of this study was to assess the renal function in patients after primary coronary angioplasty by estimating the levels of serum creatinine (Sr. Cr) Materials & Methods: Sample sizes of 51 subjects were included for the study. Serum creatinine levels were measured before and after the procedure. The difference in Serum creatinine levels was calculated.CIN was defined as increase in Sr Cr >0.5 or >25% compared to the baseline value. Results: Out of the 51 cases, CIN occurred in 7.8% (4 cases). In addition,19.6 %( 10 cases) had preexisting renal insufficiency out of which 3 subjects developed CIN.According to recently proposed definition of contrast nephropathy 5.9 %(3 cases) and 7.8% (4 cases) were in grade 1and 2 respectively. Incidence of CIN in patients with diabetes mellitus was found to be statistically significant (p<0.05) as compared to patients without diabetes mellitus. Conclusion: CIN is a common complication of primary coronary angioplasty. Its incidence is more in patients with pre existing renal insufficiency and diabetes mellitus compared to patients with normal renal function and without any underlying risk factors.

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Contrast-Induced Nephropathy

Cited by 164 publications

References 74 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

Serum creatinine role in predicting outcome after cardiac surgery beyond acute kidney injury

Risk of Contrast induced nephropathy in patients undergoing Primary coronary angioplasty

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