Filtration Markers in Acute Kidney Injury

Different methods for assessing glomerular filtration rate in the elderly

OBJECTIVE

The objective of this study was to identify the best method to replace cystatin C in the evaluation of glomerular filtration in the elderly.

METHODS

Individuals over 60 years of age from a primary care center were studied. Blood was collected to determine creatinine and cystatin C and 24-h urine. Three methods were compared to determine glomerular filtration: Creatinine clearance, Cocroft-Gault, modification of diet in renal disease, and Collaboration Epidemiology of Chronic Kidney Disease based on creatinine, considering as a reference the determination of glomerular filtration using the cystatin-based Chronic Kidney Disease Epidemiology Collaboration equation. The statistical methods used were linear regression, Bland-Altman curve, and receiver operating characteristic.

RESULTS

A total of 180 elderly people were evaluated, but 14 patients were lost from the sample, resulting in a total of 166 patients. The average age of patients was 66.9±6.1 years, and 69.8% were females. Regarding the number of patients eligible for the study, there were 12 black, 108 brown, and 46 white, 42.77% hypertensive, and 38.3% diabetic. Glomerular filtration was less than 60 mL/min in 22.28% of patients. Regarding the evaluation of the different equations, the correlation coefficient was lower for creatinine clearance and progressively higher for Cocroft-Gault, modification of diet in renal disease, and Collaboration Epidemiology of Chronic Kidney Disease based on creatinine. The Bland-Altman diagram and the receiver operating characteristic curve showed similar performance to the correlation coefficient for the different equations evaluated.

CONCLUSION

Collaboration Epidemiology of Chronic Kidney Disease based on creatinine presented the best performance. Creatinine debug had the worst performance, which reinforces the idea that 24-h urine collection is unnecessary in these patients.

Results from the IRoc-GN international registry of patients with COVID-19 and glomerular disease suggest close monitoring

The effects of SARS-CoV-2 infection on individuals with immune-mediated glomerulonephritis, who are often undergoing immunosuppressive treatments, are unknown. Therefore, we created the International Registry of COVID infection in glomerulonephritis (IRoc-GN) and identified 40 patients with glomerulonephritis and COVID-19 followed in centers in North America and Europe. Detailed information on glomerulonephritis diagnosis, kidney parameters, and baseline immunosuppression prior to infection were recorded, as well as clinical presentation, laboratory values, treatment, complications, and outcomes of COVID-19. This cohort was compared to 80 COVID-positive control cases from the general population without glomerulonephritis matched for the time of infection. The majority (70%) of the patients with glomerulonephritis and all the controls were hospitalized. Patients with glomerulonephritis had significantly higher mortality (15% vs. 5%, respectively) and acute kidney injury (39% vs. 14%) than controls, while the need for kidney replacement therapy was not statistically different between the two groups. Receiving immunosuppression or renin-angiotensin-aldosterone system inhibitors at presentation did not increase the risk of death or acute kidney injury in the glomerulonephritis cohort. In the cohort with glomerulonephritis, lower serum albumin at presentation and shorter duration of glomerular disease were associated with greater risk of acute kidney injury and need for kidney replacement therapy. No differences in outcomes occurred between patients with primary glomerulonephritis versus glomerulonephritis associated with a systemic autoimmune disease (lupus or vasculitis). Thus, due to the higher mortality and risk of acute kidney injury than in the general population without glomerulonephritis, patients with glomerulonephritis and COVID-19 should be carefully monitored, especially when they present with low serum albumin levels.

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

Background

Acute 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.

Objectives

To 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 design

Quantitative 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 participants

Quantitative 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 collection

Data (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 analysis

Quantitative – 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.

Findings

Quantitative – 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.

Conclusions

This 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.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

Cystatin C in prediction of acute kidney injury: a systemic review and meta-analysis

BACKGROUND

Cystatin C (CysC) has been proposed as a filtration marker for the early detection of acute kidney injury (AKI); however, a wide range of its predictive accuracy has been reported.

STUDY DESIGN

Meta-analysis of diagnostic test studies.

SETTING & POPULATION

Various clinical settings of AKI, including patients after cardiac surgery, pediatric patients, and critically ill patients.

SELECTION CRITERIA

Computerized search of PubMed, Current Contents, CINAHL, and EMBASE from inception until November 15, 2010, was performed to identify potentially relevant articles. Inclusion criteria were studies investigating the diagnostic accuracy of CysC level to predict AKI. There were no language restrictions in the search.

INDEX TESTS

Increasing or increased serum CysC level or urinary CysC excretion.

REFERENCE TESTS

The outcome was the development of AKI, primarily based on serum creatinine level (definition varied across studies).

RESULTS

We analyzed data from 19 studies and 11 countries involving 3,336 patients. Of these studies, 13 could be included in the meta-analysis. Across all settings, the diagnostic OR for serum CysC level to predict AKI was 27.7 (95% CI, 12.8-59.8), with sensitivity and specificity of 0.86 and 0.82, respectively. The area under the receiver operating characteristic curve (AUROC) of serum CysC levelto predict AKI was 0.87 (95% CI, 0.81-0.93). In an analysis excluding studies that did not clearly define the measurement time point, early serum CysC (within 24 hours after renal insult or intensive care unit admission) remained of diagnostic value. For the diagnostic value of urinary CysC excretion, the diagnostic OR was 3.10 (95% CI, 2.00-4.81), with sensitivity and specificity of0.61 and 0.67, respectively. TheAUROC of urinary CysC excretion to predict AKI was 0.67 (95% CI, 0.63-0.71) [corrected].

LIMITATIONS

Variation in criteria for definitions of index and reference tests, absence of measured glomerular filtration rate in most studies.

CONCLUSION

Serum CysC appears to be a good biomarker in the prediction of AKI, whereas urinary CysC excretion has only moderate diagnostic value.