The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report

The definition and classification for chronic kidney disease was proposed by the National Kidney Foundation Kidney Disease Outcomes Quality Initiative (NKF-KDOQI) in 2002 and endorsed by the Kidney Disease: Improving Global Outcomes (KDIGO) in 2004. This framework promoted increased attention to chronic kidney disease in clinical practice, research and public health, but has also generated debate. It was the position of KDIGO and KDOQI that the definition and classification should reflect patient prognosis and that an analysis of outcomes would answer key questions underlying the debate. KDIGO initiated a collaborative meta-analysis and sponsored a Controversies Conference in October 2009 to examine the relationship of estimated glomerular filtration rate (GFR) and albuminuria to mortality and kidney outcomes. On the basis of analyses in 45 cohorts that included 1,555,332 participants from general, high-risk, and kidney disease populations, conference attendees agreed to retain the current definition for chronic kidney disease of a GFR <60ml/min per 1.73m2 or a urinary albumin-to-creatinine ratio >30mg/g, and to modify the classification by adding albuminuria stage, subdivision of stage 3, and emphasizing clinical diagnosis. Prognosis could then be assigned based on the clinical diagnosis, stage, and other key factors relevant to specific outcomes. KDIGO has now convened a workgroup to develop a global clinical practice guideline for the definition, classification, and prognosis of chronic kidney disease

Early Recognition of Burn- and Trauma-Related Acute Kidney Injury: A Pilot Comparison of Machine Learning Techniques.

Severely burned and non-burned trauma patients are at risk for acute kidney injury (AKI). The study objective was to assess the theoretical performance of artificial intelligence (AI)/machine learning (ML) algorithms to augment AKI recognition using the novel biomarker, neutrophil gelatinase associated lipocalin (NGAL), combined with contemporary biomarkers such as N-terminal pro B-type natriuretic peptide (NT-proBNP), urine output (UOP), and plasma creatinine. Machine learning approaches including logistic regression (LR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and deep neural networks (DNN) were used in this study. The AI/ML algorithm helped predict AKI 61.8 (32.5) hours faster than the Kidney Disease and Improving Global Disease Outcomes (KDIGO) criteria for burn and non-burned trauma patients. NGAL was analytically superior to traditional AKI biomarkers such as creatinine and UOP. With ML, the AKI predictive capability of NGAL was further enhanced when combined with NT-proBNP or creatinine. The use of AI/ML could be employed with NGAL to accelerate detection of AKI in at-risk burn and non-burned trauma patients

Lupus nephritis management guidelines compared

In the past years, many (randomized) trials have been performed comparing the treatment strategies for lupus nephritis. In 2012, these data were incorporated in six different guidelines for treating lupus nephritis. These guidelines are European, American and internationally based, with one separate guideline for children. They offer information on different aspects of the management of lupus nephritis including induction and maintenance treatment of the different histological classes, adjunctive treatment, monitoring of the patient, definitions of response and relapse, indications for (repeat) renal biopsy, and additional challenges such as the presence of vascular complications, the pregnant SLE patient, treatment in children and adolescents and considerations about end-stage renal disease and transplantation. In this review, we summarize the guidelines, determine the common ground between them, highlight the differences and discuss recent literature

Association of the KDIGO Risk Classification with the Prevalence of Heart Failure in Patients with Type 2 Diabetes

[Abstract] The objectives of this study were to determine the main characteristics associated with the presence of heart failure (HF) in patients with type 2 diabetes (T2DM), and specifically to assess the association of the risk classification proposed by the Kidney Disease Improving Global Outcomes (KDIGO) guidelines with HF. The DIABET-IC study is a multicentre, observational, prospective and analytical study in T2DM patients recruited in Spanish hospitals. This work, which features a cross-sectional design, has been conducted with the data obtained at the inclusion visit. The main dependent variable analysed was the presence of HF. The predictive variables evaluated were the demography, clinic, laboratory testing (including natriuretic peptides) and echocardiography. Patients were classified according to the number of vascular territories with atherosclerotic involvement and the KDIGO risk category. Multivariate logistic regression models were performed to determine the risk posed by the various baseline variables to present HF at the time of study inclusion. The study included 1517 patients from 58 hospitals, with a mean age of 67.3 (standard deviation (SD): 10) years, out of which 33% were women. The mean DM duration was 14 (SD: 11) years. The prevalence of HF was 37%. In a multivariate analysis, the independent predictors of HF were increased age (odds ratio (OR) per 1 year = 1.02; p = 0.006), decreased systolic blood pressure (OR per 1 mmHg = 0.98; p 1 territory = 2.39; p = 0.02 and p < 0.001 respectively) and the KDIGO risk classification (high-risk OR = 2.46 and very high-risk OR = 3.39; p < 0.001 for both). The KDIGO risk classification is useful to screen for the presence of HF in T2DM patients. Therefore, we believe that it is necessary to carry out a systematic screening for HF in the high- and very high-risk KDIGO categories.This research was funded by the Spanish Society of Diabetes (SED) and the Spanish Society of Cardiology (SEC

Phenotype standardization for drug-induced kidney disease.

Drug-induced kidney disease is a frequent cause of renal dysfunction; however, there are no standards to identify and characterize the spectrum of these disorders. We convened a panel of international, adult and pediatric, nephrologists and pharmacists to develop standardized phenotypes for drug-induced kidney disease as part of the phenotype standardization project initiated by the International Serious Adverse Events Consortium. We propose four phenotypes of drug-induced kidney disease based on clinical presentation: acute kidney injury, glomerular, tubular, and nephrolithiasis, along with the primary and secondary clinical criteria to support the phenotype definition, and a time course based on the KDIGO/AKIN definitions of acute kidney injury, acute kidney disease, and chronic kidney disease. Establishing causality in drug-induced kidney disease is challenging and requires knowledge of the biological plausibility for the specific drug, mechanism of injury, time course, and assessment of competing risk factors. These phenotypes provide a consistent framework for clinicians, investigators, industry, and regulatory agencies to evaluate drug nephrotoxicity across various settings. We believe that this is the first step to recognizing drug-induced kidney disease and developing strategies to prevent and manage this condition

Comparing Results of Five Glomerular Filtration Rate-Estimating Equations in the Korean General Population. MDRD Study, Revised Lund-Malmö, and Three CKD-EPI Equations

Estimated glomerular filtration rate (eGFR) is a widely used index of kidney function. Recently, new formulas such as the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations or the Lund-Malmö equation were introduced for assessing eGFR. We compared them with the Modification of Diet in Renal Disease (MDRD) Study equation in the Korean adult population. METHODS: The study population comprised 1,482 individuals (median age 51 [42-59] yr, 48.9% males) who received annual physical check-ups during the year 2014. Serum creatinine (Cr) and cystatin C (CysC) were measured. We conducted a retrospective analysis using five GFR estimating equations (MDRD Study, revised Lund-Malmö, and Cr and/or CysC-based CKD-EPI equations). Reduced GFR was defined as eGFR &lt;60 mL/min/1.73 m². RESULTS: For the GFR category distribution, large discrepancies were observed depending on the equation used; category G1 (≥90 mL/min/1.73 m²) ranged from 7.4-81.8%. Compared with the MDRD Study equation, the other four equations overestimated GFR, and CysC-based equations showed a greater difference (-31.3 for CKD-EPI(CysC) and -20.5 for CKD-EPI(Cr-CysC)). CysC-based equations decreased the prevalence of reduced GFR by one third (9.4% in the MDRD Study and 2.4% in CKD-EPI(CysC)). CONCLUSIONS: Our data shows that there are remarkable differences in eGFR assessment in the Korean population depending on the equation used, especially in normal or mildly decreased categories. Further prospective studies are necessary in various clinical settings

Empagliflozin and Cardiovascular and Kidney Outcomes across KDIGO Risk Categories: Post Hoc Analysis of a Randomized, Double-Blind, Placebo-Controlled, Multinational Trial

BACKGROUND AND OBJECTIVES: In the Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG Outcome), empagliflozin, in addition to standard of care, significantly reduced risk of cardiovascular death by 38%, hospitalization for heart failure by 35%, and incident or worsening nephropathy by 39% compared with placebo in patients with type 2 diabetes and established cardiovascular disease. Using EMPA-REG Outcome data, we assessed whether the Kidney Disease Improving Global Outcomes (KDIGO) CKD classification had an influence on the treatment effect of empagliflozin. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Patients with type 2 diabetes, established atherosclerotic cardiovascular disease, and eGFR≥30 ml/min per 1.73 m2 at screening were randomized to receive empagliflozin 10 mg, empagliflozin 25 mg, or placebo once daily in addition to standard of care. Post hoc, we analyzed cardiovascular and kidney outcomes, and safety, using the two-dimensional KDIGO classification framework. RESULTS: Of 6952 patients with baseline eGFR and urinary albumin-creatinine ratio values, 47%, 29%, 15%, and 8% were classified into low, moderately increased, high, and very high KDIGO risk categories, respectively. Empagliflozin showed consistent risk reductions across KDIGO categories for cardiovascular outcomes (P values for treatment by subgroup interactions ranged from 0.26 to 0.85) and kidney outcomes (P values for treatment by subgroup interactions ranged from 0.16 to 0.60). In all KDIGO risk categories, placebo and empagliflozin had similar adverse event rates, the notable exception being genital infection events, which were more common with empagliflozin for each category. CONCLUSIONS: The observed effects of empagliflozin versus placebo on cardiovascular and kidney outcomes were consistent across the KDIGO risk categories, indicating that the effect of treatment benefit of empagliflozin was unaffected by baseline CKD status. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: EMPA-REG OUTCOME, NCT01131676

Urinary chitinase 3-like protein 1 for early diagnosis of acute kidney injury : a prospective cohort study in adult critically ill patients

Background: Acute kidney injury (AKI) occurs frequently and adversely affects patient and kidney outcomes, especially when its severity increases from stage 1 to stages 2 or 3. Early interventions may counteract such deterioration, but this requires early detection. Our aim was to evaluate whether the novel renal damage biomarker urinary chitinase 3-like protein 1 (UCHI3L1) can detect AKI stage >= 2 more early than serum creatinine and urine output, using the respective Kidney Disease vertical bar Improving Global Outcomes (KDIGO) criteria for definition and classification of AKI, and compare this to urinary neutrophil gelatinase-associated lipocalin (UNGAL). Methods: This was a translational single-center, prospective cohort study at the 22-bed surgical and 14-bed medical intensive care units (ICU) of Ghent University Hospital. We enrolled 181 severely ill adult patients who did not yet have AKI stage >= 2 based on the KDIGO criteria at time of enrollment. The concentration of creatinine (serum, urine) and CHI3L1 (serum, urine) was measured at least daily, and urine output hourly, in the period from enrollment till ICU discharge with a maximum of 7 ICU-days. The concentration of UNGAL was measured at enrollment. The primary endpoint was the development of AKI stage >= 2 within 12 h after enrollment. Results: After enrollment, 21 (12 %) patients developed AKI stage >= 2 within the next 7 days, with 6 (3 %) of them reaching this condition within the first 12 h. The enrollment concentration of UCHI3L1 predicted the occurrence of AKI stage >= 2 within the next 12 h with a good AUC-ROC of 0.792 (95 % CI: 0.726-0.849). This performance was similar to that of UNGAL (AUC-ROC of 0.748 (95 % CI: 0.678-0.810)). Also, the samples collected in the 24-h time frame preceding diagnosis of the 1st episode of AKI stage >= 2 had a 2.0 times higher (95 % CI: 1.3-3.1) estimated marginal mean of UCHI3L1 than controls. We further found that increasing UCHI3L1 concentrations were associated with increasing AKI severity. Conclusions: In this pilot study we found that UCHI3L1 was a good biomarker for prediction of AKI stage >= 2 in adult ICU patients