Accuracy of glomerular filtration rate estimation using creatinine and cystatin C for identifying and monitoring moderate chronic kidney disease: the eGFR-C study.

Background: Estimation of glomerular filtration rate using equations based on creatinine is widely used to manage chronic kidney disease. In the UK, the Chronic Kidney Disease Epidemiology Collaboration creatinine equation is recommended. Other published equations using cystatin C, an alternative marker of kidney function, have not gained widespread clinical acceptance. Given higher cost of cystatin C, its clinical utility should be validated before widespread introduction into the NHS. Objectives: Primary objectives were to: (1) compare accuracy of glomerular filtration rate equations at baseline and longitudinally in people with stage 3 chronic kidney disease, and test whether accuracy is affected by ethnicity, diabetes, albuminuria and other characteristics; (2) establish the reference change value for significant glomerular filtration rate changes; (3) model disease progression; and (4) explore comparative cost-effectiveness of kidney disease monitoring strategies. Design: A longitudinal, prospective study was designed to: (1) assess accuracy of glomerular filtration rate equations at baseline (n = 1167) and their ability to detect change over 3 years (n = 875); (2) model disease progression predictors in 278 individuals who received additional measurements; (3) quantify glomerular filtration rate variability components (n = 20); and (4) develop a measurement model analysis to compare different monitoring strategy costs (n = 875). Setting: Primary, secondary and tertiary care. Participants: Adults (≥ 18 years) with stage 3 chronic kidney disease. Interventions: Estimated glomerular filtration rate using the Chronic Kidney Disease Epidemiology Collaboration and Modification of Diet in Renal Disease equations. Main outcome measures: Measured glomerular filtration rate was the reference against which estimating equations were compared with accuracy being expressed as P30 (percentage of values within 30% of reference) and progression (variously defined) studied as sensitivity/specificity. A regression model of disease progression was developed and differences for risk factors estimated. Biological variation components were measured and the reference change value calculated. Comparative costs of monitoring with different estimating equations modelled over 10 years were calculated. Results: Accuracy (P30) of all equations was ≥ 89.5%: the combined creatinine-cystatin equation (94.9%) was superior (p < 0.001) to other equations. Within each equation, no differences in P30 were seen across categories of age, gender, diabetes, albuminuria, body mass index, kidney function level and ethnicity. All equations showed poor (< 63%) sensitivity for detecting patients showing kidney function decline crossing clinically significant thresholds (e.g. a 25% decline in function). Consequently, the additional cost of monitoring kidney function annually using a cystatin C-based equation could not be justified (incremental cost per patient over 10 years = £43.32). Modelling data showed association between higher albuminuria and faster decline in measured and creatinine-estimated glomerular filtration rate. Reference change values for measured glomerular filtration rate (%, positive/negative) were 21.5/-17.7, with lower reference change values for estimated glomerular filtration rate. Limitations: Recruitment of people from South Asian and African-Caribbean backgrounds was below the study target. Future work: Prospective studies of the value of cystatin C as a risk marker in chronic kidney disease should be undertaken. Conclusions: Inclusion of cystatin C in glomerular filtration rate-estimating equations marginally improved accuracy but not detection of disease progression. Our data do not support cystatin C use for monitoring of glomerular filtration rate in stage 3 chronic kidney disease. Trial registration: This trial is registered as ISRCTN42955626. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 11/103/01) and is published in full in Health Technology Assessment; Vol. 28, No. 35. See the NIHR Funding and Awards website for further award information.

Chronic kidney disease, which affects approximately 14% of the adult population, often has no symptoms but, in some people, may later develop into kidney failure. Kidney disease is most often detected using a blood test called creatinine. Creatinine does not identify everyone with kidney disease, or those most likely to develop more serious kidney disease. An alternative blood test called cystatin C may be more accurate, but it is more expensive than the creatinine test. We compared the accuracy of these two tests in more than 1000 people with moderate kidney disease. Participants were tested over 3 years to see if the tests differed in their ability to detect worsening kidney function. We also wanted to identify risk factors associated with loss of kidney function, and how much the tests normally vary to better understand what results mean. We compared the accuracy and costs of monitoring people with the two markers. Cystatin C was found slightly more accurate than the creatinine test at estimating kidney function when comparing the baseline single measurements (95% accurate compared to 90%), but not at detecting worsening function over time. This means that the additional cost of monitoring people over time with cystatin C to detect kidney disease progression could not be justified. Kidney test results could vary by up to 20% between tests without necessarily implying changes in underlying kidney function ­ this is the normal level of individual variation. Cystatin C marginally improved accuracy of kidney function testing but not ability to detect worsening kidney function. Cystatin C improves identification of moderate chronic kidney disease, but our results do not support its use for routine monitoring of kidney function in such patients.

The Clinical Teaching Fellow role: exploring expectations and experiences.

BACKGROUND: Many UK junior doctors are now taking a year out of the traditional training pathway, usually before specialty training, and some choose to work as a clinical teaching fellow (CTF). CTFs primarily have responsibility for delivering hospital-based teaching to undergraduate medical students. Only a very small amount of literature is available regarding CTF posts, none of which has explored why doctors choose to undertake the role and their expectations of the job. This study aimed to explore the expectations and experiences of CTFs employed at NHS hospital Trusts in the West Midlands. METHODS: CTFs working in Trusts in the West Midlands region registered as students on the Education for Healthcare Professionals Post Graduate Certificate course at the University of Birmingham in August 2019 took part in a survey and a focus group. RESULTS: Twenty-eight CTFs participated in the survey and ten participated in the focus group. In the survey, participants reported choosing a CTF role due to an interest in teaching, wanting time out of training, and being unsure of which specialty to choose. Expectations for the year in post were directly related to reasons for choosing the role with participants expecting to develop teaching skills, and have a break from usual clinical work and rotations. The focus group identified five main themes relating to experiences starting their job, time pressures and challenges faced in post, how CTF jobs differed between Trusts, and future career plans. Broadly, participants reported enjoying their year in a post at a mid-year point but identified particular challenges such as difficulties in starting the role and facing time pressures in their day-to-day work. CONCLUSION: This study has provided a valuable insight into the CTF role and why doctors choose a CTF post and some of the challenges experienced, adding to the sparse amount of literature. Understanding post holders' experiences may contribute to optimisation of the role. Those employing CTFs should consider ensuring a formal handover process is in place between outgoing and incoming CTFs, having a lead person at their Trust responsible for evaluating changes suggested by CTFs, and the balance of contractual duties and personal development time.

Translating the potential of the urine steroid metabolome to stage NAFLD (TrUSt-NAFLD): study protocol for a multicentre, prospective validation study.

INTRODUCTION: Non-alcoholic fatty liver disease (NAFLD) affects approximately one in four individuals and its prevalence continues to rise. The advanced stages of NAFLD with significant liver fibrosis are associated with adverse morbidity and mortality outcomes. Currently, liver biopsy remains the 'gold-standard' approach to stage NAFLD severity. Although generally well tolerated, liver biopsies are associated with significant complications, are resource intensive, costly, and sample only a very small area of the liver as well as requiring day case admission to a secondary care setting. As a result, there is a significant unmet need to develop non-invasive biomarkers that can accurately stage NAFLD and limit the need for liver biopsy. The aim of this study is to validate the use of the urine steroid metabolome as a strategy to stage NAFLD severity and to compare its performance against other non-invasive NAFLD biomarkers. METHODS AND ANALYSIS: The TrUSt-NAFLD study is a multicentre prospective test validation study aiming to recruit 310 patients with biopsy-proven and staged NAFLD across eight centres within the UK. 150 appropriately matched control patients without liver disease will be recruited through the Oxford Biobank. Blood and urine samples, alongside clinical data, will be collected from all participants. Urine samples will be analysed by liquid chromatography-tandem mass spectroscopy to quantify a panel of predefined steroid metabolites. A machine learning-based classifier, for example, Generalized Matrix Relevance Learning Vector Quantization that was trained on retrospective samples, will be applied to the prospective steroid metabolite data to determine its ability to identify those patients with advanced, as opposed to mild-moderate, liver fibrosis as a consequence of NAFLD. ETHICS AND DISSEMINATION: Research ethical approval was granted by West Midlands, Black Country Research Ethics Committee (REC reference: 21/WM/0177). A substantial amendment (TrUSt-NAFLD-SA1) was approved on 26 November 2021. TRIAL REGISTRATION NUMBER: ISRCTN19370855.

Urine steroid metabolomics as a diagnostic tool in primary aldosteronism.

Primary aldosteronism (PA) causes 5-10% of hypertension cases, but only a minority of patients are currently diagnosed and treated because of a complex, stepwise, and partly invasive workup. We tested the performance of urine steroid metabolomics, the computational analysis of 24-hour urine steroid metabolome data by machine learning, for the identification and subtyping of PA. Mass spectrometry-based multi-steroid profiling was used to quantify the excretion of 34 steroid metabolites in 24-hour urine samples from 158 adults with PA (88 with unilateral PA [UPA] due to aldosterone-producing adenomas [APAs]; 70 with bilateral PA [BPA]) and 65 sex- and age-matched healthy controls. All APAs were resected and underwent targeted gene sequencing to detect somatic mutations associated with UPA. Patients with PA had increased urinary metabolite excretion of mineralocorticoids, glucocorticoids, and glucocorticoid precursors. Urine steroid metabolomics identified patients with PA with high accuracy, both when applied to all 34 or only the three most discriminative steroid metabolites (average areas under the receiver-operating characteristics curve [AUCs-ROC] 0.95-0.97). Whilst machine learning was suboptimal in differentiating UPA from BPA (average AUCs-ROC 0.65-0.73), it readily identified APA cases harbouring somatic KCNJ5 mutations (average AUCs-ROC 0.79-85). These patients showed a distinctly increased urine excretion of the hybrid steroid 18-hydroxycortisol and its metabolite 18-oxo-tetrahydrocortisol, the latter identified by machine learning as by far the most discriminative steroid. In conclusion, urine steroid metabolomics is a non-invasive candidate test for the accurate identification of PA cases and KCNJ5-mutated APAs.