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 cost-effectiveness of procalcitonin for guiding antibiotic prescribing in individuals hospitalized with COVID-19: part of the PEACH study.

BACKGROUND: Many hospitals introduced procalcitonin (PCT) testing to help diagnose bacterial coinfection in individuals with COVID-19, and guide antibiotic decision-making during the COVID-19 pandemic in the UK. OBJECTIVES: Evaluating cost-effectiveness of using PCT to guide antibiotic decisions in individuals hospitalized with COVID-19, as part of a wider research programme. METHODS: Retrospective individual-level data on patients hospitalized with COVID-19 were collected from 11 NHS acute hospital Trusts and Health Boards from England and Wales, which varied in their use of baseline PCT testing during the first COVID-19 pandemic wave. A matched analysis (part of a wider analysis reported elsewhere) created groups of patients whose PCT was/was not tested at baseline. A model was created with combined decision tree/Markov phases, parameterized with quality-of-life/unit cost estimates from the literature, and used to estimate costs and quality-adjusted life years (QALYs). Cost-effectiveness was judged at a £20 000/QALY threshold. Uncertainty was characterized using bootstrapping. RESULTS: People who had baseline PCT testing had shorter general ward/ICU stays and spent less time on antibiotics, though with overlap between the groups' 95% CIs. Those with baseline PCT testing accrued more QALYs (8.76 versus 8.62) and lower costs (£9830 versus £10 700). The point estimate was baseline PCT testing being dominant over no baseline testing, though with uncertainty: the probability of cost-effectiveness was 0.579 with a 1 year horizon and 0.872 with a lifetime horizon. CONCLUSIONS: Using PCT to guide antibiotic therapy in individuals hospitalized with COVID-19 is more likely to be cost-effective than not, albeit with uncertainty.

Evidence of quality of life for hospitalised patients with COVID-19: a scoping review.

Background: Information on the quality of life of people hospitalised with COVID-19 is important, both in assessing the burden of disease and the cost-effectiveness of treatments. However, there were potential barriers to collecting such evidence. Objective: To review the existing evidence on quality of life for people hospitalised with COVID-19, with a focus on the amount of evidence available and methods used. Design: A scoping review with systematic searches. Results: A total of 35 papers were selected for data extraction. The most common study type was economic evaluation (N = 13), followed by cross-sectional (N = 10). All economic evaluations used published utility values for other conditions to represent COVID-19 inpatients' quality of life. The most popular quality-of-life survey measure was the Pittsburgh Sleep Quality Index (N = 8). There were 12 studies that used a mental health-related survey and 12 that used a sleep-related survey. Five studies used EQ-5D, but only one collected responses from people in the acute phase of COVID-19. Studies reported a negative impact on quality of life for people hospitalised with COVID-19, although many studies did not include a formal comparison group. Limitations: Although it used systematic searches, this was not a full systematic review. Conclusion: Quality-of-life data were collected from people hospitalised with COVID-19 from relatively early in the pandemic. However, there was a lack of consensus as to what survey measures to use, and few studies used generic health measures. Economic evaluations for COVID-19 treatments did not use utilities collected from people with COVID-19. In future health crises, researchers should be vigilant for opportunities to collect quality-of-life data from hospitalised patients but should try to co-ordinate as well as ensuring generic health measures are used more. Funding: This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number NIHR132254.

Quality of life can be measured using short, simple surveys. It is important to assess quality of life in this way, as it can show how health conditions affect people. Quality-of-life surveys can also be used to measure how treatments benefit people and to judge whether it is cost-effective to fund them. COVID-19 is a new disease, with new treatments developed to treat it. COVID-19 also created possible barriers to collecting quality-of-life survey data, especially from people in hospital at the start of the pandemic. This paper reviews studies which report data on quality of life for people hospitalised with COVID-19, especially how much evidence is available and how the studies were carried out. There were 35 studies included in the review. Of these, 13 assessed how cost-effective treatments for COVID-19 were. None of them collected survey responses directly from patients. Instead, they used data previously collected from people with other conditions such as influenza to represent people with COVID-19's quality of life. The studies which did collect data from patients used a wide variety of different surveys, which made comparing their results difficult. Mental health-related surveys were used by 12 studies, and a further 12 used sleep-related surveys. Relatively few studies used general surveys which could assess the overall effect of COVID-19 on people's quality of life. In future health crises, we recommend using more general quality-of-life measures. We also recommend that researchers co-ordinate to reduce the number of different surveys they use, as this will make comparing results easier.

Target Product Profile for a Machine Learning-Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study.

BACKGROUND: Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. OBJECTIVE: This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. METHODS: This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence's Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from "definitely exclude" to "definitely include," and suggest edits. The document will be iterated between rounds based on participants' feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote. RESULTS: Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024. CONCLUSIONS: The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50568.

Evidence Synthesis and Linkage for Modelling the Cost-Effectiveness of Diagnostic Tests: Preliminary Good Practice Recommendations.

OBJECTIVES: To develop preliminary good practice recommendations for synthesising and linking evidence of treatment effectiveness when modelling the cost-effectiveness of diagnostic tests. METHODS: We conducted a targeted review of guidance from key Health Technology Assessment (HTA) bodies to summarise current recommendations on synthesis and linkage of treatment effectiveness evidence within economic evaluations of diagnostic tests. We then focused on a specific case study, the cost-effectiveness of troponin for the diagnosis of myocardial infarction, and reviewed the approach taken to synthesise and link treatment effectiveness evidence in different modelling studies. RESULTS: The Australian and UK HTA bodies provided advice for synthesising and linking treatment effectiveness in diagnostic models, acknowledging that linking test results to treatment options and their outcomes is common. Across all reviewed models for the case study, uniform test-directed treatment decision making was assumed, i.e., all those who tested positive were treated. Treatment outcome data from a variety of sources, including expert opinion, were utilised for linked clinical outcomes. Preliminary good practice recommendations for data identification, integration and description are proposed. CONCLUSION: Modelling the cost-effectiveness of diagnostic tests poses unique challenges in linking evidence on test accuracy to treatment effectiveness data to understand how a test impacts patient outcomes and costs. Upfront consideration of how a test and its results will likely be incorporated into patient diagnostic pathways is key to exploring the optimal design of such models. We propose some preliminary good practice recommendations to improve the quality of cost-effectiveness evaluations of diagnostics tests going forward.

The National Health Service urgent cancer referral pathway for suspected urological cancers: early economic evaluation of a risk prediction test.

OBJECTIVES: In the UK, the number of patients urgently referred for suspected cancer is increasing, and providers are struggling to cope with demand. We explore the potential cost-effectiveness of a new risk prediction test - the PinPoint test - to triage and prioritize patients urgently referred with suspected urological cancers. METHODS: Two simulation models were developed to reflect the diagnostic pathways for patients with (i) suspected prostate cancer, and (ii) bladder or kidney cancer, comparing the PinPoint test to current practice. An early economic analysis was conducted from a UK National Health Service (NHS) perspective. The primary outcomes were the percentage of individuals seen within 2 weeks and health care costs. An exploratory analysis was conducted to understand the potential impact of the Pinpoint test on quality-adjusted life years gained. RESULTS: Across both models and applications, the PinPoint test led to more individuals with urological cancer being seen within 2 weeks. Using PinPoint only to prioritize patients led to increased costs overall, whereas using PinPoint to both triage and prioritize patients led to cost savings. The estimated impact on life years gained/lost was very small and highly uncertain. CONCLUSIONS: Using the PinPoint test to prioritize urgent referrals meant that more individuals with urological cancer were seen within 2 weeks, but at additional cost to the NHS. If used as a triage and prioritization tool, the PinPoint test shortens wait times for referred individuals and is cost saving. More data on the impact of short-term delays to diagnosis on health-related quality of life is needed.

Short-term psychosocial outcomes of adding a non-contrast abdominal computed tomography (CT) scan to the thoracic CT within lung cancer screening.

OBJECTIVES: To evaluate psychological, social, and financial outcomes amongst individuals undergoing a non-contrast abdominal computed tomography (CT) scan to screen for kidney cancer and other abdominal malignancies alongside the thoracic CT within lung cancer screening. SUBJECTS AND METHODS: The Yorkshire Kidney Screening Trial (YKST) is a feasibility study of adding a non-contrast abdominal CT scan to the thoracic CT within lung cancer screening. A total of 500 participants within the YKST, comprising all who had an abnormal CT scan and a random sample of one-third of those with a normal scan between 14/03/2022 and 24/08/2022 were sent a questionnaire at 3 and 6 months. Outcomes included the Psychological Consequences Questionnaire (PCQ), the short-form of the Spielberger State-Trait Anxiety Inventory, and the EuroQoL five Dimensions five Levels scale (EQ-5D-5L). Data were analysed using regression adjusting for participant age, sex, socioeconomic status, education, baseline quality of life (EQ-5D-5L), and ethnicity. RESULTS: A total of 380 (76%) participants returned questionnaires at 3 months and 328 (66%) at 6 months. There was no difference in any outcomes between participants with a normal scan and those with abnormal scans requiring no further action. Individuals requiring initial further investigations or referral had higher scores on the negative PCQ than those with normal scans at 3 months (standardised mean difference 0.28 sd, 95% confidence interval 0.01-0.54; P = 0.044). The difference was greater in those with anxiety or depression at baseline. No differences were seen at 6 months. CONCLUSION: Screening for kidney cancer and other abdominal malignancies using abdominal CT alongside the thoracic CT within lung cancer screening is unlikely to cause significant lasting psychosocial or financial harm to participants with incidental findings.