#4271 THE GROWING BURDEN OF CHRONIC KIDNEY DISEASE IN THE UK: AN IMPACT CKD ANALYSIS
Bibliographic record
Abstract
Abstract Background and Aims Chronic kidney disease (CKD) is a major source of morbidity and mortality, with an increasing incidence and prevalence worldwide. Patients with CKD experience diminished quality of life associated with increased risk of cardiovascular (CV) events, acute kidney injury (AKI), and reduced renal function. Late-stage CKD (stage 5) is also associated with significant economic burden related to renal replacement therapy (RRT). Public health and policy planning should consider the broader burden of CKD, including its societal and environmental burden, in addition to its clinical impacts and direct costs. Hence, IMPACT CKD aims to quantify the clinical, economic, humanistic, societal, and environmental burden of CKD in the United Kingdom (UK). Method A patient-level simulation model was developed to simulate the UK population using parameter data from published literature, national statistics, and health surveys. Individuals were assigned key characteristics associated with CKD, such as estimated glomerular filtration rate (eGFR), albuminuria, co-morbidities (e.g., diabetes, hypertension, heart failure), and prior CV events (e.g., myocardial infarction and stroke). Individuals were categorized as not having CKD (i.e., non-CKD) or CKD stage 1, 2, 3a, 3b, 4 or 5, based on their eGFR and albuminuria levels. Among those with CKD, patients were either classified as diagnosed or undiagnosed. Progression through the CKD stages was predicted by the simulated patient's annual eGFR rate of decline. Risk of CV and AKI events, as well as co-morbidity development were also considered in the model. Clinical progression and outcomes for the CKD population were simulated over 10 years. CKD prevalence was projected by stage and diagnosis status, as well as associated CKD and RRT costs, productivity losses from patients and caregivers, and environmental impacts as determined by CO2 emissions. Extensive validation and calibration was conducted. Results From 2022 to 2032, the prevalence of CKD is expected to increase by 4% from 8.27 million to 8.61 million people in the UK. Growth in the CKD population is driven by eGFR decline and increases in albuminuria, related to kidney function decline and AKI as the model population ages. In 2032, the prevalence of CKD by stage is projected to be 30.36%, 21.07%, 29.78%, 11.86%, 4.15%, and 2.78% in stage 1, 2, 3a, 3b, 4, and 5 patients, respectively. The diagnosed CKD population is projected to be 32.43% of the total CKD population and is primarily composed of CKD stage 3a/b, 4, and 5 patients in 2032. Patients with CKD receiving RRT are projected to increase by 44% from 73,365 in 2022 to 105,860 in 2032. The increase in late-stage CKD population is associated with an increase in RRT costs from £1.09 billion in 2022 to £1.85 billion in 2032. Over the 10-year time horizon, CKD is projected to result in 81.60 million missed workdays in diagnosed patients with CKD, and 11.89 million missed workdays by caregivers of patients with CKD. Environmental impacts equivalent to 1.35 million tonnes of CO2 emissions for patients receiving in-centre hemodialysis are predicted; however, the total environmental impact would likely be larger if the total CKD care pathway was included. Conclusion The IMPACT CKD model forecasts the prevalence and burden of CKD to remain high in the UK over the next ten years. In addition to the significant clinical burden and direct costs, CKD was also associated with extensive productivity loss and detrimental environmental impact. The model provides a validated framework for testing the sensitivity of the projections to data uncertainty, thereby identifying areas for further research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".