#1658 A patient-level simulation study assessing the clinical, economic, and environmental burden of chronic kidney disease in the Netherlands
Bibliographic record
Abstract
Abstract Background and Aims The increasing prevalence of chronic kidney disease (CKD) presents significant challenges for individuals and societies, driven by an aging population and rising rates of comorbidities associated with CKD. The Dutch healthcare system faces increasing pressure due to an aging population and rising comorbidities associated with CKD, necessitating effective resource allocation and management strategies. Understanding the implications of CKD on healthcare capacity, ecological impact, and economic burden is critical for effective resource allocation and management. The aim of this study, IMPACT-CKD, was to quantify the burden of CKD on clinical, patient, health system and environmental outcomes within the Dutch healthcare context. Methods A patient-level simulation model was developed to project CKD-related outcomes over a 10-year horizon using data from registries including the Dutch renal registry and published literature. The simulation included one million patients and modeled disease progression across CKD stages 1 to 5, dialysis, and kidney transplantation. Decline in estimated glomerular filtration rate was calculated using regression equations for patients with CKD and annual decline rates for non-CKD individuals. The model incorporated clinical events such as cardiovascular events, acute kidney injury, mortality, the incidence of dialysis and transplantation, and development of comorbidities including heart failure. Extensive validation and calibration were conducted to ensure alignment with known population data, including the prevalence of dialysis and kidney transplantation. Results From 2022 to 2032, the number of patients in CKD stages 3 to 5 in the Netherlands is projected to increase by 46.6%, resulting in an estimated total CKD prevalence of approximately 2.6 million individuals, or 14.5% of the population. This increase is expected to drive a corresponding rise in demand for dialysis by 3.6% and for kidney transplantation by 50.2%. Furthermore, there is an increase of 83.4% in number of acute kidney injury cases in CKD patients. Additionally, the burden of CKD-related cardiovascular events and mortality is projected to grow by 81.9%. CKD patients are anticipated to account for 10% of annual emergency room visits, 17% of hospital admissions, and 44% of outpatient visits. Also, there will be an increase of annual incidence of heart failure with 1.32% compared to 0.44% in the general population. Number of annual HF cases in CKD patients to rise 57.9% by 2032. This all contributes significantly to healthcare resource utilization. The economic burden is substantial, with CKD-related healthcare costs projected to represent 2.4% of the total healthcare budget, including an annual cost of €537 million attributed to dialysis alone. Environmental impacts of CKD management are also significant. An average increase of 48%–53% in freshwater use, fossil fuel depletion, and carbon dioxide emissions is expected, with 66–82% of these impacts attributable to dialysis. Conclusion The projected increase in CKD prevalence, particularly in the later stages of the disease, highlights significant challenges for the Dutch healthcare system, patients, caregivers, and society at large. Additionally, the economic burden underscores the need for strategic financial planning to ensure sustainable healthcare delivery. The environmental impact of CKD management, particularly in dialysis, demands urgent consideration of greener healthcare solutions and sustainable practices. These findings emphasize the importance of developing holistic strategies that align with the objectives of the IMPACT-CKD workstream that address the multifaceted challenges of CKD while promoting environmental sustainability and equitable healthcare delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".