#1658 A patient-level simulation study assessing the clinical, economic, and environmental burden of chronic kidney disease in the Netherlands
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».