#4271 THE GROWING BURDEN OF CHRONIC KIDNEY DISEASE IN THE UK: AN IMPACT CKD ANALYSIS
Notice bibliographique
Résumé
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.
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 | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».