#2766 Impact of CKD screening in high-risk populations and guideline-directed therapy on CV event occurrence and costs in Europe: an IMPACT CKD analysis
Notice bibliographique
Résumé
Abstract Background and Aims Chronic kidney disease (CKD) is a growing health problem in Europe exacerbated by aging and the rise of comorbidities such as diabetes mellitus (DM) and hypertension (HTN). Patients with CKD are often multi-morbid and commonly present with cardiovascular (CV) complications. While CV events including myocardial infarction (MI), stroke, and hospitalization for heart failure (HHF) occur more often in patients with advanced CKD, patients with early-stage CKD are also at increased risk. Patients with CKD also experience an increased risk of CV-related mortality, with many patients dying from CV events prior to kidney failure and renal replacement therapy. CV events also present a significant burden to clinicians and healthcare systems due to the high costs and resource use required to manage and treat them. Despite this multidimensional burden, CKD is currently underdiagnosed in Europe, and CKD screening and treatment are rarely included in public health strategies to improve mortality and health outcomes. This study aims to explore the impact of targeted CKD screening followed by guideline-directed therapy in high-risk populations on CV event occurrence and associated treatment costs across Europe. Method The IMPACT CKD microsimulation model simulates CKD progression, CV events, and comorbidities. CKD status is assigned based on estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio levels (UACR), and CKD progression is predicted by an annual eGFR decline rate. The model was used to compare two scenarios in four country populations (Germany, Netherlands, Spain, United Kingdom [UK]): 1) targeted screening for people with DM and/or HTN followed by optimal compliance to guideline-directed therapy and 2) current practice (no screening, underdiagnosis, low treatment rates). Annual targeted screening assumed both eGFR and UACR testing. Treatment initiation for people diagnosed with CKD was based on Kidney Disease Improving Global Outcomes guidelines with a 90% compliance assumed to approximate maximum clinical benefit. Therapies were assumed to have a multiplicative treatment effect on eGFR decline and CV events. Current practice assumed observed diagnosis rates without screening and observed treatment rates in each country. For both scenarios, the model projected incidence of CV events and associated management costs in year 10 (2032) and cumulatively over the 10 simulated years (2023-2032). Results Results compare the targeted screening followed by guideline-directed treatment scenario to continuation of current practices for Germany, Netherlands, Spain, and UK (Table 1). The timely diagnosis of patients with CKD and effective treatment was associated with a reduction of 46.7-53.3%, 38.9-41.6%, and 42.1-50.5% in the number of MI, stroke, and HHF events, respectively in 2032 across the four countries. These decreases in the number of CV events were accompanied by proportional reductions in costs to treat the events. Additionally, this policy approach resulted in declines in the cumulative 10-year CV event development and associated healthcare costs, with MI having the largest reductions (40.6-47.2%), followed by HHF (37.4-43.0%), and stroke (29.8-36.1%) across the four countries. Conclusion This study illustrated that early identification of high-risk patients with CKD coupled with guideline-directed treatment would yield substantial clinical and economic benefits from reducing CV events across all four European countries. Fewer MI, stroke, and HHF events were projected in patients with CKD in both 2032 and cumulatively from 2023 to 2032 due to delayed disease progression following effective interventions. Driven by the reduced clinical burden, lower healthcare costs for CV event management were also predicted. These findings support the need for the adoption of policies for early diagnosis and treatment of CKD to slow disease progression and mitigate the growing CV disease burden among patients with CKD and for health systems. Future studies should explore the costs associated with targeted CKD screening and the guideline-directed pharmacological therapies for CKD.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».