MO486<i>INSIDE CKD</i>: MODELLING THE IMPACT OF IMPROVED SCREENING FOR CHRONIC KIDNEY DISEASE IN THE AMERICAS AND ASIA-PACIFIC REGION
Notice bibliographique
Résumé
Abstract Background and Aims With an estimated global prevalence of 10%, chronic kidney disease (CKD) and its associated complications place a substantial strain on healthcare systems worldwide, which is compounded by the burden of undiagnosed CKD. Early CKD diagnosis followed by guideline-recommended interventions can improve patient outcomes and reduce associated healthcare-related costs, particularly by delaying or preventing the development of complications and progression to kidney failure. Urinary albumin-to-creatinine ratio (UACR) can be used to screen for CKD, but adherence to screening recommendations is suboptimal in routine care. Inside CKD aims to model the global clinical and economic burden of CKD using country-specific, patient-level microsimulation models. We used the Inside CKD microsimulation to model the potential clinical and economic impacts of routine measurement of UACR followed by appropriate intervention in patients aged 45 years and over in the US and Canada. Method The Inside CKD microsimulation model was used to model the clinical and economic impacts associated with measurement of UACR with subsequent appropriate intervention during routine primary care visits versus current practice in individuals aged 45 years and over. The model covers the period 2020–2025. In preliminary analyses, virtual populations representing the general populations of the US and Canada were constructed using published country-specific data, including demographics, prevalence of CKD and comorbidities (type 2 diabetes, uncontrolled hypertension and heart failure), incidence of complications (heart failure, myocardial infarction, stroke and acute kidney injury) and costs associated with CKD. The model also included parameters relating to the proportion of patients who visit a primary care physician at least once a year, the proportion of patients who agreed to UACR measurements, and the diagnostic sensitivity and specificity of UACR measurements. The modelling is being expanded to additional countries in the Americas and the Asia-Pacific region. Results Preliminary results from the US and Canada show that over the 2020–2025 period routine measurement of UACR during primary care visits followed by appropriate intervention could prevent progression to CKD stages 3b–5 in approximately 1.3M patients in the US and 160 000 in Canada, compared with current clinical practice, with linear increases in the cumulative numbers of prevented cases (Figure). Associated savings in healthcare costs in 2025 are projected to be approximately US$16B in the US and C$2.5B in Canada, corresponding to a reduction in cost for that year of 4.4% and 7.4%, respectively, compared with current clinical practice. Conclusion Preliminary results from the Inside CKD microsimulation model in the US and Canada show that routine measurement of UACR with subsequent intervention in primary care would prevent progression to CKD stages 3b–5 in a substantial number of patients compared with current screening practices, and could therefore decrease associated healthcare costs considerably. This analysis is being extended to further countries in the Americas and the Asia-Pacific region.
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 | 0,003 |
| 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 | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».