MO509: Reveal-CKD: Prevalence of Undiagnosed Stage 3 Chronic Kidney Disease Italy
Notice bibliographique
Résumé
Abstract BACKGROUND AND AIMS Chronic kidney disease (CKD) is a serious progressive disease with a substantial impact on global health that affects ∼10% of the world's population. However, CKD remains largely under-recognized. Effective actions to slow disease progression and improve outcomes depend on timely detection and diagnosis before a further decline in estimated glomerular filtration rate (eGFR). The aim of the REVEAL-CKD study is to assess the prevalence of, and factors associated with, undiagnosed early (stage 3) CKD. METHOD REVEAL-CKD is a multi-national, multi-regional observational study using secondary data from electronic medical records and claims databases. In this analysis, we extracted data regarding patient demographics, laboratory tests, diagnoses and treatments from an Italian electronic medical record database, the Italian Longitudinal Patients Database (IQVIA, Italy). The Italian Longitudinal Patients Database consists of anonymised patient records collected from routine visits to general practitioners and represents ∼900 general practitioners and 1.2 million patients across Italy. The study cohort included patients aged ≥18 years between 2015–21 with two consecutive estimated glomerular filtration rate (eGFR) results ≥30 and <60 mL/min/1.73 m2 recorded >90 and ≤730 days apart. The date of the second qualifying eGFR was defined as the index date. Patients with no presence of a CKD diagnostic code recorded any time before their first qualifying eGFR and up to 6 months after their second qualifying eGFR were considered to be undiagnosed. The prevalence of undiagnosed CKD was calculated as the ratio of undiagnosed patients to all patients who met the study inclusion criteria. RESULTS The study cohort included 65 676 patients who met the eGFR criteria for stage 3 CKD. The mean age at index date was 79 years (standard deviation: 9 years) and 58% were female. The overall prevalence of undiagnosed CKD was 77.0% [95% confidence interval (CI): 76.6–77.3]. The prevalence of undiagnosed CKD was greater in patients with stage 3a CKD (83.0%) compared with those with stage 3b CKD (64.8%). Female patients and those aged > 65 years showed a higher prevalence of undiagnosed CKD, and the prevalence of undiagnosed CKD ranged from 66.6% to 75.7% in patients with pre-existing comorbidities (Table 1). In patients who were undiagnosed at the index date (n = 52 533), 15.5% (n = 8152) were diagnosed with CKD after the index date, with a median time to diagnosis of 404 days (IQR: 389–418 days); 84.5% (n = 44 381) remained undiagnosed. CONCLUSION This study indicates that a high proportion of patients with stage 3 CKD are undiagnosed by their general practitioners, with inequity noted for females and older patients. Underdiagnosis of CKD persisted in those with known risk factors for CKD such as diabetes, heart failure and hypertension. With the availability of targeted evidence-based therapies to decrease the risk of disease progression and improve patient outcomes, there is a clear need to proactively detect, diagnose and intervene in patients with early-stage 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 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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».