REVEAL-CKD: Prevalence of Undiagnosed Early CKD in France and Japan
Bibliographic record
Abstract
Background: Screening and monitoring of at-risk populations, such as those with type 2 diabetes (T2D), is necessary for early detection and management of chronic kidney disease (CKD). Global prevalence of undiagnosed early CKD and associated factors have not been recently studied. The objective of the REVEAL-CKD study is to assess the prevalence of undiagnosed stage 3 (S3) CKD. Methods: REVEAL-CKD is a multi-national, multi-region secondary data study. Data for the French study cohort was extracted from THIN Cegedim (Cegedim Health Data, Boulogne-Billancourt, France) an electronic medical record (EMR) database from outpatient primary care practices. RWD database (Real World Data Co., Ltd., Kyoto, Japan) linking hospital systems' EMR with reimbursement claims, was used for the Japanese cohort. The study population included patients aged ≥18 years between 2015-2020 with two consecutive estimated glomerular filtration rate (eGFR) readings ≥30 and <60 mL/min/1.73 m2 recorded >90 and ≤730 days apart. Undiagnosed CKD was defined as the absence of an associated CKD diagnosis code any time before 12 months prior to the first eGFR measurement and up to 6 months after the second eGFR. Presence of recorded urine albumin-to-creatinine ratio (UACR) was also assessed. Results: After applying the eligibility criteria the study cohorts included 23,160 patients in France and 90,902 in Japan, and the proportions of patients with undiagnosed S3 CKD were 95.4% (95% confidence interval [CI] 95.1, 95.7) and 92.1% (91.9, 92.3), respectively. Prevalence in both cohorts was consistent across subgroups stratified by age (45-65, and >65 y), sex, and presence of comorbidities (T2D, HTN, and heart failure) with the exception of T2D in Japan, where undiagnosed prevalence was 82% (95% CI 81.9, 83.0). Only 2.4% of patients in the cohort in France and 5.5% in Japan had a record of a UACR value. Conclusions: The results presented here indicate that a high proportion of early CKD patients in France and Japan are undiagnosed, with a very low frequency of UACR testing. With the advent of promising novel therapies to mitigate disease progression in patients at risk and the potential to improve patient outcomes, a clear imperative exists to highlight the importance of early CKD detection, diagnosis, and intervention. Funding: Commercial Support - AstraZeneca
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".