REVEAL-CKD: Management and Monitoring of Patients With CKD Stage 3 in France, Germany, Italy, Japan, and the United States
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is vastly under-recognised yet affects 11.1% of the global population. Early diagnosis and active management can slow disease progression. This study assessed CKD management and monitoring in patients with CKD stage 3. Methods: REVEAL-CKD is a multi-national, observational study, using medical record and claims data from the general population. Data were extracted from six databases from France, Germany, Italy, Japan, and the USA. Included patients were aged ≥18 years with 2 consecutive eGFR values ≥30 and <60 mL/min/1.73m2 recorded 91-730 days apart between 2015-2020. The date of the second qualifying eGFR was the index date. Patients with no CKD diagnosis code before and up to 6 months after index were considered undiagnosed. Data on selected quality indicators were extracted from 6-months post-index and the proportion of diagnosed and undiagnosed patients meeting these indicators was calculated. Results: Across the six databases cohort sizes were 20,012-250,879 patients with mean ages of 71-80 years; 41.9-52.9% were male, and median index eGFR was 49-52 mL/min/1.73m2. Prevalence of undiagnosed CKD ranged from 61.6 to 95.5%. UACR monitoring and treatment with SGLT2i was low across databases; UACR was notably 3-fold higher for patients with diagnosed CKD in Japan. Across all countries, blood pressure monitoring, and treatment with ACEi/ARB and statins was greater in patients with diagnosed CKD (Image 1). Conclusions: In five countries, a large proportion of patients with CKD stage 3 are undiagnosed and do not receive timely CKD management and monitoring, however, a greater proportion of patients meet care quality indicators when they have a CKD diagnosis. There is a clear need to proactively diagnose early-stage CKD so that patients can receive guideline-directed monitoring and treatments to improve outcomes. 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".