Design and Baseline Characteristics of the FIND-CKD Trial: Efficacy of Finerenone on Kidney Disease Progression in People with Non-Diabetic CKD
Bibliographic record
Abstract
Background: Finerenone, a selective, nonsteroidal mineralocorticoid receptor antagonist, improved kidney and cardiovascular (CV) outcomes in people with CKD and type 2 diabetes (T2D) in two phase 3 outcome trials. However, CKD is due to non-diabetic etiologies in most people across many parts of the world. The effects of finerenone on kidney outcomes in people with CKD without diabetes are being investigated in the FIND-CKD trial (NCT05047263 and EudraCT: 2021-000421-27). Methods: FIND-CKD is a randomized, double-blind, and placebo-controlled phase 3 trial for people with CKD of non-diabetic etiology. People with a urine albumin-to-creatinine ratio of ≥200-≤3500 mg/g and estimated glomerular filtration rate (eGFR) ≥25-<90 ml/min/1.73 m2 at screening are randomized 1:1 to once daily 10 or 20 mg finerenone or placebo on top of optimized renin-angiotensin system blockade. The primary efficacy outcome is total eGFR slope, defined as the mean annual rate of change in eGFR from baseline to month 32. Secondary efficacy outcomes include a combined cardiorenal composite outcome comprising time to kidney failure, sustained ≥57% decrease in eGFR, hospitalization for heart failure or CV death, as well as separate kidney and CV composite outcomes. Adverse events are recorded to assess tolerability and safety. Results: The FIND-CKD trial will study the efficacy and safety of finerenone in people with non-diabetic causes of CKD at high risk of disease progression. The first patient was enrolled in September 2021 and patient enrolment completed in May 2023. Baseline clinical characteristics and demographics will be presented. Conclusions: FIND-CKD is the first phase 3 trial of finerenone in people with CKD of non-diabetic etiology. This trial will determine the potential expanded role for finerenone for the treatment of CKD beyond T2D. Funding: Commercial Support - The study and this analysis were funded by Bayer AG, Wuppertal, Germany. Medical writing and/or editorial assistance was provided by Charlotte Simpson, PhD, and Melissa Ward, BA, both of Scion, London, UK. This assistance was funded by Bayer AG, Wuppertal Germany according to Good Publication Practice guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".