Risk of Hospitalization for Heart Failure (HHF) by eGFR and Urinary Albumin-to-Creatinine Ratio (UACR): Pooled Analyses from the CANVAS Program and CREDENCE
Bibliographic record
Abstract
Background: People with type 2 diabetes (T2D) are at particularly elevated risk of cardiovascular (CV) events including heart failure (HF) if they have chronic kidney disease (CKD). Albuminuria and eGFR are each associated with increased risk, leading to recommendations for annual assessment of these parameters. We analyzed the combined effects of eGFR and UACR on risk of HHF, and the effect of canagliflozin (CANA) on reducing risk, in patients with T2D using pooled data from the CANVAS Program and CREDENCE. Methods: The CANVAS Program enrolled 10,142 patients with T2D and CV disease or high CV risk. CREDENCE enrolled 4401 patients with T2D and CKD. Risk of HHF was examined in subgroups by baseline eGFR (<45, 45-60, and >60mL/min/1.73m2) and UACR (<30, 30-300, and >300mg/g). Hazard ratios (HR) and 95% CI were estimated using a Cox proportional hazards model. Results: In placebo-treated participants (Figure), the risk of HHF was generally lowest in people with UACR <30 and eGFR >60, and highest in those with eGFR <45 and UACR >300. HHF rates increased 6.5-fold between those with UACR <30 and >300 and eGFR >60 at baseline and almost 10-fold as eGFR declined from >60 to <45 in patients with UACR <30 at baseline. CANA reduced the risk of HHF overall with some evidence of treatment heterogeneity by UACR and eGFR (P interaction=0.0218).Figure.: HHF by eGFR and UACR.Conclusions: People with T2D and reduced eGFR, increased albuminuria, and especially both, were at increased risk of HHF. The risk of HHF was reduced overall by CANA. Funding: Commercial Support - Janssen Scientific Affairs, LLC
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.019 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".