Independent predictors of heart failure in patients with type 2 diabetes and chronic kidney disease: modeling from the CREDENCE trial
Bibliographic record
Abstract
Abstract Background SGLT2 inhibitors have been shown to reduce hospitalization for heart failure (HHF). We sought to determine independent baseline predictors for HHF specifically in a population with type 2 diabetes and chronic kidney disease (CKD). Methods CREDENCE randomized 4401 participants with type 2 diabetes and CKD to canagliflozin 100 mg versus placebo. We evaluated the baseline clinical and demographic factors using multivariate regression modeling to identify the independent predictors of HHF. Results Overall, 230 participants (89 canagliflozin; 141 placebo) had at least 1 HHF event. Canagliflozin reduced the incidence of HHF compared with placebo (4.0% vs 6.4%; HR 0.61; 95% CI 0.47–0.80). Participants with HHF events postrandomization were older (65.8 vs 62.9 y), and had a longer duration of diabetes (17.4 vs 15.7 y), higher prevalence of prior HF (30.4% vs 14.0%), higher urinary albumin:creatinine ratio (1347 vs 904 mg/g), lower estimated glomerular filtration rate (51.5 vs 56.4 mL/min/1.73m2), and higher prevalence of prior cardiovascular disease (65.7% vs 49.6%) compared to those without HHF. Independent predictors of HHF are shown in the Table. Conclusions HHF is common in patients with type 2 diabetes and CKD. Canagliflozin reduces HHF by 39% compared with placebo. Higher urinary albumin:creatinine ratio was the most potent predictor of HHF and should be part of patient risk assessment. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Janssen Research & Development, 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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".