Abstract 14915: Efficacy of Canagliflozin on Heart Failure Hospitalization Across Diabetes-Specific Risk Scores
Bibliographic record
Abstract
Introduction: Canagliflozin (CAN) vs. placebo (PBO) reduced the risk of hospitalization for heart failure (HHF) in type 2 diabetes. Whether this benefit is uniform across diabetes-specific HF risk scores (WATCH-DM and TRS-HF DM ) is not known. Methods: Using data from the pooled CANVAS and CANVAS-R trials, we stratified participants without prevalent HF by the integer WATCH-DM score derived quintiles (low [≤14] = quintiles 1-2, intermediate [15-19] = quintiles 3-4, high [≥20] = quintile 5) and by the TRS-HF DM score (low [0-1], intermediate [2], high [3-6]). Discrimination and calibration were assessed by Harrell’s C-index and Hosmer-Lemeshow test, respectively. Cox regression models evaluated the effect of CAN on risk of HHF across risk score categories. Results: Among participants without prevalent HF (n = 8,691), CAN vs. PBO reduced the risk of HHF (HR 0.80, 95% CI 0.62-1.03). The WATCH-DM score demonstrated a C-index of 0.70 (95% CI, 0.66-0.73) and no evidence of miscalibration (χ 2 < 20). CAN consistently reduced risk of HHF across WATCH-DM strata (P-intxn = 0.55) with the greatest absolute risk reduction and lowest NNT observed in the highest vs. lowest risk cohort (ARR 4.6% vs. 0.3% and NNT 22 vs. 333) ( Fig. A ). Comparatively, the TRS-HF DM demonstrated a C-index of 0.67 (95% CI, 0.63-0.71) and no evidence of miscalibration (χ 2 < 20). Similar to WATCH-DM, patients in the highest TRS-HF DM risk group derived the greatest absolute risk reduction and lowest NNT (ARR 3.1% vs. -0.3%% and NNT 32 vs. -333) with no differences across strata (P-intxn = 0.17) ( Fig. B ). Conclusions: Both the WATCH-DM and TRS-HF DM can accurately stratify HHF risk in patients with type 2 diabetes and free of HF. Greater absolute risk reductions with CAN vs PBO were observed with higher risk scores.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".