Preventing Heart Failure Admission with Sodium-Glucose Cotransporter-2 Inhibitors versus Angiotensin Receptor-Neprilysin Inhibitor: A Target Trial Emulation Study
Bibliographic record
Abstract
Little is known on the real-world comparative effectiveness of sodium-glucose cotransporter-2 inhibitors (SGLT2i) versus angiotensin receptor-neprilysin inhibitor (ARNi) used for heart failure (HF) management.This study used South Korea's nationwide claims data from 2015 to 2020 to construct a population-based cohort of new users of SGLT2is or ARNi.Individuals were followed from the first prescription date of SGLT2is or ARNi until outcome occurrence, treatment switch or discontinuation, death, or end of the study period.Within the 1:1 propensity score-matched cohort, we estimated hazard ratios (HR) with 95% confidence intervals (CI) for the risk of HF admission with SGLT2is compared with ARNi using proportional subdistribution hazards model of Fine and Gray.We identified 496 propensity-score matched patient-pairs of SGLT2is and ARNi; with a mean age of 72.5 years and a male representation of 57.6%.Incidence rate of HF admission was 27.3 and 35.6 per 100 person-years in SGLT2is and ARNi group.When comparing the risk of HF admission associated with SGLT2is group with ARNi group, HR was 0.71 (95% CI 0.48-1.04).Effect modifications were observed by history of hospitalization for HF (p-for-interaction=0.002) and by recent use of renin-angiotensin-system inhibitors (p-for-interac-tion=0.005).With future studies using more recent data warranted to corroborate our study results, these preliminary findings support current guideline recommendations for HF management and further, suggest similar effectiveness between SGLT2is and ARNi in routine care settings.
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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 | 0.007 |
| 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 | 0.001 |
| 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".