Guideline-Directed Medical Therapy Use in the STRONG-HF Trial
Bibliographic record
Abstract
BACKGROUND: Assessment of medication changes in heart failure trials and registries is complex and may not capture the entirety of care. A comprehensive and standardized method is needed. We used different methods to assess the use of guideline-directed medical therapies (GDMT) and verified the association between GDMT intensity score with the STRONG-HF trial (Safety, Tolerability and Efficacy of Rapid Optimization, Helped by NT-proBNP Testing of Heart Failure Therapies) clinical outcomes. METHODS: We used data from the STRONG-HF trial to examine the baseline GDMT use for all randomized patients by applying the GDMT intensity score and evaluated its change over time. We also examined their basic adherence, indication-corrected adherence, and dose-corrected adherence, and the association with clinical outcomes up to 180 days. RESULTS: At 90 days, triple therapy indication-corrected use increased from 4.5% to 36% in the usual care group, and from 5.2% to 93.5% in the high-intensity care group ( P <0.001 between the 2 groups). Triple therapy dose-corrected use increased from 4.5% to 20.5% in the usual care group, and from 3.3% to 77.4% in the high-intensity care group ( P <0.001). The GDMT intensity score at baseline was <6 in 358 (33%) patients, 6 to 7 in 329 (31%) patients, and >7 in 386 (36%) patients. At 90 days, 88.4% of patients in the high-intensity arm achieved a score >7 versus 14.3% in the usual care arm ( P <0.0001). The GDMT intensity score was correlated with clinical outcomes at 180 days. CONCLUSIONS: The GDMT intensity score provides a comprehensive description of medication use by means of standardized measurements and is linked to clinical outcomes. Future studies should consider utilizing this as a trial end point. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03412201.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".