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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".