Non-Cardiac Comorbidities and Intensive Up-Titration of Oral Treatment in Patients Recently Hospitalized for Heart Failure: Insights from the STRONG-HF Trial
Bibliographic record
Abstract
AIMS: To assess the potential interaction between non-cardiac comorbidities (NCCs) and the efficacy and safety of high-intensity care (HIC) versus usual care (UC) in the STRONG-HF trial, including stable patients with improved but still elevated natriuretic peptides. METHODS AND RESULTS: In the trial, eight NCCs were reported: anaemia, diabetes, renal dysfunction, severe liver disease, chronic obstructive pulmonary disease/asthma, stroke/transient ischaemic attack, psychiatric/neurological disorders, and malignancies. Patients were classified by NCC number (0, 1, 2 and ≥3). The treatment effect of HIC versus UC on the primary endpoint, 180-day death or heart failure (HF) rehospitalization, was compared by NCC number and by each individual comorbidity. Among the 1078 patients, the prevalence of 0, 1, 2 and ≥3 NCCs was 24.3%, 39.8%, 24.5% and 11.4%, respectively. Achievement of full doses of HF therapies at 90 and 180 days in the HIC was similar irrespective of NCC number. In HIC, the primary endpoint occurred in 10.0%, 16.6%, 13.6% and 26.2%, in those with 0, 1, 2 and ≥3 NCCs, respectively, as compared to 19.1%, 25.4%, 23.3% and 26.2% in UC (interaction-p = 0.80). The treatment benefit of HIC versus UC on the primary endpoint did not differ significantly by each individual comorbidity. There was no significant treatment interaction by NCC number in quality-of-life improvement (p = 0.98) or the incidence of serious adverse events (p = 0.11). CONCLUSIONS: In the STRONG-HF trial, NCCs neither limited the rapid up-titration of HF therapies, nor attenuated the benefit of HIC on the primary endpoint. In the context of a clinical trial, the benefit-risk ratio favours the rapid up-titration of HF therapies even in patients with multiple NCCs.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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) | 0.003 | 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".