Perceived risk profile and treatment optimization in heart failure: an analysis from BIOlogy Study to TAilored Treatment in chronic heart failure
Bibliographic record
Abstract
BACKGROUND: Achieving target doses of angiotensin-converting-enzyme inhibitor/angiotensin-receptor blockers (ACEi/ARB) and beta-blockers in heart failure with reduced ejection fraction (HFrEF) is often underperformed. In BIOlogy Study to TAilored Treatment in chronic heart failure (BIOSTAT-CHF) study, many patients were not up-titrated for which no clear reason was reported. Therefore, we hypothesized that perceived-risk profile might influence treatment optimization. METHODS: We studied 2100 patients with HFrEF (LVEF≤40%) to compare the clinical characteristics and adverse events associated with treatment up-titration (after a 3-month titration protocol) between; a) patients not reaching target doses for unclear reason; b) patients not reaching target doses due to symptoms and/or side effects; c) patients reaching target doses. RESULTS: For ACEi/ARB, (a), (b) and (c) was observed in 51.3%, 25.9% and 22.7% of patients, respectively. For beta-blockers, (a), (b) and (c) was observed in 67.5%, 20.2% and 12.3% of patients, respectively. By multinomial logistic regression analysis for ACEi/ARB, patients in group (a) and (b) had lower blood pressure and poorer renal function, and patients in group (a) were older and had lower ejection fraction. For beta-blockers, patients in group (a) and (b) had more severe congestion and lower heart rate. At 9 months, adverse events (i.e., hypotension, bradycardia, renal impairment, and hyperkalemia) occurred similarly among the three groups. CONCLUSIONS: Patients in whom clinicians did not give a reason why up-titration was missed were older and had more co-morbidities. Patients in whom up-titration was achieved did not have excess adverse events. However, from these observational findings, the pattern of subsequent adverse events among patients in whom up-titration was missed cannot be determined.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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".