Guideline Implementation, Drug Sequencing, and Quality of Care in Heart Failure: Design and Rationale of TITRATE-HF
Bibliographic record
Abstract
AIMS: Current heart failure (HF) guidelines recommend to prescribe four drug classes in patients with HF with reduced ejection fraction (HFrEF). A clear challenge exists to adequately implement guideline-directed medical therapy (GDMT) regarding the sequencing of drugs and timely reaching target dose. It is largely unknown how the paradigm shift from a serial and sequential approach for drug therapy to early parallel application of the four drug classes will be executed in daily clinical practice, as well as the reason clinicians may not adhere to new guidelines. We present the design and rationale for the real-world TITRATE-HF study, which aims to assess sequencing strategies for GDMT initiation, dose titration patterns (order and speed), intolerance for GDMT, barriers for implementation, and long-term outcomes in patients with de novo, chronic, and worsening HF. METHODS AND RESULTS: A total of 4000 patients with HFrEF, HF with mildly reduced ejection fraction, and HF with improved ejection fraction will be enrolled in >40 Dutch centres with a follow-up of at least 3 years. Data collection will include demographics, physical examination and vital parameters, electrocardiogram, laboratory measurements, echocardiogram, medication, and quality of life. Detailed information on titration steps will be collected for the four GDMT drug classes. Information will include date, primary reason for change, and potential intolerances. The primary clinical endpoints are HF-related hospitalizations, HF-related urgent visits with a need for intravenous diuretics, all-cause mortality, and cardiovascular mortality. CONCLUSIONS: TITRATE-HF is a real-world multicentre longitudinal registry that will provide unique information on contemporary GDMT implementation, sequencing strategies (order and speed), and prognosis in de novo, worsening, and chronic HF patients.
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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 | 0.206 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".