BLITZ‐HF: a nationwide initiative to evaluate and improve adherence to acute and chronic heart failure guidelines
Bibliographic record
Abstract
AIMS: To assess adherence to guideline recommendations among a large network of Italian cardiology sites in the management of acute and chronic heart failure (HF) and to evaluate if an ad-hoc educational intervention can improve their performance on several pharmacological and non-pharmacological indicators. METHODS AND RESULTS: BLITZ-HF was a cross-sectional study based on a web-based recording system with pop-up reminders on guideline recommendations used during two 3-month enrolment periods carried out 3 months apart (Phase 1 and 3), interspersed by face-to-face macro-regional benchmark analyses and educational meetings (Phase 2). Overall, 7218 patients with acute and chronic HF were enrolled at 106 cardiology sites. During the enrolment phases, 3920 and 3298 patients were included, respectively, 84% with chronic HF and 16% with acute HF in Phase 1, and 74% with chronic HF and 26% with acute HF in Phase 3. At baseline, adherence to guideline recommendations was already overall high for most indicators. Among acute HF patients, an improvement was obtained in three out of eight indicators, with a significant rise in echocardiographic evaluation. Among chronic HF patients with HF and preserved or mid-range ejection fraction, performance increased in two out of three indicators: creatinine and echocardiographic evaluations. An overall performance improvement was observed in six out of nine indicators in ambulatory HF with reduced ejection fraction patients with a significant increase in angiotensin receptor-neprilysin inhibitor prescription rates. CONCLUSIONS: Within a context of an already elevated level of adherence to HF guideline recommendations, a structured multifaceted educational intervention could be useful to improve performance on specific indicators. Extending this approach to other non-cardiology healthcare professionals, who usually manage patients with HF, should be considered.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".