A Resident-Led Quality Improvement Initiative to Accelerate Medical Therapy Implementation in Acute Heart Failure: ACCELERATE-HF
Bibliographic record
Abstract
INTRODUCTION: Guideline-directed medical therapy (GDMT) optimization and effective de-congestion are known to improve outcomes in admitted heart failure (HF) patients, yet their implementation is suboptimal. Using an HF decision aid and point-of-care ultrasound (POCUS) at admission, this resident-led quality improvement (QI) initiative aimed to improve GDMT use and reduce HF readmissions. METHODS: This prospective, single-center QI initiative compared resident-performed POCUS and HF decision-aid use (the QI arm) with routine hospital care in admitted HF patients. Assignment to the QI arm was based on the availability of residents trained to perform POCUS on admission. PRIMARY OUTCOME: 30-day hospital readmission; secondary outcomes: GDMT utilization using a standard score, adverse outcome occurrence, and 30-day mortality. RESULTS: The study was terminated early due to funding constraints. We enrolled 103 (42 in the QI arm) out of the planned 254 patients. At discharge, the QI arm had a trend towards improvement in GDMT scores (+21 ± 23% vs. +12 ± 24% in controls, p = 0.106). In particular, a numerically higher proportion of patients in the QI arm were discharged on renin-angiotensin-aldosterone blockers (70.4% vs. 51.4% of controls, p = 0.126). Adverse effect occurrence and 30-day outcomes (readmissions and mortality) were not significantly different between groups. Significantly more people in the QI arm had a transthoracic echocardiogram in the hospital (26 [65.0%] vs. control: 21 [38.9%], p = 0.012). CONCLUSION: This study showed that a resident-led QI initiative was feasible and resulted in modest improvement in GDMT use and echocardiography utilization, but without effectively altering 30-day outcomes.
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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 | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".