Abstract 11716: The Impact of a Virtual Multi-Disciplinary Heart Failure Optimization Program on Achievement of Guideline-Directed Therapy
Bibliographic record
Abstract
Introduction: Current guidelines for management of Heart Failure with Reduced Ejection Fraction (HFrEF) recommend Beta-blockers, Angiotensin Receptor Neprilysin Inhibitors (ARNI), Mineralocorticoid Receptor Antagonists (MRA) and SGLT2 inhibitors. However, guideline-directed medical therapies (GDMT) remain underutilized. The 2018 CHAMP-HF registry of HFrEF patients demonstrated that only 1% of patients were treated with target doses of Beta-blockers, MRA and ACE-inhibitors (ACE-I) and Angiotensin Receptor Blocker (ARB) or ARNI. Methods: We conducted a single center study of a virtual, multidisciplinary HFrEF optimization program for achieving GDMT. Patients referred from both inpatient and outpatient settings were enrolled in this 3-month initiative. After an initial cardiology consult, patients were seen virtually once weekly by a kinesiologist for lifestyle optimization and virtually by a nurse once every two weeks for up-titration of guideline-based therapies, with cardiologist oversight. Vital signs, serum creatinine and electrolytes were obtained after medication adjustment. Results: Over 9 months, 297 patients were referred and all enrolled in the virtual HFrEF optimization program. Mean age was 69 and 63% were male. Mean ejection fraction was 28% and 54% had ischemic cardiomyopathy. At initial visit, the frequency of patients prescribed optimal dosage for Beta-Blockers was 64%, 7% for MRA and 1% for SGLT2 inhibitors. 88% were prescribed ACE-I or ARB while 1% of patients were on ARNI. At 3 month follow-up, the frequency of patients prescribed optimal dosage for Beta-Blockers was 84% (p<0.01), 58% for MRA (p<0.01), 77% for SGLT2 inhibitors (p<0.01) and 96% for ARNI (p<0.01). Overall, 39% of patients achieved maximal doses of all 4 classes of medications at 3 month follow-up. During the study period, there were no hospitalizations or medication adverse events among participants. The most common causes of failure to achieve GDMT was chronic kidney disease or drug cost or coverage. Conclusions: This study demonstrates that a virtual multidisciplinary program can safely and effectively improve usage of GDMT in HFrEF patients. Further studies should examine the effect of similar interventions on patient outcomes in a randomized format.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".