Abstract 4147428: Breaking the Cycle: 'Snak-Chat' Intervention Transforms Heart Failure Management with MRA Optimization
Bibliographic record
Abstract
Introduction: Heart failure (HF) remains an epidemic with high rates of hospitalization and mortality. Despite the proven efficacy of guideline directed medical therapy (GDMT), there is disparity between guidelines and real-world implementation. Of the pillars of GDMT, initiation and uptitration of mineralocorticoid antagonists (MRAs) pose challenges related to hyperkalemia. To address this, we developed an interdisciplinary intervention called "Snak-Chat" aimed at facilitating MRA optimization using a stepwise approach involving collaboration with a dietitian, and monitoring via our local remote patient monitoring application, Medly. This study aims to assess the rate of MRA optimization, incidences of hospitalization, and improvements in ejection fraction (EF). Hypothesis: The implementation of the Snak-Chat strategy will lead to a higher rate of MRA optimization, a reduction in hospitalization rates and improvements in ejection fraction (EF) among HF patients. Methods: This is a single center study of adult patients with HFrEF and history of hyperkalemia. Baseline potassium level, LVEF, and MRA dose were obtained. Patients were followed for one year, and MRA dose optimization, hyperkalemic incidents, HF-related hospitalization, and mortality were compared and matched to a standard-of-care (SoC) cohort followed on Medly. Time-to-event analyses were performed using Kaplan-Meier for mortality and Fine and Gray’s subdistribution methods for reaching MRA optimization and the incidents of hyperkalemia, and HF-related hospitalization. Between-group differences in mortality and cumulative incidence rates were evaluated using log-rank and Gray’s tests. Results: A total of 185 patients were included, with 51 in the intervention arm. Baseline potassium levels (5.1 vs. 4.2 mmol/L, p<0.0001) and hyperkalemic events (68.8% vs. 10.5%, p<0.0001) were significantly higher in the intervention group prior to enrollment. After a 12-month follow-up, there was a substantial increase in reaching MRA optimization (Figure 1A) with an associated improved LVEF (30.2% to 38.5%, p=0.0003) in the intervention group. There was a significantly lower rate of hospitalization in the intervention arm and no differences in the number of hyperkalemic or mortality events (Figure 1B-D). Conclusion: Our targeted interdisciplinary approach to MRA optimization led to near-complete optimization of MRA and improved EF, without an increase in hyperkalemic events and resulted in lower hospitalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".