Abstract P171: The “Hub and Spoke” Model of Heart Function Care Achieves Quality Index Markers
Bibliographic record
Abstract
Introduction: Heart failure (HF) affects approximately 2% of the population with major effects on morbidity and mortality. Over 80% of existing Heart Function Clinics (HFC) are located within a hospital setting. The CoHealth (Ontario) “hub-and-spoke” model encourages community-based HFCs for patients who are less complex or relatively stable while more complex/unstable patients receive care in a hospital-based HFC. The purpose of this project was to explore patient populations and achievement of established quality indicators (QIs) within a specialist-supported community-based HFC in a Family Health Team (FHT-HFC) and a HFC in a tertiary hospital (H-HFC) over 12 months. Methods: Retrospective standardized chart reviews were conducted from all 60 patients enrolled in the FHT-HFC since its inception in 2010 to 2015, and 100 patients followed in the H-HFC in 2013. QIs were measured and compared at enrollment, 6 months and 12 months. Results: Patients attending the H-HFC vs FHT-HFC had no difference in age, sex or etiology of HF. However, patients at the H-HFC were significantly more likely to have reduced LV function, have marked HF symptoms, and more likely to have a recent hospital admission. Both cohorts had multiple comorbidities, with greater frequencies of MI, ICD/CRT/pacemaker, smoking and respiratory disease in the H-HFC group. By 6 months’ post enrollment, the number of patients with a HF with a reduced ejection fraction (HFrEF) in the H-HFC (n=65) who were on an ACEi/ARB, beta-blocker, and MRA increased from 75% to 82%, 86% to 88%, and 29% to 34% (vs 99% to 99%, 77% to 89% and 33% to 39%, respectively in the FHT-HFC (n=28)). Over the 12 months following enrollment in a HFC the proportion of patients with NYHA class III symptoms decreased from 67% to 38% (H-HFC) vs 47% to 14% (FHT-HFC) (p < 0.05) and HF hospital admissions were reduced by 31% (H-HFC) vs 68% (FHT-HFC) (p < 0.05). At 12 months 34% (H-HFC) vs 20% (FHT-HFC) (p=0.07) of patients were enrolled in a cardiac rehabilitation program. Conclusion: While the H-HFC and FHT-HFC patients had similar demographics and comorbidities, H-HFC patients tended to more frequently have HFrEF, were more symptomatic and more likely to have been recently hospitalized. Participation in either the H-HFC or FHT-HFC was associated with medication optimization, decreased symptoms and fewer hospitalizations compared to the previous year. In conclusion, this retrospective study shows that the “hub and spoke” HFC model may have merit but needs further evaluation on a wider, more formal scale.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".