A Population-Based Study to Evaluate the Effectiveness of Multidisciplinary Heart Failure Clinics and Identify Important Service Components
Bibliographic record
Abstract
BACKGROUND: Multidisciplinary heart failure (HF) clinics are efficacious in clinical trials. Our objectives were to compare real-world outcomes of patients with HF treated in HF clinics versus usual therapy and identify HF clinic features associated with improved outcomes. METHODS AND RESULTS: The service components at all HF clinics in Ontario, Canada, were quantified using a validated instrument and categorized as high/medium/low intensity. We used propensity-scores to match HF clinic and control patients discharged alive after a HF readmission in 2006-2007. Outcomes were mortality, and both all-cause and HF readmission. Cox-proportional hazard models were used to evaluate HF clinic-level characteristics associated with improved outcomes. We identified 14 468 patients with HF, of whom 1288 were seen in HF clinics. Within 4 years of follow-up, 52.1% of patients treated at a HF clinic died versus 54.7% of control patients (P=0.02). Patients treated at HF clinics had increased readmissions (87.4% versus 86.6% for all-cause [P=0.009]; 58.7% versus 47.3% for HF related [P<0.001]). There was no difference between high, medium, or low intensity clinics in terms of mortality, all-cause, or HF readmissions. HF clinics with greater frequency of visits (>4 contacts of significant duration for 6 months) were associated with lower mortality (hazard ratio, 0.14; P<0.0001) and hospitalization (hazard ratio, 0.69; P=0.039). More intensive medication management was associated with lower all-cause (hazard ratio, 0.46; P<0.001) and HF readmission (hazard ratio, 0.42; P<0.001). CONCLUSIONS: In this real-world population-based study, we found that multidisciplinary HF clinics are associated with a decrease in mortality, but an increase in readmissions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".