Guideline-Referral Criteria and Risk Profiles of Outpatients Referred to a Specialised Heart Failure Clinic
Bibliographic record
Abstract
<h2>Abstract</h2><h3>Background</h3> Specialised heart failure (HF) care improves outcomes for patients with HF. To understand the risk profiles of HF outpatients referred to a specialised clinic, we evaluated referral reasons, predicted risk, and the presence of guideline-recommended referral criteria at a large specialised HF clinic. <h3>Methods</h3> We conducted a cross-sectional study including outpatients with HF (≥ 18 years old) referred from November 2021 to November 2022. We calculated 1-year predicted mortality with the use of the Seattle Heart Failure Model (SHFM) and the I-NEED-HELP referral criteria. We compared median SHFM-predicted mortality with referral reasons and the I-NEED-HELP criteria by means of Kruskal-Wallis, Wilcoxon rank-sum, chi-square, and Fisher exact tests. <h3>Results</h3> Among 245 consecutive HF outpatients included, median SHFM-predicted 1-year mortality was 4% (interquartile range [IQR] 2%-8%). Reasons for referral included evaluation for advanced therapies (29%), medication optimisation (23%), diagnostic evaluation (19%), post-hospitalisation/emergency department visit (14%), ongoing HF management (12%), patient request (2%), and transition to adult care (1%). The median SHFM-predicted 1-year mortality did not differ significantly by referral reason (<i>P</i> = 0.11) but differed significantly among patients meeting any (5%, IQR 3%-9%) vs no (3%, IQR 2%-5%) I-NEED-HELP criteria (<i>P</i> < 0.001). Across referral reasons, the presence of any I-NEED-HELP criteria differed significantly (<i>P</i> < 0.001); most patients referred for advanced therapies evaluation (96%) and diagnostic evaluation (94%) met at least 1 criterion. <h3>Conclusions</h3> Patients referred to a specialised HF clinic have a wide risk range. The difference in predicted mortality among patients meeting any vs no I-NEED-HELP criteria appears clinically insignificant. Incorporating model-predicted risk at the time of referral can guide triage and patient prioritisation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".