Guideline-Referral Criteria and Risk Profiles of Outpatients Referred to a Specialised Heart Failure Clinic
Bibliographic record
Abstract
Background 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. Methods 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. Results 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 ( P = 0.11) but differed significantly among patients meeting any (5%, IQR 3%-9%) vs no (3%, IQR 2%-5%) I-NEED-HELP criteria ( P < 0.001). Across referral reasons, the presence of any I-NEED-HELP criteria differed significantly ( P < 0.001); most patients referred for advanced therapies evaluation (96%) and diagnostic evaluation (94%) met at least 1 criterion. Conclusions 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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".