Utility of the Seattle Heart Failure Model for palliative care referral in advanced ambulatory heart failure
Bibliographic record
Abstract
BACKGROUND: Physicians face uncertainty when predicting death in heart failure (HF) leading to underutilisation of palliative care. To facilitate decision-making, we assessed the Seattle Heart Failure Model (SHFM) as a referral tool by evaluating its performance in predicting 1-year event-free survival from death, heart transplant (HTx), and ventricular assist device (VAD) implantation. METHODS: We retrospectively reviewed the charts of consecutive patients with advanced ambulatory HF with New York Heart Association Class III/IV HF and a left ventricular ejection fraction of ≤40% from 2000 to 2016. We evaluated SHFM's performance by using the Cox proportional hazards model, its discrimination using the c-statistic, its calibration by comparing the observed and predicted survival and its clinical utility by hypothetically assessing the proportion of patients adequately or inadequately referred to palliative care. RESULTS: We included 612 patients in our study. During the 1-year follow-up, there were 83 deaths, 4 HTx and 1 VAD. Although SHFM showed very good discrimination (c-statistic=0.71) and adequate calibration in medium to low-risk patients, it underestimated event-free survival by 12% in high-risk patients. SHFM's clinical utility was limited: 33% of eligible patients would have missed the opportunity for referral and only 27% of referred patients would have benefited. CONCLUSION: Use of SHFM could result in a high proportion of referrals while capturing the majority of patients who may benefit from palliative care. Though this may be a more encompassing and safer alternative than current referral practices, it could lead to many early referrals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".