Abstract 3273: Timely Access to Care - Risk Stratification Utilizing the Seattle Heart Failure Model in patients with Advanced Heart Failure: Dying to be seen.
Bibliographic record
Abstract
Introduction Heart failure is an epidemic with age-adjusted mortality of 45%/5 years. Multidisciplinary heart function clinics (HFC) have been shown to improve outcomes in patients. Timely access to cardiac care remains one of Canada’s leading concerns. Risk stratification of patients upon referral to a HFC may identify patients that require urgent access to, and benefit from, multidisciplinary care. Hypothesis To determine if a priori assessment using the Seattle Heart Failure Survival Model (SHFM) at the time of referral to a multidisciplinary HFC would help risk stratify patients regarding urgency of consultation. Methods The referral packages of patients known to have died prior to or within 60 days of initial consultation were retrospectively reviewed (Group 1). Data were collected to determine the mortality risk based on the SHFM. Age and sex-matched controls were randomly selected from our HFC database (Group 2). Statistical analysis was performed using SPSS. Results A total of 107 patients were included in this study (Group 1, n=57; Group 2 n=50). There were no significant differences in baseline characteristics between the groups. In Group 1, 38% of patients died before being evaluated, while the remaining 62% died within 60 days of the initial HFC visit. The majority of patients in both groups had either ischemic or idiopathic dilated cardiomyopathy (52% and 22%, respectively). Patients in Group 1 reported NYHA class III/IV symptoms 40%/33%, respectively, versus Group 2 patients reporting NYHA class III/IV symptoms 46%/8%, respectively. There was a statistically significant difference in the mean SHFM mortality risk score, predicted at the time of initial receipt of referral, between the study groups with Group 1 patients having a much higher predicted mortality versus Group 2 at 1, 2 and 5 years (p<0.001). Conclusion The SHFM is a useful tool to risk stratify patients with HF at the time of referral/entry into a multidisciplinary clinic. It provides a reliable method to triage risk and ensure that those at greatest risk are seen soonest, hence facilitating timely access for care. Prospective validation regarding the triage applicability of the SHFSS is needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".