Screening and Initiating Supportive Care in Patients With Heart Failure
Bibliographic record
Abstract
Background: Patients with heart failure (HF) experience a major symptom burden and an overall reduction of quality of life. However supportive care (SC) remains an under-utilized resource for these patients. Among the many existing barriers to integrating SC into routine care, identifying patients with SC needs remains challenging. The Kansas City Cardiomyopathy Questionnaire (KCCQ) is an important predictor of SC needs in patients with HF. Methods and Results: We used the shortened version KCCQ-12 as a screening tool for SC need in our ambulatory HF patient population using a KCCQ-12 summary score of <29 as the cut-off. Of the 456 patients who completed the KCCQ-12, 41 (9%) were predictive to have SC needs. Demographics, medical history, biochemical and echocardiographic assessment and medical treatment were similar between the two groups of patients. However, patients with KCCQ-12 <29 were more symptomatic based on NYHA classification with a higher prevalence of depression. We established a multidisciplinary SC clinic and the profile and outcomes of patients with SC needs that were referred and followed at our SC clinic were also evaluated. Twenty-three patients were referred to our SC clinic: 2 died before being seen, 1 refused SC and 20 received SC. Of these 20 patients, 11 died and 9 are currently being followed. Median survival after starting the SC clinic is 3 months. Conclusions: The magnitude of unmet supportive care needs in patients with HF is significant. While the KCCQ-12 questionnaire is a useful tool to identify patients with SC, serial clinical evaluation, establishment of a SC clinic and prompt referral are essential for patients needing supportive care.
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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) | 0.001 | 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) | 0.000 | 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".