The hospital frailty risk score in patients with heart failure is strongly associated with outcomes but less so with pharmacotherapy
Bibliographic record
Abstract
BACKGROUND: Although frailty is known to be an important prognostic factor in heart failure (HF), HF risk-adjustment models do not incorporate frailty measures and the interplay between frailty, age and pharmacotherapy is unclear. OBJECTIVES: To explore the relationships between frailty, pharmacotherapy and outcomes in heart failure (HF). METHODS: Retrospective cohort study of all adults in Alberta, Canada hospitalized for the first time for HF between 2004 and 2016. Frailty was defined using the Hospital Frailty Risk Score (HFRS). RESULTS: In 26 626 patients (mean age 77.4 years), the 8887 (33.4%) defined as frail (HFRS ≥ 5) were older, had higher Charlson scores and more prior emergency department visits or hospitalizations. The HFRS and the Charlson Score were only weakly correlated (r = 0.35). Whilst more common in older patients (41.4% of patients 80 or older), frailty was present in 22.4% of patients younger than 65. Frail patients had longer lengths of stay and worse outcomes postdischarge, but adding the HFRS to age, sex and Charlson score did not improve prediction of events (c-statistics 0.69 for 30-day mortality after admission, and 0.54 for 30-day readmission/ED visit/or death after discharge). Frail patients younger than 65 were significantly more likely than nonfrail patients 80 or older to be prescribed high-dose evidence-based HF therapies (27.1% vs. 22.2%, P = 0.003). CONCLUSION: Although the HFRS reflects aspects of frailty that patient age and Charlson scores do not, the addition of the HFRS to standard risk prediction equations provides little additional information. Prescribing practices correlate more with patient age than frailty status.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".