Which frailty tool best predicts morbidity and mortality in ambulatory patients with heart failure? A prospective study
Bibliographic record
Abstract
BACKGROUND: Frailty is common in patients with heart failure (HF) and is associated with adverse outcome, but it is uncertain how frailty should best be measured. OBJECTIVES: To compare the prognostic value of commonly-used frailty tools in ambulatory patients with HF. METHODS AND RESULTS: We assessed, simultaneously, three screening tools [clinical frailty scale (CFS); Derby frailty index (DFI); acute frailty network (AFN) frailty criteria), three assessment tools (Fried criteria; Edmonton frailty score (EFS); deficit index (DI)) and three physical tests (handgrip strength, timed get-up-and-go test (TUGT), 5-metre walk test (5MWT)] in consecutive patients with HF attending a routine follow-up visit. 467 patients (67% male, median age = 76 years, median NT-proBNP = 1156 ng/L) were enrolled. During a median follow-up of 554 days, 82 (18%) patients died and 201 (43%) patients were either hospitalised or died. In models corrected for age, Charlson score, haemoglobin, renal function, sodium, NYHA, atrial fibrillation (AF), and body mass index, only log[NT-proBNP] and frailty were independently associated with all-cause death. A base model for predicting mortality at 1 year including NYHA, log[NT-proBNP], sodium and AF, had a C-statistic = 0.75. Amongst screening tools: CFS (C-statistic = 0.84); amongst assessment tools: DI (C-statistic = 0.83) and amongst physical test: 5MWT (C-statistic = 0.80), increased model performance most compared with base model (P <0.05 for all). CONCLUSION: Frailty is strongly associated with adverse outcomes in ambulatory patients with HF. When added to a base model for predicting mortality at 1 year including NYHA, NT-proBNP, sodium, and AF, CFS provides comparable prognostic information with assessment tools taking longer to perform.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".