Which frailty tool best predicts morbidity and mortality in ambulatory patients with heart failure? A prospective study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.002 |
| 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 | 0.001 |
| 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 it