Predictors of Clinical Outcomes in Elderly Patients with Heart Failure
Bibliographic record
Abstract
AIMS: Heart failure (HF) in the elderly carries a poor prognosis. We used the SENIORS dataset of elderly HF patients aged ≥70 years in order to develop a risk model for this population. METHODS AND RESULTS: The SENIORS trial evaluated the effects of nebivolol and enrolled 2128 patients ≥70 years with HF (ejection fraction ≤35%, or recent HF admission). We randomly selected 1400 patients from the full dataset to produce a derivation cohort and the remaining 728 patients were used as a validation cohort. Baseline variables were entered into a bootstrap model with 200 iterations to determine their association with two outcomes, the composite of all-cause mortality or cardiovascular hospitalization, or all-cause mortality alone. Variables retaining a significant association with these outcomes in a multivariate model were used to develop a risk prediction score tested in the validation cohort. Five factors were associated with increased risk of both outcomes in the multivariate model: higher New York Heart Association class, higher uric acid level, lower body mass index, prior myocardial infarction, and larger left atrial (LA) dimension. For the composite outcome, peripheral arterial disease, years with heart failure, right bundle branch block, diabetes mellitus, and orthopnoea were also retained. For all-cause mortality, creatinine, 6 min walk test distance, coronary artery disease, and age were retained. CONCLUSION: In addition to conventional prognostic markers, uric acid and LA dimension appear to be important novel risk prediction markers in elderly patients with heart failure, and could be useful in guiding management.
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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 | 0.000 |
| 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) | 0.001 | 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".