Abstract 17975: Anemia in Heart Failure With Preserved Ejection Fraction: Insights From the TOPCAT trial
Bibliographic record
Abstract
Introduction: Anemia predicts higher rates of mortality and HF hospitalizations in patients with heart failure (HF). We sought to determine the factors associated with anemia in TOPCAT, a large randomized trial that tested the effects of spironolactone in patients with HF and preserved EF (HFpEF). Methods: Amongst the 3445 patients enrolled in TOPCAT, 3422 had available baseline hemoblobin. All significant factors assessed in baseline characteristic comparisons were put in a saturated multivariable logistic regression model along with randomized treatment assignment. The stepwise elimination method was used to obtain a parsimonious model identifying factors associated with anemia. Results: The overall prevalence of anemia (hemoglobin, Hgb < 12g/dL in women and < 13g/dL in men) was 28.5% (974 patients), with higher prevalence in patients enrolled in the Americas (41%) than in Russia/Georgia (15.4%). Previous hospitalization for HF, randomization in the Americas, older age, insulin-treated diabetes, orthopnea, peripheral edema, lower eGFR, lower diastolic BP, lower HR, and longer QRS duration were independent correlates of anemia; whereas female gender, white race, higher BMI, known dyslipidemia, left ventricular hypertrophy, and eating meals at home were independently associated with the absence of anemia (Table). Conclusions: Anemia in HFpEF is associated with multiple markers of increased cardiovascular risk, and of more advanced HF. We identified new factors associated with the absence of anemia, such as eating most meals at home. Complex interactions may underlie the observed relationships, including medications, and causality cannot be inferred from our observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".