Risk Factors for Heart Failure Hospitalization in Kidney Transplant Recipients: Results From FAVORIT
Bibliographic record
Abstract
Background: The development of heart failure (HF) post kidney transplantation is associated with a higher risk of allograft failure and death. Using data from stable kidney transplant recipients (KTRs) enrolled in FAVORIT, we evaluated risk factors for HF hospitalization. Methods: FAVORIT randomized 4,110 stable KTRs to either a high-dose or low-dose multivitamin (folate, B6, B12). HF hospitalizations were determined by review of discharge diagnoses. Stepwise Cox regression models with forwards selection (P<0.05 for entry) were fit to assess for independent risk factors of HF hospitalization events (candidate variables included age, sex, race, country, BMI, cardiovascular disease, diabetes, donor type, graft vintage, albuminuria, smoking, aspirin, statin, ACEi or ARB use; treatment assignment and eGFR were forced in the model). Results: Of the 3,633 patients with complete data available, 115 patients (3.2%) experienced a HF hospitalization over a mean follow-up of four years (0.8 events per 100 patient years (95%CI 0.7-1.0)). The variables associated with a higher adjusted risk of HF events were albuminuria, older age, higher BMI, Black race (vs. non-Black), country (United States+Canada vs. Brazil), prior history of CV disease, and diabetes (Table 1). Conclusions: In a post-hoc analysis of FAVORIT, we identified multiple independent risk factors for HF hospitalization. Some of these, such as BMI and albuminuria, may be modifiable by lifestyle modifications and newer drug therapies, which should be adequately tested in this high-risk population.Risk factors for heart failure hospitalization among stable kidney transplant recipients
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".