Association of Albuminuria With 1‐Year Risk of Heart Failure and Other Adverse Outcomes in Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Albuminuria is associated with increased stroke risk in atrial fibrillation (AF), but its relationship with heart failure (HF) and other adverse outcomes in AF is less well understood. METHODS: Using linked administrative databases, we conducted a retrospective cohort study of individuals aged ≥66 years who were newly diagnosed with AF between April 2009 and March 2019 in Ontario, Canada. Albuminuria was assessed using (1) urine albumin-to-creatinine ratio (UACR, mg/g) and (2) dipstick proteinuria (negative, trace, 1+, 2+, ≥3+). Cause-specific hazards regression estimated adjusted hazard ratios (HRs) for HF hospitalizations or emergency department visits, stroke hospitalizations, bleeding hospitalizations, and death over 1 year. RESULTS: We included 64 717 individuals with UACR data and 110 430 with dipstick proteinuria data. Relative to UACR 5 mg/g, the HRs for UACR 30 mg/g (below the microalbuminuria threshold) were 1.39 (95% CI, 1.28-1.50) for HF, 1.22 (95% CI, 1.07-1.40) for bleeding, and 1.35 (95% CI, 1.27-1.42) for death. A UACR of 30 mg/g versus 5 mg/g was associated with an HR of 1.16 (95% CI, 0.99-1.36) for stroke but the HR was significantly elevated at UACR values ≥65 mg/g. Increasing dipstick proteinuria was also associated with increases in the HR for adverse outcomes. A UACR of 30 mg/g was associated with greater HF risk (versus 5 mg/g) than all CHA₂DS₂VASc components except age >75 years and prior HF. CONCLUSIONS: Albuminuria is associated with increased hazards of HF, stroke, bleeding, and death in patients with AF even at low UACR levels. Albuminuria may enhance risk stratification in AF beyond traditional scores.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".