Association of Proteinuria and Incident Atrial Fibrillation in Patients With Intact and Reduced Kidney Function
Bibliographic record
Abstract
Background Early evidence suggests proteinuria is independently associated with incident atrial fibrillation (AF). We sought to investigate whether the association of proteinuria with incident AF is altered by kidney function. Methods and Results Retrospective cohort study using administrative healthcare databases in Ontario, Canada (2002–2015). A total of 736 666 patients aged ≥40 years not receiving dialysis and with no previous history of AF were included. Proteinuria was defined using the urine albumin‐to‐creatinine ratio (ACR) and kidney function by the estimated glomerular filtration rate (eGFR). The primary outcome was time to AF. Cox proportional models were used to determine the hazard ratio for AF censored for death, dialysis, kidney transplant, or end of follow‐up. Fine and Grey models were used to determine the subdistribution hazard ratio for AF, with death as a competing event. Median follow‐up was 6 years and 44 809 patients developed AF. In adjusted models, ACR and eGFR were associated with AF ( P <0.0001). The association of proteinuria with AF differed based on kidney function (ACR × eGFR interaction, P <0.0001). Overt proteinuria (ACR, 120 mg/mmol) was associated with greater AF risk in patients with intact (eGFR, 120) versus reduced (eGFR, 30) kidney function (adjusted hazard ratios, 4.5 [95% CI, 4.0–5.1] and 2.6 [95% CI, 2.4–2.8], respectively; referent ACR 0 and eGFR 120). Results were similar in competing risk analyses. Conclusions Proteinuria increases the risk of incident AF markedly in patients with intact kidney function compared with those with decreased kidney function. Screening and preventative strategies should consider proteinuria as an independent risk factor for AF.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".