Associations among Estimated Glomerular Filtration Rate, Proteinuria, and Adverse Cardiovascular Outcomes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Most studies of chronic kidney disease (CKD) and outcomes focus on mortality and ESRD, with limited data on other adverse outcomes. This study examined the associations among proteinuria, eGFR, and adverse cardiovascular (CV) events. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This was a population-based longitudinal study with patients identified from province-wide laboratory data from Alberta, Canada, between 2002 and 2007. Selected for this study from a total of 1,526,437 patients were 920,985 (60.3%) patients with at least one urine dipstick measurement and 102,701 patients (6.7%) with at least one albumin-creatinine ratio (ACR) measurement. Time to hospitalization was considered for one of four indications: congestive heart failure (CHF), coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI), peripheral vascular disease (PVD), and stroke/transient ischemic attacks [TIAs] (cerebrovascular accident [CVA]/TIA). RESULTS: After a median follow-up of 35 months, in fully adjusted models and compared with patients with estimated GFR (eGFR) of 45 to 59 ml/min per 1.73 m(2) and no proteinuria, patients with heavy proteinuria by dipstick and eGFR ≥ 60 ml/min per 1.73 m(2) had higher rates of CABG/PCI and CVA/TIA. Similar results were obtained in patients with proteinuria measured by ACR. CONCLUSIONS: Risks of major CV events at a given level of eGFR increased with higher levels of proteinuria. The findings extend current data on risk of mortality and ESRD. Measurement of proteinuria is of incremental prognostic benefit at every level of eGFR. The data support use of proteinuria measurement with eGFR for definition and risk stratification in CKD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".