Albuminuria, Reduced Kidney Function, and the Risk of ST‐ and non–ST‐segment–elevation myocardial infarction
Bibliographic record
Abstract
Background Chronic kidney disease is a recognized independent risk factor for cardiovascular disease, but whether the risks of ST‐segment–elevation myocardial infarction ( STEMI ) and non–ST‐segment–elevation myocardial infarction ( NSTEMI ) differ in the chronic kidney disease population is unknown. Methods and Results Using administrative data from Ontario, Canada, we examined patients ≥66 years of age with an outpatient estimated glomerular filtration rate ( eGFR ) and albuminuria measure for incident myocardial infarction from 2002 to 2015. Adjusted Fine and Gray subdistribution hazard models accounting for the competing risk of death were used. In 248 438 patients with 1.2 million person‐years of follow‐up, STEMI , NSTEMI , and death occurred in 1436 (0.58%), 4431 (1.78%), and 30 015 (12.08%) patients, respectively. The highest level of albumin‐to‐creatinine ratio (>30 mg/mmol) was associated with a 2‐fold higher adjusted risk of both STEMI and NSTEMI among patients with eGFR ≥60 mL/(min·1.73 m 2 ) compared to albumin‐to‐creatinine ratio <3 mg/mmol. The lowest level of eGFR (<30 mL/[min·1.73 m 2 ]) was not associated with higher STEMI risk but with a 4‐fold higher risk of NSTEMI compared to those with eGFR ≥60 mL/(min·1.73 m 2 ). The lowest eGFR (<30 mL/[min·1.73 m 2 ]) and highest albumin‐to‐creatinine ratio (>30 mg/mmol) were associated with a greater than 4‐fold higher risk of both STEMI and NSTEMI (subdistribution hazard models [95% confidence interval] 4.53 [3.30‐6.21] and 4.42 [3.67‐5.32], respectively) compared to albumin‐to‐creatinine ratio <3 mg/mmol and eGFR ≥60 mL/(min·1.73 m 2 ). Conclusions Elevations in albuminuria are associated with a higher risk of both NSTEMI and STEMI , regardless of kidney function, whereas reduced kidney function alone is associated with a higher NSTEMI risk.
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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 | 0.001 |
| 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".