Cardiovascular Drug Use After AKI Among Hospitalized Patients with a History of Myocardial Infarction: A Population-Based Study
Bibliographic record
Abstract
Background: Patients who survive an episode of acute kidney injury (AKI) are at increased risk of cardiovascular morbidity and mortality but may receive fewer cardioprotective drugs than patients without AKI. Our main objective was to evaluate the use of cardiovascular drugs after AKI among hospitalized patients with myocardial infarction (MI). Methods: We conducted a population-based study of patients aged ≥ 66 years old with a prior history of MI, hospitalized from January 1, 2008, to March 31, 2017. We ascertained AKI using KDIGO serum creatinine criteria. We used propensity score matching to assemble a cohort of patients with and without AKI. The primary outcome was time to receipt of prescriptions for ACEi/ARB, beta-blocker, and statin (all 3 drugs) within one year of hospital discharge. We utilized proportional subdistribution hazards regression, accounting for the competing risk of death, to determine the cumulative incidence of receipt of cardiovascular drugs after AKI compared to patients without AKI. Results: We identified 28,871 patients with AKI, of whom 21,452 were matched 1:1 to similar patients without AKI. Acute kidney injury was associated with a 7% (95% CI 5-9%) lower likelihood of receiving all 3 cardiovascular drug classes within one year of hospital discharge. This result was largely driven by a 13% (95% CI 11-15%) lower likelihood of ACEi/ARB prescription across all categories of AKI severity. Lower use of beta-blockers and statins was observed in severe AKI. Conversely, AKI was associated with more frequent use of loop diuretics (sHR=1.20, 95% CI 1.17-1.23) and mineralocorticoid receptor antagonists (sHR=1.22, 95% CI 1.15-1.28). The use of most medications stabilized at 3 months post-AKI. Conclusions: In patients with a history of myocardial infarction, survivors of AKI were less likely to receive prescriptions for all 3 cardiovascular drug classes with strong evidence (ACEi/ARB, beta-blocker, and statin) and more likely to receive loop diuretics and mineralocorticoid receptor antagonists within one year of hospital discharge. Most medication changes stabilized at 3 months, indicating a critical timeframe to provide follow-up care.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".