Association of Acute Increases in Plasma Creatinine after Renin-Angiotensin Blockade with Subsequent Outcomes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Data from observational and interventional studies provide discordant results regarding the relationship between creatinine increase after renin-angiotensin system inhibition (RASi) and adverse outcomes. We compared health outcomes among patients with different categories of increase in creatinine upon initiation of RASi in a large population-based cohort. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We performed a retrospective analysis of the Stockholm CREAtinine Measurements database, which contains complete information on diagnoses, medication dispensation claims, and laboratory test results for all Stockholm citizens accessing health care. Included were 31,951 adults initiating RASi during 2007-2011 with available pre- and postinitiation creatinine monitoring. Multivariable Cox regression was used to compare mortality, cardiovascular and ESKD events among individuals with different ranges of creatinine increases within 2 months after starting treatment. RESULTS: 0.25). Results were robust across subgroups, among continuing users, when patients with decreases in creatinine were excluded from the reference group, and after accounting for death as a competing risk. CONCLUSIONS: Among real-world monitored adults, increases in creatinine (>10%) after initiation of RASi are associated with worse health outcomes. These results do not address the issue of discontinuation of RASi when plasma creatinine increases but do suggest that patients with increases in creatinine have higher subsequent risk of cardiovascular and kidney outcomes.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".