FC 129: Stopping versus Continuing Renin–Angiotensin System Inhibitors After Acute Kidney Injury and Adverse Clinical Outcomes: An Observational Study From Routine Care Data
Bibliographic record
Abstract
Abstract BACKGROUND AND AIMS The risk–benefit ratio of continuing with renin–angiotensin system inhibitors (RASi) after an episode of acute kidney injury (AKI) is unclear. While stopping RASi may prevent recurrent AKI or hyperkalaemia, it may deprive patients of the cardiovascular benefits of using RASi. METHOD We analysed outcomes of long-term RASi users experiencing AKI (stage 2 or 3, or clinically coded) during hospitalization in Stockholm, Sweden, during 2007–2018. We compared stopping RASi within 3 months after discharge with continuing. The primary study outcome was the composite of all-cause mortality, myocardial infarction and stroke. Recurrent AKI was our secondary outcome, and we considered hyperkalaemia as a positive control outcome. Propensity score overlap weighted Cox models were used to estimate hazard ratios, balancing 75 confounders. Weighted absolute risk differences (ARD) were also determined. RESULTS We included 10 165 individuals, of whom 4429 stopped and 5736 continued RASi, with a median follow-up of 2.3 years. Median age was 78 years, 45% were women, and median kidney function before the index episode of AKI was 55 mL/min/1.73 m2. After weighting, those who stopped had an increased risk [HR, 95% confidence interval (95% CI)] of the composite of death, myocardial infarction and stroke (1.13, 1.07–1.19; ARD 3.7, 95% CI 2.6–4.8) compared with those who continued, a similar risk of recurrent AKI (0.94, 0.84–1.05), and a decreased risk of hyperkalaemia (0.79, 0.71–0.88). CONCLUSION Stopping RASi use among survivors of moderate-to-severe AKI was associated with a similar risk of recurrent AKI, but higher risk of the composite of death, myocardial infarction and stroke.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".