Clinical impact of suboptimal RAASi therapy following an episode of hyperkalemia
Bibliographic record
Abstract
Abstract Background Hyperkalemia (HK) is a barrier to optimization of renin-angiotensin-aldosterone system inhibitor (RAASi) therapy in heart failure (HF) and chronic kidney disease (CKD). We investigated cardiorenal risk associated with changes in RAASi regimen after an episode of HK in patients with HF and/or CKD. Methods This observational study utilized data from hospital records, claims, and health registers from the US (Optum’s de-identified Market Clarity Data) and Japan (Medical Data Vision). Included patients had an index episode of HK between July 2019 and September 2021 (US), or May 2020 and September 2021 (Japan), with prior diagnosis of HF or CKD (stage 3 or 4), and RAASi use. Risk of a cardiorenal composite outcome (HF emergency visit, HF hospitalization, or progression to end-stage kidney disease) was determined in patients who discontinued RAASi, down-titrated their dose by > 25%, or maintained or up-titrated their dose following the HK episode. Results A total of 15,488 and 6020 patients were included from the US and Japan, respectively. Prior to the episode of HK, 59% (US) and 27% (Japan) of patients had achieved > 50% target RAASi dose. Following the episode of HK, 33% (US) and 32% (Japan) of patients did not fill a new RAASi prescription. Risk of the cardiorenal outcome at 6 months was higher in patients who discontinued or down-titrated versus maintained or up-titrated RAASi treatment both in the US (17.5, 18.3, and 10.6%; p < 0.001) and in Japan (19.7, 20.0, and 15.1%; p < 0.001). Conclusion HK-related RAASi discontinuation or down-titration was associated with higher risk of cardiorenal events versus maintained or up-titrated RAASi.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".