SGLT2 Inhibitors and Risk for Hyperkalemia Among Individuals Receiving RAAS Inhibitors
Bibliographic record
Abstract
Importance: Hyperkalemia is a common complication of taking a renin-angiotensin-aldosterone system inhibitor (RAASi). Post hoc analyses of large randomized clinical trials suggested that the addition of sodium-glucose cotransporter 2 inhibitors (SGLT2i) may attenuate this risk. It is unknown if this observation extends to daily clinical practice. Objective: To evaluate the association between SGLT2i initiation and hyperkalemia in individuals receiving RAASi with a background of diabetes, heart failure, or chronic kidney disease. Design, Setting, and Participants: This population-based retrospective cohort study was conducted in Ontario, Canada, from July 1, 2015, to June 30, 2021. The cohort comprised adults 66 years and older who were prescribed a RAASi and had a history of diabetes or heart failure, an estimated glomerular filtration rate of less than 45 mL/min/1.73 m2, and/or a urine albumin to creatinine ratio of greater than 30 mg/mmol. The data were analyzed between March 28, 2023, and March 22, 2024. Exposure: The study exposure was a new prescription of an SGLT2i compared to noninitiation of an SGLT2i. Inverse probability of treatment weighting by a propensity score for the receipt of SGLT2i was used to achieve balance of baseline covariates in both exposure groups. Main Outcomes and Measures: The primary study outcome was hyperkalemia, defined as a serum potassium of greater than 5.5 mEq/L or an administrative code for an inpatient or outpatient encounter with hyperkalemia within 1 year of the index date. Results: A total of 20 063 individuals who initiated an SGLT2i (mean [SD] age, 76.9 [6.6] years; 12 020 [59.9%] male) were compared to a pseudopopulation of 19 781 nonusers (mean [SD] age, 76.8 [7.0] years; 11 731 [59.3%] male). In the overall cohort, 95% had diabetes, 17% had heart failure, and 32% had stage 3 to 5 chronic kidney disease. SGLT2i initiation was associated with a lower risk of hyperkalemia (hazard ratio, 0.89 [95% CI, 0.82-0.96]). SGLT2i users had a significantly lower rate of RAASi discontinuation compared to nonusers (36% vs 45%; P < .001). Conclusions and Relevance: This cohort study demonstrated that, among individuals with diabetes, heart failure, or chronic kidney disease who were receiving a RAASi, SGLT2i initiation was associated with a lower risk of hyperkalemia and RAASi discontinuation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".