Trimethoprim-sulfamethoxazole and the risk of a hospital encounter with hyperkalemia: a matched population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Trimethoprim-sulfamethoxazole (TMP-SMX) can cause hyperkalemia by reducing renal potassium excretion. We assessed the risk of hyperkalemia after initiating TMP-SMX versus amoxicillin and determined if this risk is modified by a patient's baseline kidney function [estimated glomerular filtration rate (eGFR)]. METHODS: We conducted a population-based cohort study in Ontario, Canada involving adults ≥66 years of age newly treated with TMP-SMX (n = 58 999) matched 1:1 with those newly treated with amoxicillin (2008-2020). The primary outcome was a hospital encounter with hyperkalemia defined by a laboratory serum potassium value ≥5.5 mmol/L within 14 days of antibiotic treatment. Secondary outcomes included a hospital encounter with acute kidney injury (AKI) and all-cause hospitalization. Risk ratios (RRs) were obtained using a modified Poisson regression. RESULTS: A hospital encounter with hyperkalemia occurred in 269/58 999 (0.46%) patients treated with TMP-SMX versus 80/58 999 (0.14%) in those treated with amoxicillin {RR 3.36 [95% confidence interval (CI) 2.62-4.31]}. The absolute risk of hyperkalemia in patients treated with TMP-SMX versus amoxicillin increased progressively with decreasing eGFR (risk difference of 0.12% for an eGFR ≥60 ml/min/1.73 m2, 0.42% for eGFR 45-59, 0.85% for eGFR 30-44 and 1.45% for eGFR <30; additive interaction P < .001). TMP-SMX versus amoxicillin was associated with a higher risk of a hospital encounter with AKI [RR 3.15 (95% CI 2.82-3.51)] and all-cause hospitalization [RR 1.43 (95% CI 1.34-1.53)]. CONCLUSIONS: The 14-day risk of a hospital encounter with hyperkalemia was higher in patients newly treated with TMP-SMX versus amoxicillin and the risk was highest in patients with a low eGFR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".