Trimethoprim-Sulfamethoxazole–Induced Hyperkalemia in Patients Receiving Inhibitors of the Renin-Angiotensin System
Bibliographic record
Abstract
BACKGROUND: Trimethoprim therapy can cause hyperkalemia and is often coprescribed with angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs). The objective of this study was to characterize the risk of hyperkalemia-associated hospitalization in elderly patients who were being treated with trimethoprim-sulfamethoxazole along with either an ACEI or an ARB. METHODS: We conducted a population-based, nested case-control study of a cohort of elderly patients 66 years or older who were residents of Ontario, Canada, and who were receiving continuous therapy with either an ACEI or an ARB. Case patients were those with a hyperkalemia-associated hospitalization within 14 days of receiving a prescription for trimethoprim-sulfamethoxazole, amoxicillin, ciprofloxacin, norfloxacin, or nitrofurantoin. For each case, we identified up to 4 control patients from the same cohort matched for age, sex, and presence or absence of chronic renal disease and diabetes. Odds ratios were determined for the association between hyperkalemia-associated hospitalization and previous antibiotic use. RESULTS: During the 14-year study period, we identified 4148 admissions involving hyperkalemia, 371 of which occurred within 14 days of antibiotic exposure. Compared with amoxicillin, the use of trimethoprim-sulfamethoxazole was associated with a nearly 7-fold increased risk of hyperkalemia-associated hospitalization (adjusted odds ratio, 6.7; 95% confidence interval, 4.5-10.0). No such risk was found with the use of comparator antibiotics. CONCLUSIONS: Among older patients treated with ACEIs or ARBs, the use of trimethoprim-sulfamethoxazole is associated with a major increase in the risk of hyperkalemia-associated hospitalization relative to other antibiotics. Alternate antibiotic therapy should be considered in these patients when clinically appropriate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".