A Description of QT-Interval Prolonging Drug Interactions with Fluoroquinolones in Older Women with Uncomplicated Urinary Tract Infections
Bibliographic record
Abstract
Background: Fluoroquinolone (FQ) antibiotics are associated with QT-interval prolongation and Torsades de Pointes (TdP). Female sex, older age, and other QT-interval prolonging medications further increase risk for TdP. Our aim was to describe QT-interval prolonging drug interactions when FQs were dispensed to women who resided in long-term care (LTC) for uncomplicated urinary tract infections (UTIs). Methods: This retrospective cohort study used administrative health data from the Nova Scotia Seniors' Pharmacare program from January 2005 through March 2020. The cohort included women residing in LTC dispensed a FQ antibiotic within five days of a diagnostic code for an uncomplicated UTI in physician billing data. Additional drug dispensations were collected 30 and 90 days after the FQ to identify drug interactions that resulted in potentially increased QT-interval prolongation risk. Drug interactions were described. A Mann-Kendall trend test assessed the change in the frequency of FQ-drug interactions over the study period. Results: =.00007). Within 30 days of the FQ dispensation, the most common drug interactions identified were: furosemide (n=702, 20.3% of FQ-drug interactions), citalopram (n=566, 16.4% of FQ-drug interactions), and trazodone (n=461, 13.3% of FQ-drug interactions). Conclusions: An increasing proportion of women dispensed a FQ for uncomplicated UTI experienced a potential QT-interval prolonging drug interaction over the study period. When prescribing FQs to older women, addressing potentially modifiable risk factors for TdP, and monitoring closely, is warranted.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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".