Ciprofloxacin does not Prolong the QTc Interval: A Clinical Study in ICU Patients and Review of the Literature
Bibliographic record
Abstract
PURPOSE: Ciprofloxacin may prolong the QT interval and increase the risk of Torsade de Pointes (TdP). Intravenous administration of ciprofloxacin in patients with additional risks may elevate the risk of QTc interval prolongation. We prospectively assessed whether intravenous ciprofloxacin prolongs the QT interval in patients with additional co-morbidities and risk factors. We also reviewed the literature on the QT prolonging effect or TdP inducing effect of ciprofloxacin. METHODS: ICU Patients who were treated with intravenous ciprofloxacin as part of their therapy were recruited. ECG was recorded within 60 min before start and in the last 30 min of 1 h infusion, or within 30 min after the end of ciprofloxacin infusion. QT interval was corrected for the heart rate using both Bazett's and Fridericia's formula. The changes were analyzed using the paired Student's t-test. RESULTS: Ten patients were included in the study (average age 74-y, 6 males). The average baseline QTc interval corrected with Bazett's formula was 448 ms that was shortened during or after ciprofloxacin infusion by 3 ms and 2 ms based on Bazett's (p=0.67) and Fridericia's (p=0.68) formula, respectively. No observational study or cohort study thus far has shown that ciprofloxacin has a QT prolonging effect or increases the risk of TdP or (cardiovascular) mortality. Conclusion. Based on our results and the results of previous studies, it is unlikely that ciprofloxacin has a clinically relevant QT prolonging effect or an increased risk of TdP. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".