Deutsch
Nederlands
.png)
 as a function of uncorrected QT-interval (Joy et al, 2011).png)
Probability of torsades de pointes (TdP) based on uncorrected QT interval: ...
{{ model.survival.PITTitle }}
{{ model.survival.YNETitle }}
Probability of torsades de pointes (TdP) based on uncorrected QT interval:
| {{ row }} |
Outcome stratification
Conditional information
Result interpretation
Context information:
Measures of the magnitude of (uncorrected) QT interval predicted the risk of TdP significantly better than dose, heart rate, or the mere presence of a prolonged QT. Moreover, the magnitude of the QT prolongation was a better predictor of TdP than the mere presence or absence of an abnormal QT, highlighting the problematic nature of using a single cutoff value for risk assessment.
Source:
Joy JP, Coulter CV, Duffull SB, Isbister GK. Prediction of torsade de pointes from the QT interval: analysis of a case series of amisulpride overdoses. Clin Pharmacol Ther. 2011;90(2):243-5.
Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.
Model feedback
No feedback yet 1 Comment {{ model.comments.length }} Comments
Please sign in to enable Evidencio print features
If you don't have an Evidencio Community Account you can create your free personal account at:
https://www.evidencio.com/registration
Printed results - Examples {{ new Date().toLocaleString() }}
Evidencio Community Account Benefits
With an Evidencio Community account you can:
- Create and publish your own prediction models.
- Share your prediction models with your colleagues, research group, organization or the world.
- Review and provide feedback on models that have been shared with you.
- Validate your models and validate models from other users.
- Find models based on Title, Keyword, Author, Institute, or MeSH classification.
- Use and save prediction models and their data.
- Use patient specific protocols and guidelines based on sequential models and decision trees.
- Stay up-to-date with new models in your field as they are published.
- Create your own lists of favorite models and topics.
{{ variable.title }}