Deutsch
Nederlands
Predicting international normalised ratio ≥ 4.5 in hospitalised patients using vitamin K antagonists
Vitamin K antagonists (VKAs) used for the prevention and treatment of thromboembolic disease, increase the risk of bleeding complications. This model was developed and validated to predict the risk of an international normalised ratio (INR) ≥4.5 during a hospital stay.
We developed and validated a clinical prediction model for an INR ≥4.5 in VKA-treated patients admitted to our hospital. The model includes factors that are collected during routine care and are extractable from electronic patient records, enabling easy use of this model to predict an increased bleeding
risk in clinical practice.
We developed and validated a clinical prediction model for an INR ≥4.5 in VKA-treated patients admitted to our hospital. The model includes factors that are collected during routine care and are extractable from electronic patient records, enabling easy use of this model to predict an increased bleeding
risk in clinical practice.
Research authors: Albert R. Dreijer, Joseph S. Biedermann, Jeroen Diepstraten, Anouk D. Lindemans, Marieke J.H.A. Kruip, Patricia M.L.A. van den Bemt, Yvonne Vergouwe
Details
Formula
Study characteristics
Files & References
★★★★
Model author
Model ID
1221
Version
1.9
Revision date
2018-03-20
Specialty
MeSH terms
Model type
Logistic regression
(Calculation)
| Formula | No Formula defined yet |
|---|
| Condition | Formula |
|---|
Additional information
Adult patients admitted to a tertiary hospital and treated with VKAs between 2006 and 2010 were analysed. Bleeding risk was operationalised as an INR value ≥4.5. Multivariable logistic regression analysis was used to assess the association between potential predictors and an INR ≥4.5 and validated in an independent cohort of patients from the same hospital between 2011 and 2014.8996 admissions of patients treated with VKAs were identified, of which 1507 (17%) involved an INR ≥4.5. The final model included the following predictors: gender, age, concomitant medication and several biochemical parameters. Temporal validation showed a c-statistic of 0.71.
The development cohort constisted of 8996 patients, and the validation cohort consisted of 9018 patients.
Study Population
Total population size: 8996Males: {{ model.numberOfMales }}
Females: {{ model.numberOfFemales }}
Continuous characteristics
| Name | LL | Q1 | Median | Q3 | UL | Unit |
|---|---|---|---|---|---|---|
| Alanine amino transferase | 16 | 25 | 44 | u/l | ||
| Aspartate amino transferase | 22 | 30 | 44 | u/l | ||
| Gamma-glutamyl transferase | 33 | 61 | 131 | u/l | ||
| Lactate dehydrogenase | 357 | 442.5 | 589.8 | u/l | ||
| Albumin | 31 | 36 | 41 | g/l | ||
| estimated Glomerular filtration rate | 49 | 70 | 90 | ml/min/1.73m2 | ||
| Haemoglobin | 100 | 116 | 134 | g/l | ||
| Thyroid stimulation hormone | 0.7 | 1.4 | 2.8 | mu/l | ||
| Triiodothyronine | 1.0 | 1.4 | 1.8 | nmol/l | ||
| Thyroxin | 83.5 | 104.5 | 132.0 | nmol/l | ||
| C-reactive protein | 8 | 30 | 82 | mg/l | ||
| plateletcount | 175 | 229 | 300.8 | x10^9/l |
Categorical characteristics
| Name | Subset / Group | Nr. of patients |
|---|---|---|
| INR ≥4.5 during a previous admission | Yes | 868 |
| No | 8128 | |
| VKA type | acenocoumarol | 7978 |
| Other | 1018 | |
| Ward type | Medical ward | 5497 |
| Ither | 3499 | |
| Use of concomitant medication | Miconazole | 153 |
| Cotrimoxazole | 337 | |
| Fluconazole | 119 | |
| Voriconazole | 7 | |
| Amiodarone | 724 | |
| Rifampicin | 53 | |
| Carbamazepine | 73 | |
| Phenytoin | 89 | |
| Colestyramin | 17 | |
| Anti-thyroid drugs | 110 |
|
Predicting international normalised ratio ≥ 4.5 in hospitalised patients using vitamin K antagonists |
|
V-1.9-1221.18.03.20 |
 |
Refer to Intended Use for instructions before use |
 |
Evidencio B.V., Irenesingel 19, 7481 GJ, Haaksbergen, the Netherlands |
Related files
No related files available
Supporting Publications
| Title or description | Tags |
|---|---|
|
Development of a clinical prediction model for an international normalized ratio ≥4.5 in hospitalized patients using vitamin K antagonists
DOI:
10.1111/bjh.15161
|
External validation Internal validation Paper Peer review |
Predicted risk of INR ≥ 4.5 ...
{{ resultSubheader }}
{{ model.survival.PITTitle }}
{{ model.survival.YNETitle }}
Result
Download report
Generating report
Note
Notes are only visible in the result download and will not be saved by Evidencio
Predicted risk of INR ≥ 4.5
{{ resultSubheader }}
{{ chart.title }}
{{ chart.title }}
{{ chart.title }}
{{ table.title }}
| {{ row }} |
{{ chart.title }}
Outcome stratification
Result interval
{{ additionalResult.min }} to
{{ additionalResult.max }}
Conditional information
Result interpretation
The prediction model can help physicians to identify patients at the lower spectrum of thromboembolic risk and those for whom the risk of bleeding during vitamin K antagonist (VKA) therapy is high. Using the prediction model may also help
when counselling and informing patients about their potential risk for haemorrhage while on anticoagulants, and in identifying those patients who might benefit from more careful management of anticoagulation. Alternatively, these patients can also be switched to direct oral anticoagulants (DOACs), which cause less major bleeding, such as intracranial haemorrhages, compared to VKAs
Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.
Comments
Rating
Comment
Please enter a comment of rating
Comments are visible to anyone
Model feedback
No feedback yet 1 Comment {{ model.comments.length }} Comments
Recent Clinical pharmacology Models
Recent Internal medicine Models
Sign In
Sign Up
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.
Please sign in to enable Evidencio print features
In order to use the Evidencio print features, you need to be logged in.
If you don't have an Evidencio Community Account you can create your free personal account at:
https://www.evidencio.com/registration
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.
Disclaimer: Calculations alone should never dictate patient care, and are no substitute for professional judgement.
{{ variable.title }}