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.
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
MeSH terms
  • International Normalized Ratio
  • Ratio, International Normalized
  • Bleeding
  • Model type
    Logistic regression (Calculation)
    Status
    public
    Rating
    Share
    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: 8996
    Males: {{ 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

    Related files

    No related files available

    Supporting Publications

    Predicted risk of INR ≥ 4.5
    ...

    {{ resultSubheader }}

    {{ model.survival.PITTitle }}

    {{ model.survival.YNETitle }}

    Result
    Note
    Notes are only visible in the result download and will not be saved by Evidencio

    Predicted risk of INR ≥ 4.5

    {{ resultSubheader }}

    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

    {{ file.classification }}

    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
    Not rated | On {{ comment.created_at }} {{ comment.user.username }} a no longer registered author wrote:
    logo

    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

    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.
    A personal Evidencio account is free, with no strings attached! Join us and help create clarity, transparency, and efficiency in the creation, validation, and use of medical prediction models.

    Disclaimer: Calculations alone should never dictate patient care, and are no substitute for professional judgement.