June 25, 2026Open Access
A Novel Predictive Tool for Poor Anticoagulation Control in Patients on Vitamin K Antagonists in Spain: An Exploratory Study
Key Result
A novel predictive tool for poor anticoagulation control in patients on Vitamin K antagonists showed poor discriminative ability (ROC AUC 0.579), similar to the SAMe-TT2R2 score (ROC AUC 0.530).
Key Points
- The study aims to create and validate a novel predictive tool for poor anticoagulation control in patients using vitamin K antagonists.
- Randomly assigned patients into training (70%, n=1982) and validation (30%, n=909) groups for model development and validation.
- Developed a model using binary logistic regression with poor INR control as the dependent variable.
- Included independent variables such as renal insufficiency, chronic obstructive pulmonary disease, diabetes, and others.
- ROC AUC of the predictive tool was 0.579 (sensitivity: 47.5%; specificity: 65.0%).
- The SAMe-TT2R2 score had an ROC AUC of 0.530 with an overall quality score of 0.51.
- Neither model achieved an acceptable threshold for discriminative ability.
Study Design
Type
RCT (n=2,891)
Randomization
Randomly assigned into a training group (70%) and a validation group (30%)
Structured PICO
Does a novel predictive tool better predict poor anticoagulation control compared to the SAMe-TT2R2 score in patients on Vitamin K antagonists?
P
Population
2,891 patients on Vitamin K antagonists in Spain, randomly assigned to training and validation cohorts to develop a predictive tool for poor anticoagulation control.
I
Intervention
Novel predictive tool for poor anticoagulation control (incorporating renal insufficiency, COPD, diabetes, active smoker, alcohol abuse, previous ablation, hemoglobin level, HbA1c, functional dependence, and number of treatments).
C
Comparator
SAMe-TT2R2 score
O
Outcome
Poor international normalized ratio (INR) control (time in therapeutic range <65% using the Rosendaal method)surrogate
Current predictive models, including a novel tool and the SAMe-TT2R2 score, demonstrate poor discriminative ability for predicting suboptimal anticoagulation control in clinical practice.
Main Result
Effect estimate: ROC AUC 0.579
Abstract
Background: Given the limitations of available predictive tools for poor anticoagulation control, we aimed to build and validate a novel tool and compare its predictive ability with the SAMe-TT2R2 score, using the patients from the OBJETIVO 2024 study. Methods: The original sample was randomly assigned into a training group (70%, n = 1982) for model development and a validation group (30%, n = 909) for model validation. Stratification of patients was performed based on the presence of diabetes and functional dependence on daily living activities (N with available data = 2891). The model was developed through binary logistic regression, with poor international normalized ratio (INR) control (time in therapeutic range <65% using the Rosendaal method) as a dependent variable. Independent variables included renal insufficiency (glomerular filtration rate < 60 mL/min), chronic obstructive pulmonary disease, diabetes, active smoker, alcohol abuse, previous ablation, hemoglobin level, HbA1c, functional dependence in daily living activities, and number of treatments received in the last 6 months. Results: The receiver operating characteristic area under the curve (ROC AUC) was 0.579. The optimal cut-off point was 0.474 (sensitivity: 47.5%; specificity: 65.0%). Overall quality of the model for the training and validation groups was 0.49 and 0.55, respectively. The mean SAMe-TT2R2 in patients from the OBJETIVO 2024 study was 2.3. The ROC AUC for the SAMe-TT2R2 tool was 0.530. Overall quality of SAMe-TT2R2 for the present population was 0.51. Conclusions: None of the models presently tested reached the minimum threshold considered acceptable for discriminative ability. To date, utility of different models to predict poor anticoagulation control seems far from optimal in clinical practice.