Prediction of Bleeding Risk in Patients on Extended Oral Anticoagulation for Venous Thromboembolism
Notice bibliographique
Résumé
Abstract Background: While tools exist to predict the risk of major bleeding in patients on oral anticoagulation therapy (OAT) for venous thromboembolism (VTE), these have focused on the higher risk period during the first 3 months of OAT therapy (risk of major bleeding 2.4% in first 3 months vs. 2% per year thereafter), and have not been developed or evaluated for bleeding risk after the first 3 months of therapy (extended OAT). It is widely considered that a bleeding risk tool that is able to identify patients with an annual rate of major bleeding over 3% is needed, as this is the cut-point at which the risk of continued anticoagulant therapy exceeds the benefit. Aims: We sought to evaluate if the rates of major bleeding were over 3% when four existing, previously published tools (RIETE,outpatient bleeding risk index (OBRI), ACCP and HAS-BLED) classified VTE patients during extended OAT as being high risk for major bleeding, and to assess if the differences in major bleeding risk between high risk and not high risk patients were statistically significantly different. Methods: The Bleeding Risk study was a multicentre, multinational prospective cohort study of patients on extended OAT for unprovoked VTE, or provoked VTE with prior VTE, designed to generate a new prediction tool for major bleeding. Patients were enrolled after at least 3 months of OAT. All major bleeding events during long term OAT were captured and adjudicated. We applied each of the 4 tools above to determine the risk for major bleeding according to patient scores. Not all variables in these tools were collected, including drug and alcohol history, and INR control. Results: 2514 patients enrolled at 12 sites have contributed over 7000 years of observation. The mean patient age was 60.2±14.7 years, 64% were male, 92% Caucasian, average BMI was 31.3, and 9% of patients were on antiplatelet agents. Patients were followed for a mean of 2.8 years (range, 0.1 to 6.8 years. 121 patients (4.8%) experienced at least one episode of major bleeding. The annual rate of bleeding was 1.7 per 100 patient years of observation. The proportion of patients classified as high risk of major bleeding by the RIETE (score of 3 or 4), OBRI (score of 3), ACCP (score ≥ 2) and HAS-BLED (score ≥ 3) scores were 12.3%, 13.3%, 28.0% and 23.1%, with major bleeding rates of 3.9%, 3.2%, 3.4% and 3.4% per year, respectively. The major bleeding rates of patients who were classified as not high risk of bleeding were 1.4%%, 1.5%, 1.1% and 1.2% per year, respectively. All differences were statistically significant with p values < 0.0001. Conclusion: Despite the potential to underestimate risk due to missing variables, all currently available prediction tools are able to identify patients with a 3% or higher risk of major bleeding per year. , The HAS-BLED and ACCP scores were able to identify the highest proportions of patients as high risk for major bleeding. Disclosures Wells: BMS/Pfizer: Research Funding; Bayer Healthcare: Other: Speaker Fees and Advisory Board; Janssen Pharmaceuticals: Consultancy; Itreas: Other: Served on a Writing Committee. Kovacs:Bayer: Honoraria, Research Funding; LEO Pharma: Honoraria; Daiichi Sankyo Pharma: Research Funding; Pfizer: Honoraria, Research Funding. Anderson:Bayer Healthcare: Research Funding. Rodger:Boehringer Ingelheim: Research Funding; Canadian Agency for Drugs and Technologies in Health: Consultancy.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».