Prevalence of Risk Factors for VTE In Hospitalized Medical and Surgical Patients. Data From the Comparison of Methods for Thromboembolic Risk Assessment with Clinical Perceptions and AwareneSS In Real Life Surgical and Medical Patients (COMPASS) Study
Notice bibliographique
Résumé
Abstract Abstract 3337 Introduction: Risk assessment models (RAM) are helpful tools for the screening VTE risk in hospitalized patients. Most of the available RAMs have been constructed on a disease-based or surgery-based approach and include some of the most relevant risk factors for VTE. There is limited information on the impact and importance of individual and comorbidity related risk factors for VTE present during hospitalization on the global VTE risk. Incorporation of the most frequent VTE risk and bleeding risk factors related to comorbidities might improve the ability of RAM to detect real-life patients at risk VTE and to evaluate drawbacks for the application of thromboprophylaxis. Aim of the study: The primary aim of the COMPASS programme was to evaluate the prevalence of the all known VTE and bleeding risk factors reported in the literature in real-life surgical and medical hospitalized patients. Methods: A prospective multicenter cross-sectional observational study was conducted in 6 hospitals in Greece and 1 in France. All inpatients aged >40 years hospitalised for medical diseases and inpatients aged >18 years admitted due to a surgical procedure and hospitalisation for a period exceeding three days were included. Patients and their treating physicians were interviewed with standardised questionnaire including all VTE and bleeding risk factors described in literature (130 items) on the third day of hospitalisation. Patients not giving informed consent, or receiving anticoagulant treatment for any reason or hospitalised in order to undergo diagnostic investigation without any further therapeutic intervention were excluded. Results: A total of 806 patients were enrolled in the study (414 medical and 392 surgical). Most frequent causes of hospitalisation in medical patients were infection (42%), ischemic stroke (14%), cancer (13%), gastrointestinal disease (9%), pulmonary disease (4%), renal disease (3%) and rheumatologic disease (1,4%). Surgical patients were hospitalised for vascular disease (22%) cancer (19,4%) gastrointestinal disease (12,5%), infection (8%), orthopaedic surgery and trauma (14%) or minor surgery (7%). Analysis of the frequency of risk factors for VTE showed that active cancer, recent hospitalisation, venous insufficiency and total bed rest without bathroom privileges were frequent in both groups. Medical patients had significantly more frequently than surgical patients several important predisposing risk factors for VTE. Moreover, medical patient had more frequently than surgical ones bleeding risk factors. The data for the most frequent risk factors are summarised in Table 1. Conclusion: COMPASS is the first registry that provides key data on the prevalence of all known VTE and bleeding risk factors in real life medical and surgical patients hospitalised in two countries of European Union. The analysis of the data shows that in addition to risk stemin from the disease or surgical act both medical and surgical patients share common VTE risk factors. The careful analysis of the most frequent and relevant VTE risk factors will allow the derivation of a practical VTE and bleeding risk assessment model taken into account these factors. Disclosures: Chrysanthidis: Sanofi-Aventis: Employment.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,004 |
| 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 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| 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 ».