Comparison of the Villalta Post Thrombotic Syndrome (PTS) Score in the Ipsilateral Versus Contralateral Leg After a First Unprovoked Deep Vein Thrombosis (DVT): Results From the REVERSE Study
Notice bibliographique
Résumé
Abstract Abstract 1236 Background: Post thrombotic syndrome (PTS) occurs in up to 40% of persons after a proximal DVT despite optimal anticoagulant therapy. International guidelines issued by the ISTH recommend assessing PTS with the Villalta scale, a clinical measure that incorporates ratings of 5 patient-rated venous symptoms and 6 clinician-rated venous signs in the leg ipsilateral to DVT (range of scores: 0–33). However, these signs and symptoms are not specific for PTS and their presence and relevance in the contralateral leg have not been studied. The objectives of this analysis were to assess the Villalta score in the contralateral leg and its correlation with the Villalta score in the ipsilateral leg after a first DVT. Methods: Using data from the REVERSE prospective multicentre cohort study, we compared the Villalta score in the ipsilateral vs. contralateral leg in patients with a first, unprovoked, unilateral proximal DVT 5–7 months previously. As per ISTH standards, PTS was considered present if the Villalta score was >4 in the leg ipsilateral to DVT. We also compared the distribution of the Villalta scale's signs and symptoms components in both legs in patients with PTS in the ipsilateral leg and Villalta score >4 in the contralateral leg. Results: Between October 2001 and March 2006, 664 patients with a first unprovoked venous thromboembolism (VTE) were enrolled in the REVERSE study after having received a 5–7 month course of anticoagulant therapy. The population for the present analysis consisted of the 360 REVERSE patients who had unilateral DVT as the index event, no previous secondary DVT or pulmonary embolism, and non-missing Villalta score data. Overall, mean Villalta score was higher in the ipsilateral than in the contralateral legs: 3.7 (interquartile range [IQR] 1.0 – 5.0) and 1.9 (IQR 0.0 – 3.0), respectively (paired t-test: p <0.0001). PTS was present in 31.9% (n=115) of patients. With increasing severity of venous ectasia in the contralateral leg, prevalence and severity of PTS gradually and significantly increased (both p<0.0001). Prevalence of any PTS increased from 25% (59/238) in the absence of contralateral venous ectasia to 100% (4/4) in the presence of severe contralateral venous ectasia, whereas the prevalence of severe PTS (Villalta score >15) increased from 0.9% (2/238) in patients without contralateral venous ectasia to 25% (1/4) in patients with severe contralateral venous ectasia. Villalta scores were strongly correlated in the ipsilateral and contralateral legs (r=0.68; R-Square: 0.4686; paired t-test: p<0.0001). Among the 13.9% (n=50) of patients with a Villalta score >4 in the contralateral leg, 92% (n=46) had PTS in the ipsilateral leg. Similarly, 40% (n=46) of patients with ipsilateral PTS had a Villalta score>4 in the contralateral leg. Among these 46 patients, the distribution of Villalta signs and symptoms components in both legs were similar: symptoms accounted for an average of 61% of the Villalta score in the ispsilateral leg and for 63% in the contralateral leg (paired t-test: p=0.3; 80% power to detect a paired difference of 7% using our standard deviation of 0.165 with a bilateral test at 5% alpha). Conclusions: Villalta scores in the ipsilateral and contralateral legs are strongly correlated. Almost half of patients with ipsilateral PTS had a Villalta score>4 in the contralateral leg. Among patients with Villalta score > 4 in both legs, distribution of venous signs and symptoms were similar between the two legs. This suggests that up to half of incident PTS diagnosed with the Villalta scale might in fact reflect pre-existing symptomatic chronic venous disease. Performing bilateral Villalta assessments after DVT both clinically and in PTS-related research could be of interest to distinguish PTS from previous primary venous disorders. Disclosures: Kahn: sanofi aventis: Honoraria, Research Funding; Leo Pharma: Honoraria.
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 ».