Post-Thrombotic Syndrome and Recurrent Thromboembolism in Patients with Upper Extremity Deep Vein Thrombosis: A Systematic Review and Meta-Analysis of Proportions
Notice bibliographique
Résumé
Abstract Background: Upper extremity deep vein thrombosis (UEDVT) is known to be less common than lower extremity DVT (LEDVT). In comparison to LEDVT, there is limited data on the occurrence of complications in patients with UEDVT. As a result, it remains uncertain as to how aggressively these patients should be treated. In this systematic review, we aimed to determine the rate of complications, including post-thrombotic syndrome and recurrent thromboembolism, in patients with UEDVT. Methods: We conducted a systematic literature search of MEDLINE and EMBASE databases for relevant studies from 1970 onwards. Our search included conference publications from major international meetings. Studies were eligible for inclusion if they were observational studies (case-control, cohort), randomized trials, or cases series including more than 20 patients. Studies must have included only patients age 18 or older and have objectively diagnosed UEDVT by means of venography or ultrasound. The primary outcome measures were the recurrence rate of UEDVT in patients with previously documented UEDVT and the rate of PTS. The secondary outcome measure was the rate of major bleeding, as defined by the International Society on Thrombosis and Haemostasis (ISTH), following the initiation of treatment for UEDVT. For each outcome, we calculated a pooled proportion using a fixed effects model and its 95% confidence interval (CI) using the Wilson score method. We also determined the mean proportions of outcomes and the interquartile ranges (IQR) of single proportions from individual studies. Results: A total of 61 studies were included in the meta-analysis (58 full text articles and 3 conference publications). The studies included a total of 5500 patients diagnosed with UEDVT. Of the 61 included studies, 39 reported data on recurrence, 21 on PTS, and 28 on major bleeding. PTS was defined either by the Villalta scale explicitly (a score of 5 or more) or by similar clinical descriptors posed by the investigators. The results are summarized in Table 1. When evaluating all studies, the pooled proportions of outcomes were: 2.4% (95% CI: 2.0-2.9%) for recurrence, 15.0% (95% CI: 12.8-17.5%) for PTS, and 2.9% (95% CI: 2.5-3.5%) for major bleeding. The mean proportions were: 8.4% (IQR: 0.0-10.1%), 18.4% (IQR: 0.0-26.7%), and 4.3% (IQR: 0.0-5.7%), respectively. When evaluating studies specific to primary UEDVT in the setting of venous thoracic outlet syndrome, the pooled proportions were: 5.5% (95% CI: 4.3-7.1%) for recurrence, 16.0% (95% CI: 13.4-19.0) for PTS, and 3.3% (95% CI: 2.0-5.3%) for major bleeding. The mean proportions were: 6.8% (IQR: 0.0-9.5%), 19.6% (IQR: 0.0-30.5%), and 3.2% (IQR: 0.0-4.8%), respectively. When evaluating studies specific to secondary UEDVT (catheter-associated and/or malignancy-associated), the pooled proportions were: 2.0% (95% CI: 1.5-2.6%) for recurrence, 8.0% (95% CI: 4.9-12.8) for PTS, and 3.9% (95% CI: 3.2-4.8%) for major bleeding. The mean proportions were: 18.1% (IQR: 1.5-18.4%), 17.1% (IQR: 9.8-26.5%), and 3.8% (IQR: 0.0-5.2%), respectively. Conclusions: This review included a large number of patients diagnosed with UEDVT and found that both primary and secondary UEDVT are associated with high rates of PTS. The pooled proportions of PTS and recurrent thromboembolism were higher in patients with primary UEDVT than in patients with secondary UEDVT. The rates of major bleeding were similar in both groups. Further analysis is needed on the association of these complications with different treatment modalities for UEDVT to better guide the management of patients. Our next step is to calculate pooled proportions using a random effects model and present this data at the 59th American Society of Hematology (ASH) Annual Meeting. Download : Download high-res image (98KB) Download : Download full-size image Disclosures Lazo-Langner: Pfizer: Honoraria; Bayer: Honoraria; Daiichi Sankyo: Research Funding; Alexion: Research Funding.
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 | 0,013 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,020 | 0,037 |
| Bibliométrie | 0,008 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».