Long-term outcomes following thrombolysis of arteriovenous grafts
Notice bibliographique
Résumé
BACKGROUND: Thrombolysis for arteriovenous grafts (AVG) yields high technical success rates, however, long-term outcomes are unclear. We conducted a multicenter retrospective cohort study to analyze 5-year patency rates following AVG thrombolysis. METHODS: All patients who underwent AVG thrombolysis between 2005 and 2015 at three academic hospitals were included. Prospectively maintained institutional nephrology and radiology databases were used to record demographic, clinical, and AVG characteristics. The primary outcome was primary patency, defined as AVG access survival without re-intervention including angioplasty ± stent with/without re-thrombolysis. Secondary outcomes were assisted primary patency and cumulative patency, defined as AVG access survival until re-thrombosis requiring re-thrombolysis or abandonment, respectively. Technical success was defined as restoration of flow with <30% residual stenosis. Patients were followed until 2017. Patency rates were assessed using Kaplan-Meier survival analysis and Cox proportional hazards were calculated to determine associations between covariates and patency loss. RESULTS: Seventy-four patients underwent AVG thrombolysis during the study period with a median follow-up period of 21.4 (IQR 8.3-42.8) months. The average age was 58.6 years with a high rate of comorbidities, including hypertension (82.4%) and diabetes (54.1%). Thrombolysis technical success was 96%. There were 147 re-interventions in 46 patients, of which 98 were re-thrombolysis (mean re-intervention rate of 1.27/patient/year). Primary patency at 1, 3, and 5 years were 43.2%, 20.2%, and 7.7%. Assisted primary patency at 1, 3, and 5 years were 47.5%, 20.2%, and 7.7%. Cumulative patency at 1, 3, and 5 years were 75.0%, 38.8%, and 22.6%. Cox proportional hazards analysis demonstrated no associations between demographic, clinical, and procedural characteristics and patency rates. CONCLUSIONS: Despite a high technical success rate, thrombolysis for AVG dysfunction is associated with poor long-term patency. Future studies are needed to determine risk factors for re-thrombosis to identify patients who will benefit from AVG thrombolysis in the long-term.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».