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DOI: 10.1055/s-0044-1795069
An Algorithm for the Multimodality Treatment of Cerebral Arteriovenous Malformations
Um algoritmo para o tratamento multimodal de Malformações Arteriovenosas Cerebrais Disclosure of Funding None.
Abstract
Introduction: The algorithm of multimodal treatment (MMT) of arteriovenous malformations (AVMs) combined with intent-to-cure and presurgery embolization benefits was developed. The aim was to analyze the effectiveness of the MMT compared with unimodal intent-to-cure embolization in patient groups matched concerning clinical and angiographic characteristics.
Methods: A prospective cohort study of MMT effectiveness and safety was performed. To estimate differences in the total occlusion rate and complication rate of MMT compared to unimodal embolization, a 1:1 matched patient group was identified from the Russian Endovascular Neuro Society (RENS) AVM registry using the propensity score matching (EMB group). The treatment outcomes were assessed by the rate of achieving 100% AVM obliteration on follow-up angiography, morbidity, mortality, and the perioperative complication rate.
Results: Complete AVM occlusion was achieved in 93.7% of patients in the MMT group vs 76.2% of patients in the EMB group (p < 0.001). A favorable clinical outcome (mRS = 0–1 at the end of follow-up) was observed in significantly more patients in the MMT group compared with the EMB group (88.9% vs 71.4%, p = 0.024). In the MMT group, the rates of intraoperative and postoperative ischemic complications were comparable to those in the EMB group, and there was a significantly lower rate of postoperative hemorrhagic complications (6.3% vs 20.6%, p = 0.035).
Conclusions: Embolization as the main curative technique of the multimodal algorithm timely followed by microsurgery and radiosurgery ensures the highest clinical and angiographic efficacy of treatment and reduces the rate of disabling postoperative complications.
Resumo
Introdução: O algoritmo de tratamento multimodal (MMT) de malformações arteriovenosas (MAVs) combinado com os benefícios da embolização com intenção de cura e pré-cirurgia foi desenvolvido. O objetivo era analisar a eficácia da MMT em comparação com a embolização com intenção de cura unimodal em grupos de pacientes pareados em relação às características clínicas e angiográficas.
Métodos: Um estudo de coorte prospectivo da eficácia e segurança da MMT foi realizado. Para estimar as diferenças na taxa total de oclusão e na taxa de complicação da MMT em comparação com a embolização unimodal, um grupo de pacientes pareado 1:1 foi identificado no registro de MAV da Russian Endovascular Neuro Society (RENS) usando a correspondência de pontuação de propensão (grupo EMB). Os resultados do tratamento foram avaliados pela taxa de obtenção de 100% de obliteração da MAV na angiografia de acompanhamento, morbidade, mortalidade e taxa de complicação perioperatória.
Resultados: A oclusão completa da MAV foi alcançada em 93,7% dos pacientes no grupo MMT vs 76,2% dos pacientes no grupo EMB (p < 0,001). Um resultado clínico favorável (mRS = 0–1 no final do acompanhamento) foi observado em significativamente mais pacientes no grupo MMT em comparação com o grupo EMB (88,9% vs 71,4%, p = 0,024). No grupo MMT, as taxas de complicações isquêmicas intraoperatórias e pós-operatórias foram comparáveis às do grupo EMB, e houve uma taxa significativamente menor de complicações hemorrágicas pós-operatórias (6,3% vs 20,6%, p = 0,035).
Conclusões: A embolização como a principal técnica curativa do algoritmo multimodal seguida oportunamente por microcirurgia e radiocirurgia garante a mais alta eficácia clínica e angiográfica do tratamento e reduz a taxa de complicações pós-operatórias incapacitantes.
Authors' Contribution
All authors contributed equally to this work.
Publication History
Received: 07 November 2023
Accepted: 18 October 2024
Article published online:
11 December 2024
© 2024. Sociedade Brasileira de Neurocirurgia. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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